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Geothermal Energy: A Review

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A comprehensive review of integrated energy systems considering power-to-gas technology.

1. Introduction

2. planning and economic analysis, 2.1. planning models of ies with ptg technology, 2.2. economic analysis of ies with ptg technology, 3. system integration enhancement, 3.1. optimization of ies with ptg technology, 3.2. conversion technologies, 3.3. energy storage, 4. the role of ptg technology, 4.1. generation, 4.2. transmission, 4.3. distribution, 4.4. consumption, 5. conclusions and discussion, author contributions, data availability statement, acknowledgments, conflicts of interest.

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Click here to enlarge figure

Year	2016	2030	2050	Reference
Definite investment in €/kW	1500	900	500	[ , ]
Efficiency (LHV)	70%	75%	80%	[ , , , , ]
Electricity cost in €/MWh	0 or 70	0 or 70	0 or 70	[ , ]
Rate for natural gas in €/MWh	15	34	50	[ , ]
Rate for CO budgets in €/t	5	50	130	[ , ]

Technical Data	AEC		PEMEC		SOEC		Reference
	Mid-Term	Long-Term	Mid-Term	Long-Term	Mid-Term	Long-Term
Chemical reaction at anode	2OH → 0.5O + H O + 2e		H O → 2H +0.5O + 2e		O → 0.5O + 2e		[ , ]
Chemical reaction at cathode	2H O + 2e → H + 2OH		2H +2e → H		H O + 2e → H + O		[ , ]
Production rate (m h )	<760 <1000		<40 <500		<5 >5		[ ]
Min. part load (%)	30–40 10–20		0–10 0–5		N/a N/a		[ , , ]
Max. part overload (%)	<150 <150		<200 <200		N/a N/a		[ ]
Pressure (bar)	<30 <60		<200 <200		<25 <40		[ , , , , ]
Temperature (C)	60–80 60–90		60–80 60–100		700–1000 500–700		[ , , , , ]
Electricity demand (system) (kWh m )	>4.6 >4.4		>4.8 >4.4		>3.2 >3.2		[ ]
Current density (A cm )	<0.5 <0.8		<1.0 <2.0		<0.3 <1		[ ]
Cell voltage (V)	>1.9 >1.8		>1.8 >1.6		>1.0 >1.0		[ ]
Lifetime system (a)	20 30		6–15 30		N/a N/a		[ ]
Lifetime stack (h)	<100,000 <100,000		<50,000 <100,000		<5000 >5000		[ ]
Development status	Commercial		Commercial		Under development		[ ]

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Faisal, S.; Gao, C. A Comprehensive Review of Integrated Energy Systems Considering Power-to-Gas Technology. Energies 2024 , 17 , 4551. https://doi.org/10.3390/en17184551

Faisal S, Gao C. A Comprehensive Review of Integrated Energy Systems Considering Power-to-Gas Technology. Energies . 2024; 17(18):4551. https://doi.org/10.3390/en17184551

Faisal, Shah, and Ciwei Gao. 2024. "A Comprehensive Review of Integrated Energy Systems Considering Power-to-Gas Technology" Energies 17, no. 18: 4551. https://doi.org/10.3390/en17184551

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	Title	Type
1		journal	17.497 Q1	229	229	609	6629	16808	379	26.18	28.95	29.53
2		journal	12.288 Q1	44	60	79	4858	3323	78	42.06	80.97	33.06
3		journal	10.935 Q1	436	461	1124	37113	36095	1117	30.56	80.51	29.72
4		journal	8.748 Q1	319	979	2210	75985	59605	2195	25.38	77.61	30.16
5		journal	7.366 Q1	115	250	516	9291	10802	379	18.52	37.16	34.58
6		journal	7.202 Q1	195	606	1443	30127	28217	1382	18.42	49.71	30.17
7		journal	5.374 Q1	158	574	1635	45051	32636	1628	19.86	78.49	32.91
8		journal	5.202 Q1	53	149	157	11831	3484	157	21.29	79.40	31.79
9		journal	4.685 Q1	255	900	3328	57655	57410	3328	17.24	64.06	31.16
10		journal	4.364 Q1	164	242	683	7033	7650	682	10.49	29.06	24.75
11		journal	3.596 Q1	421	737	2896	85801	54654	2882	18.47	116.42	28.59
12		journal	3.563 Q1	20	42	61	3281	770	59	12.75	78.12	33.20
13		journal	3.493 Q1	51	328	244	22718	3418	234	12.60	69.26	31.99
14		journal	2.820 Q1	292	1569	4818	86374	58188	4806	11.46	55.05	25.80
15		journal	2.804 Q1	295	2221	6904	151025	77037	6898	10.43	68.00	32.90
16		journal	2.576 Q1	364	1451	5202	73603	60112	5174	11.01	50.73	38.84
17		journal	2.553 Q1	250	1127	3589	65814	39361	3574	10.51	58.40	24.29
18		journal	2.545 Q1	130	725	2349	52191	29362	2346	12.07	71.99	32.87
19		journal	2.529 Q1	110	31	66	6709	972	66	13.00	216.42	30.43
20		journal	2.508 Q1	45	16	46	1466	627	46	15.67	91.63	19.28
21		journal	2.359 Q1	76	333	882	25551	11594	875	13.26	76.73	39.27
22		journal	2.357 Q1	79	87	317	8039	2402	305	6.15	92.40	44.49
23		journal	2.320 Q1	113	368	1274	32128	10688	1274	7.69	87.30	46.73
24		journal	2.283 Q1	91	406	368	33184	5506	363	15.22	81.73	41.01
25		journal	2.278 Q1	65	177	441	6631	3449	439	7.02	37.46	30.12
26		journal	2.134 Q1	183	720	2738	42398	18470	2720	5.87	58.89	36.53
27		journal	2.116 Q1	67	245	776	16112	6986	775	9.10	65.76	30.80
28		journal	2.110 Q1	251	4008	8133	209116	82524	8115	10.29	52.17	28.46
29		journal	2.058 Q1	309	4422	15027	297573	180112	14979	11.08	67.29	35.17
30		journal	1.992 Q1	148	123	342	6682	2535	323	7.51	54.33	22.11
31		journal	1.982 Q1	57	188	294	15121	2952	268	10.05	80.43	35.61
32		journal	1.923 Q1	250	1541	6271	82131	60481	6242	9.90	53.30	27.95
33		journal	1.897 Q1	78	14	75	1663	597	72	7.55	118.79	30.00
34		journal	1.863 Q1	9	24	14	1575	154	12	11.00	65.63	29.47
35		journal	1.857 Q1	357	1218	3893	69166	34492	3885	8.41	56.79	30.31
36		journal	1.814 Q1	46	178	435	6691	2324	423	5.77	37.59	27.77
37		journal	1.747 Q1	72	32	102	1497	453	94	3.48	46.78	39.58
38		journal	1.681 Q1	60	250	383	18738	4017	382	9.61	74.95	34.11
39		journal	1.664 Q1	173	1687	5159	92426	41683	5096	7.17	54.79	35.02
40		journal	1.635 Q1	22	26	47	1235	430	42	4.30	47.50	42.57
41		journal	1.628 Q1	18	19	59	1216	839	59	13.93	64.00	27.27
42		journal	1.595 Q1	105	3161	4523	194045	45142	4518	9.64	61.39	28.12
43		journal	1.566 Q1	77	628	2073	36276	17149	2072	8.23	57.76	24.20
44		journal	1.555 Q1	16	32	74	1930	470	72	6.35	60.31	21.54
45		journal	1.513 Q1	263	2934	9374	182599	70908	9276	7.57	62.24	31.04
46		journal	1.474 Q1	31	166	297	9317	2305	296	7.63	56.13	18.17
47		journal	1.464 Q1	27	95	121	5956	1034	121	7.84	62.69	28.53
48		journal	1.449 Q1	50	195	419	13681	2858	414	6.28	70.16	33.77
49		journal	1.414 Q1	81	95	286	6355	1768	274	6.58	66.89	39.79
50		journal	1.390 Q1	17	30	84	1801	493	82	6.73	60.03	33.96

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A review of wave energy technology from a research and commercial perspective

Corresponding Author

Bingyong Guo

• [email protected]
• orcid.org/0000-0003-3134-0043

School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, Shaanxi, China

Centre for Ocean Energy Research, Maynooth University, Maynooth, Co. Kildare, Ireland

Correspondence

Bingyong Guo, School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China.

Email: [email protected]

John V. Ringwood

• orcid.org/0000-0003-0395-7943

Although wave energy prototypes have been proposed for more than 100 years, they have still not reached full commercialisation. The reasons for this are varied, but include the diversity of device operating principles, the variety of onshore/nearshore/offshore deployment possibilities, the diversity of the wave climate at various potential wave energy sites, and the consequent lack of convergence in technology and consensus. This distributed effort has, in turn, lead to a slow rate of progression up the learning curve, with a significant number of wave energy company liquidations and technical setbacks dampening investor confidence. Although a number of reviews on wave energy technology are already in the published literature, such a dynamic environment merits an up-to-date analysis and this review examines the wave energy landscape from a technological, research and commercial perspective.

1 INTRODUCTION

‘Carbon neutrality by 2050’ is the world's most urgent mission, and António Guterres, the United Nations Secretary General, stressed on 11 December 2020 [ 1 ] that “By next month, countries representing more than 65 per cent of harmful greenhouse gasses and more than 70 per cent of the world economy will have committed to achieve net zero emissions by the middle of the century.” To date, more than 110 countries have pledged to reach zero carbon emission by 2050. On the other hand, current energy demand mainly depends on fossil fuels, and is projected to rise by 1% per year until 2040 [ 2 ]. In the tension between global energy demand and carbon reduction promises, there exists a widening gap between rhetoric and action [ 3 ], and a significant transformation in the energy sector, with extra technical and non-technical efforts, is required to achieve carbon neutrality.

Among various renewable energy resources, wave energy shows great potential in bridging the gap between the rhetoric of carbon reduction and the increasing energy demand, being a relatively untapped resource, with the global wave resource in the range 1–10 TW. However, the exact global estimate of extractable wave power is debatable [ 4 ]. The theoretical estimate of global wave power is about 32,000 TWh/year (with a mean power of 3.65 TW) [ 4 ]. In terms of the usable wave power resource, excluding areas with wave power level < 5 kW/m, the global estimate is around 3 TW [ 5 ], while the mean wave power experienced by global oceanic coastlines is about 2.11 TW [ 6 ]. The assessment method and data in [ 5 ] are used by the Ocean Energy Systems (OES) and the International Renewable Energy Agency, with an estimate of 29,500 TWh/year [ 7 , 8 ], which exceeds global electricity consumption in 2018, around 22,315 TWh with two-thirds mix from fossil fuels [ 9 ]. Together with other renewable resources, wave energy can play an import role in satisfying both the requirements of carbon emission reduction, and energy supply increase. Thus, OES member countries plan to achieve over 300 GW of installed wave/tidal capacity, create 680,000 direct jobs and save 500 Mt of carbon emission by 2050 [ 7 ].

Compared with other renewable resources, especially solar and wind power, the advantages of wave power are multiple: (i) Wave power is characterised by a high-energy density, over 10 times that of wind and solar power [ 10 ]. (ii) Wave power has a high availability, up to 90%, while the availability of wind and solar is generally in the range 20–30% [ 11 ]. (iii) Wave energy technology has little impact on the environment [ 12 , 13 ]. (iv) Wave energy output can also be integrated with existing wind or solar power plants as a complementary resource for smoothing power output and reducing variability [ 14 - 19 ]. (v) Wave power is more predictable [ 20 , 21 ], giving more flexibility for regional or national power management, and planning.

Despite the enormous potential of wave power, currently active wave capacity is as small as 2.31 MW [ 8 , 22 ], and these operating wave energy projects are focused on research and demonstration. Currently, wave energy technology is at its ‘infant’ age, and there is no fully commercial scale wave energy converter (WEC) farm in operation, even though hundreds of WECs have been developed [ 23 ]. Crucially, there still exist several technical and non-technical challenges: (i) Technically, it is difficult to generate electricity from low-frequency (0.1 Hz, i.e., low velocity) oscillating motion and large force (1 MN). This requires extremely reliable structures and power take-off (PTO) systems and, consequently, high capital expenditure (CapEx). (ii) WECs operate in an offshore environment, with high installation, operation and maintenance costs. Thus, the operating expenditure (OpEx) is relatively large. (iii) The wave power resource varies on both a wave-by-wave, hour-by-hour, and site-by-site manner, in terms of wave frequency, height, direction, spectrum and power level, resulting in disparate WEC concepts without any convergence, diluting the efforts of research and development (R&D) and commercialisation. (iv) Extreme sea conditions occur from time to time, and the possibility of structural failure and device loss is relatively high. This adds extra risk for the finance sector to invest in WEC technology. (v) Currently, WEC technology is characterised by low maturity, high uncertainty and risk, and requires significant initial capital, which further discourages private investors. That is, diminishing private and public investments has been playing the most important recent role in advancing WEC technology by stimulating R&D activities.

In general, current WEC technologies or devices have not yet demonstrated their capability to harness enough wave energy at a low enough cost at commercial scale. Based on simplistic estimates of the levelised cost of energy (LCoE), some early stage WEC concepts, for example, the M4 device [ 24 , 25 ], have showed their possibility to achieve a low LCoE for some specific installation sites. Further, geometric optimisation can improve WEC's hydrodynamic performance, in terms of power capture in moderate waves and survivability in extreme waves. On the other hand, sophisticated control approaches can significantly improve power capture, while marginally increasing the CapEx and, hence, dramatically reduce the LCoE [ 26 ]. However, WEC hydrodynamics and control are inherently and non-linearly coupled [ 27 , 28 ], and a co-design approach is needed.

Current R&D activities mainly focus on wave resource assessment, WEC concept developing, hydrodynamic modelling, PTO innovation and control design. As shown in Figure  1(a) , the topics in the inner ring are well studied, and plenty of reviews have summarised the state-of-the-art of wave resource assessment [ 16 , 29 - 31 ], WEC technology [ 11 , 32 - 38 ], modelling [ 38 - 47 ], PTO [ 11 , 36 , 48 - 50 ], and control [ 51 - 55 ]. The R&D topics in the middle and outer rings in Figure  1(a) are not fully understood yet. There are a few surveys summarising WEC survivability [ 56 ], performance [ 57 ], economic characteristics [ 58 , 59 ], mooring [ 60 ] and shape optimisation [ 61 , 62 ]. However, only a few studies aim to investigate critical development factors, as shown in Figure  1(c) and (d), for successful commercialisation of WEC technology at each phase [ 63 - 66 ].

In contrast to the aforementioned reviews, this review aims to discuss potential pathway of WEC commercialisation, from lab to market, by (i) summarising R&D activities in wave resource assessment, PTO innovation, WEC modelling and control, (ii) reviewing ongoing pre-commercial WEC demonstration projects, (iii) identifying potential market opportunities, including the utility market for electricity and niche markets related to ocean applications, and (iv) discussing industry-academia-government (IAG) collaboration to improve some critical development factors for bridging the valley of death (VoD) between R&D and commercialisation activities. WEC technology commercialisation relies not only on technical readiness level (TRL), but also on technical performance level (TPL), which attempts to measure the potential economic performance of a wave energy device/project, and some external development factors, for example, investment environment, market data and national incentives. As the LCoE of WEC technology is still too high to compete with other renewable energy technologies, revenue and capital support from public sectors remains crucial [ 8 ]. Thus, public or government-related sectors play an important role in bringing together researchers and investors through support programs, market incentives, and regional policy and legislation, to form a solid IAG collaboration, as shown in Figure  1(b) .

The reminder of the paper is organised as follows: Section  2 summarises the basic foundations of ocean waves and wave resource assessment, while Section  3 investigates various WEC concepts, classification, and modelling methods. Section  4 summarises the development of PTO systems and control strategies, with Section  5 examining possible development trajectories of a WEC prototype or project. Section  6 discusses historical and commercial efforts devoted to wave energy technology. Section  7 summaries potential market opportunists for WEC technology, while Section  8 identifies some key factors and incentives for commercialising WEC technology. Finally, some concluding remarks and future perspectives are drawn in Section  9 .

2 QUANTIFYING THE WAVE ENERGY RESOURCE

In ocean observation, the wave height H and period T can be directly measured. A simple illustration of wave propagating from deep water to shallow water is given in Figure  2 . In Figure  2 , h and λ are the water depth and wavelength, respectively. The shallow and deep waters are defined by h ≤ λ 20 and h ≥ λ 2 , respectively. As water depth decreases, the shallow water effect reshapes the wave profile, which may result in non-linear waves, wave breaking and energy loss [ 10 , 67 ]. However, WECs normally operate in moderate sea states with H ≪ λ . Thus, linear wave theory is normally valid and is applied in this section, with an overview of regular and irregular waves introduced with specific foci on quantifying the wave resource, its variability, and predictability.

2.1 Regular waves and wave power

2.2 irregular waves and wave power, 2.3 the global wave power resource and spatial variability.

To estimate the wave resource over a large area, numerical wave models are generally used, of which the notable ones are the wave model, wavewatch 3, simulating waves nearshore, MIKE21-SW and TOMAWAC models, with their limitations and application scenarios discussed in [ 30 , 31 ]. To achieve accurate wave resource assessment, observed wave data, at a set of discrete spatial points, are used to calibrate the models. Although the exact global estimate of extractable wave power is debatable, depending on assessment method, wave model, and temporal and spatial resolution, a small set of studies conclude that the applicable wave power in the world is about 3 TW by excluding areas of J < 5 kW/m [ 5 , 7 , 8 ]. Considering the area with 30 nautical miles to the coastline, extractable wave power decreases to 2.11 TW [ 6 ], and decreases further to 1.85 TW (approximating 16,000 TWh/year), when wave direction and coastline alignment are considered [ 4 ].

The wave power resource is evenly distributed between the Southern and Northern Hemispheres, as shown in Figure  3(a) , but is concentrated within 30–60 degrees of latitude. Thus, latitude is one main factor affecting the spatial variability of the wave power resource. One typical example is the wave power resource along the Chilean coast, as shown in Figure  3(b) , with the wave power level increasing from 20 to 100 kW/m, as the latitude increases from 15 ∘ S to 55 ∘ S. It also shows that water depth has some influence on the wave power level. As waves propagate to the coastline, the shallow water effect causes energy loss. Consequently, spatial variability has a significant influence on WEC performance [ 72 , 74 , 75 ]. As shown in Figure  3(c) , the capacity factor of 3 WECs increases, as the latitude and wave power level increase. When wave power level is low, WECs should be scaled down accordingly to improve their performance [ 72 , 75 ].

2.4 Temporal variability and predictability of wave power

Wave power is characterised by significant temporal variability, ranging from seconds to decades. Such high temporal variability is one reason for the diversity of WEC concepts, and points to a required focus on WEC optimisation, PTO, control, survivability, power prediction, and management. Temporal variability can be classified into short-, medium- and long-term variations.

Short-term variation is characterised by irregularity in height, period and direction, varying from seconds to minutes. As the WEC control problem is typically non-causal, short-term prediction of wave elevation or excitation force is required, and prediction requirements for real-time control are investigated in [ 76 ]. Several prediction methods are discussed in [ 76 - 83 ], including the AR, ARMA, NARX and Bayesian learning methods. In addition, short-term variation results in a highly varying instantaneous wave power and a high peak-to-average power ratio, and extra design effort is required to smooth WEC harvested power, for example, PTO systems with accumulators/flywheels, to smooth high-frequency power variation.

Medium-term variation is represented by a change in wave spectrum or sea states, on an hourly or/and daily basis. Such variation may challenge the power management system of WEC farms, and accurate wave prediction over 1–72 h is required for power planning [ 16 ], and WEC installation and maintenance [ 83 ]. Compared with other renewable resources, wave power has an advantage in predictability [ 20 , 21 , 84 ], and the significant wave height can be accurately predicted in advance by a couple of days [ 16 , 83 , 85 ]. In addition, forming WEC arrays, or integrating WECs with wind turbines, can smooth power output to overcome medium-term variation [ 14 ].

Long-term variation concerns intra-annual and inter-annual variability of the wave power resource [ 4 , 86 - 88 ]. Intra-annual variability includes monthly and seasonal variability, while the inter-annual variability refers to wave power variation over decades. In general, wave power is high in winter and low in summer, as shown in Figure  3(d) and (e). Inter-annual variability has a significant influence on lifetime performance of WEC farms and, thus, should be considered when determining deployment sites and design capacity ratings [ 87 , 89 , 90 ]. For instance, the wave power on the west coast of Ireland has seen a significant increase in the 20 th century, which shows a power surplus of 15% within the lifespan of a point absorber (PA) or an oscillating wave surge converter (OWSC) [ 87 , 91 ]. However, extreme events also increase, requiring more focus on WEC survivability [ 87 ]. In addition, increase in off-limit events ( H s ≥ 5 m ) can significantly reduce the capture width ratio of an OWSC in the Irish sea, up to a level of 20% [ 92 ]. Thus, long-term trends of wave climate should be considered for commercial planning [ 29 , 73 ]. However, long-term variability can, in general, be only hindcasted rather than forecasted [ 83 ].

2.5 Influence of wave climate on commercialisation

For commercialising WEC technology, the first step is to select a deployment site, mainly according to annual mean wave power level and temporal variability. Sites with a high wave power level but low variability are preferred, and WECs should be selected accordingly. Variation in wave climate is a strong cost driver in both CapEx and OpEx [ 94 - 96 ]. Short- and medium-term variability can be handled by real-time control and power management, along with wave climate prediction. However, long-term variability is difficult to forecast but can significantly affect the lifetime performance of WEC farms.

In addition, extreme wave conditions, characterised by maximum wave height and storm occurrence, have significant influence on accessibility and availability of wave power, and survivability of WEC devices. More R&D activities are needed to improve WEC survivability in extreme waves. To ease the installation and operation of WEC farms, other key factors should be considered, including water depth, distance to the coast, wind and tidal climate, existing infrastructure, and environmental and spatial constraints [ 89 , 97 ].

3 WAVE ENERGY TECHNOLOGY PRINCIPLES

This section gives an overview of working principles for various WEC concepts and their classification, followed by an overview of hydrodynamic modelling of WECs, based on these principles.

3.1 Wave energy conversion concepts and classification

A WEC device converts the kinetic and/or potential energy contained in moving waves to useful energy (mainly electricity), comprising a set of floating or submerged bodies, a PTO unit, a control system, power electronics and other accessories. Since wave energy conversion concepts diverge, with over 1000 devices reported [ 10 ], there is no unique categorisation method to cover all possible WEC systems. In general, WECs can be classified according to their deployment locations, working principles, operation modes and device geometries [ 32 , 34 , 35 , 38 ]. In this study, the classification method detailed in [ 35 ] is adapted and shown in Figure  5 . In Figure  5 , WECs are classified into three types, including oscillating water columns (OWCs), wave activated bodies and overtopping devices. For each type, the exemplified prototypes are pre-commercial and have been tested in the open ocean.

An OWC utilises a hollow structure with an open inlet below the still water level to trap air in its chamber above the inner free-surface; wave action alternately compresses and decompresses the trapped air, which forces air to flow through a turbine coupled to a generator [ 36 ], principally using the kinetic wave energy. As listed in Figure  5 , OWCs can be further catalogued into two subclasses: (i) fixed OWCs, for example, the Pico and LIMPET devices, and (ii) floating OWCs, for example, Masuda's navigation buoy, and the Spar-buoy OWC. A comprehensive review, with a specific focus on OWCs and their PTO systems, is summarised in [ 36 ].

Overtopping devices are exemplified by fixed prototypes such as the TAPCHAN and OBREC devices, or the floating Wave Dragon (WD) device. Overtopping WECs mainly use potential wave energy, with electricity generated via somewhat conventional unidirectional (low head) hydro-turbines.

Most R&D activities focus on wave-activated WEC concepts, which can make use of the potential or/and kinetic wave energy to generate electricity [ 11 , 35 , 36 , 62 ]. Wave-activated WECs can be further classified as (i) floating or submerged subclasses, according to wave-WEC interaction, (ii) rotating or translating subtypes according to the essential degrees of freedom (DoFs) exploited, or (iii) PAs, attenuators and terminators according to WEC geometry, with respect to wavelength and propagating direction. PAs refer to WEC devices whose characteristic dimensions are much smaller than the incident wavelength. PAs may operate in heave, pitch or multiple DoFs, and can be situated nearshore or offshore. Attenuators are floating WEC devices, oriented parallel to the wave direction, usually composed of multiple floating bodies connected by hinged joints, with relative motion between two connected bodies used to generate electricity via PTO systems. Terminators are oriented perpendicular to the wave direction, typically including duck-like devices, or OWSCs. Some typical wave-activated type WECs, at pre-commercial scales, are listed in Figure  5 .

3.2 Hydrodynamic modelling

CFD methods have been widely applied to provide high-fidelity numerical solutions to WSI, which can be classified into Eulerian and Lagrangian methods [ 44 , 46 ]. Eulerian methods discretise the fluid into mesh elements, while Lagrangian approaches discretise the fluid as a set of particles. Eulerian-based CFD packages, for example, ANSYS Fluent, CFX, FLOW-3D, Star-CD/CCM+, and OpenFOAM, are generally used for modelling WEC dynamics, since they can handle all kinds of non-linear WEC hydrodynamics, for example, non-linear waves, turbulence, overtopping and slamming. On the other hand, Lagrangian-based smooth particle hydrodynamics (SPH) methods, for example, DualSPHysics [ 102 ], show advantages in automatic conservation of mass, and simplification of surface tracking, particularly suitable for extreme wave events, for example, wave breaking. However, SPH methods are not yet fully validated.

For linear incident waves, an analytical solution of ϕ i generally exists. However, analytical solutions for ϕ d and ϕ r only exist for some simple WEC structures, for example, spheres and cylinders [ 106 - 108 ]. For arbitrary WEC geometries, mesh-based boundary element methods (BEMs) are generally used to obtain numerical approximations of ϕ d and ϕ r . Common BEM solvers include WAMIT, NEMOH, AQWA, AQUA+ and WADAM in the frequency domain, and ACHIL3D in the time domain [ 47 ]. Substituting ϕ i , ϕ d and ϕ r in Equations ( 20 ) and ( 21 ) and omitting the quadratic term in Equation ( 20 ), the pressure p is obtained, to allow the hydrodynamic force in Equation ( 19 ) to be computed.

The parameters, K e ( ω ) , k e ( τ ) , M ∞ , M a ( ω ) , k r ( τ ) , B ( ω ) and K , can be obtained from the aforementioned BEM codes. The TD and FD models in Equations ( 22 ) and ( 23 ) can be connected according to Ogilvie's relations [ 110 ]. The TD and FD models can accurately depict WEC dynamics if the body motion is small. However, this is not always the case, especially when power maximisation control is applied to exaggerate WEC motion. In this case, some critical non-linear forces cannot be neglected any more, and hybrid modelling methods are generally applied to add some critical non-linear terms as treatments to f ext ( t ) in Equation ( 22 ).

By superimposing critical non-linear terms, a higher modelling fidelity can be achieved without a significant increase in computational cost. However, dominant non-linear factors depend significantly on specific WEC concepts, structure sizes, control strategies and application scenarios, and should be carefully considered on a case-by-case basis [ 42 , 45 , 111 ]. Depending on the additional non-linear term to f ext ( t ) , hybrid modelling methods are divided into four types [ 47 ], including the body-exact, weak-scatterer, viscosity and mixed treatments.

The body-exact treatment considers instantaneous body motion when computing the FK, diffraction, radiation and restoring forces, covering large WEC motion. A critical aspect is the non-linear FK force, which has a large influence on WEC hydrodynamics [ 112 - 114 ]. The weak-scatterer treatment considers the instantaneous free surface while the wetted surface boundary condition is linearised at its mean value, allowing high-order potential functions for computing the pressure and hydrostatic force in Equations ( 19 ) and ( 20 ).

The mixed treatment combines the viscosity representation with the body-exact or the weak-scatter treatments, leading to body-exact-viscosity or the weak-scatter-viscosity models. The former is more generally used for modelling WEC hydrodynamics, as controlled WECs are expected to oscillate with a large motion, even in moderate sea states [ 27 , 47 ]. So, the body-exact-viscosity treatment is useful for modelling WEC dynamics in normal operation mode, where the modelling fidelity of a heaving PA, considering non-linear FK and viscous forces, can approach CFD results, with significantly lower computational cost [ 103 ].

Although the TD model in Equation ( 22 ) shows high flexibility in handling non-linear treatments, the excitation and radiation force convolution terms are not efficient for WEC R&D activities. Thus, system identification techniques are generally used to approximate the radiation convolution terms by finite-order parametrised models, for example, state-space models [ 122 - 127 ]. As the excitation kernel function k e ( τ ) is generally non-causal, extra effort is required to represent the excitation force [ 79 , 81 , 128 - 132 ]. More generally, system identification methods are applied to derive compact linear and non-linear models directly from CFD, or experimental, data [ 133 - 137 ].

3.3 Influence of WEC technology on commercialisation

Dilution of R&D effort across many WEC concepts may be one main reason for currently low TRL and immaturity of WEC technology. To advance the convergence of WEC technology and concentration of R&D efforts, a common consensus on performance metrics for ocean energy technology is developing via international collaboration [ 66 ]. Hopefully, such a framework will accelerate the convergence of WEC technology, and consequently improve its TRL, maturity and commercialisation potential.

Hydrodynamic modelling of WECs has been well studied, but mainly for operational mode in moderate sea states. However, several WEC structural failures in storm conditions are reported in [ 138 ], even causing complete device loss. Recently, a few studies investigated WEC dynamics in extreme waves via tank testing [ 24 , 25 ] or CFD simulation [ 56 , 139 , 140 ]. However, more research focus and effort on WEC dynamics in extreme waves are required to evaluate WEC survivability, reducing the risk of WEC commercialisation. Although there are some studies on the optimisation of WEC farm layout [ 141 - 145 ], only linear hydrodynamic models of WEC farm are typically used. For full commercial-scale WEC farms with tens of WEC devices, the interaction between WECs is not yet fully understood.

4 POWER TAKE-OFF SYSTEM AND CONTROL

The PTO system is one key component of a WEC device, which transforms the mechanical power from the WEC motion to electricity. The PTO system has its own dynamics which, allied to those of the floater hydrodynamics, determines the overall frequency response characteristics of the system, which needs to be tuned to the relevant sea state via the control system. Consequently, a reliable and robust PTO, together with an appropriate control strategy, will improve commercialisation potential.

4.1 Power take-off system

Various PTO systems are illustrated in Figure  7 , including: (i) hydraulic PTOs, applicable to many kinds of WEC concept [ 49 , 50 , 146 - 152 ], (ii) air turbines for OWCs [ 36 , 153 ], (iii) hydro turbines for overtopping devices [ 154 ], (iv) mechanical rectifiers [ 155 , 156 ], and (v) direct-drive generators [ 157 - 159 ]. In general, these PTO systems are well modelled and market available, often derived from other application areas.

In general, PTOs cannot directly integrated to WEC devices, due to the following technical challenges: (i) PTOs are generally optimised to operate efficiently at a high unidirectional speed. However, WEC motion is typical slow, with mechanical rectifiers and mechanical or hydraulic gearing used to produce higher speed unidirectional motion. (ii) As discussed in Section  2 , waves can have high temporal variability, making it difficult to determine the rated specifications for PTO components. In addition, short-term variability induces a high peak-to-average power ratio, which may lead to occasional overrated conditions. Thus, PTOs which can operate efficiently over a wide range of sea states are required. (iii) Extreme sea state occurs occasionally, exceeding the PTO physical constraints, for example, maximum stroke, velocity, force and power, so PTO decoupling mechanisms are required.

Current WEC modelling tends to assume that PTOs in Figure  7 are ideal, simplified the model to a mass-spring-damper system, or neglecting some important non-linear factors, for example, hysteresis effect [ 148 ], dead-zone, saturation, friction [ 151 ], and load effects [ 161 ]. This may lead to incorrect design decisions regarding the PTO and control systems. For high-fidelity PTO modelling, both their dynamics and efficiency variations with load should be considered. In general, the average efficiency of a non-ideal PTO deceases as the reactance/resistance ratio increases [ 161 ], and the average PTO efficiency decreases dramatically when the load diverges from its rated value. A large amount of energy will also be dissipated by non-ideal PTOs, in terms of hydraulic leakage [ 152 ], mechanical loss [ 151 , 152 , 155 ], or copper loss [ 162 - 164 ]. Thus, non-linear and non-ideal PTO factors should be modelled and then considered at the control design stage.

Since PTOs are naturally non-ideal and non-linear, it is more realistic to integrate a non-linear PTO model with a non-linear WSI model to form a high-fidelity wave-to-wire (W2W) model for control, optimisation and performance evaluation [ 43 , 45 , 54 , 165 ]. However, such a model is complex and expensive in computation. Thus, systematic complexity reduction approaches are required to achieve an acceptable balance between fast computing and model fidelity, discussed in [ 164 , 166 , 167 ].

4.2 Control

Since ocean waves are irregular in amplitude and frequency, and sea states change all the time, control approaches are required by WECs for power maximisation in mild/moderate waves and survivability enhancement in extreme waves. A wide range of WEC control strategies are available, for example, reactive control [ 168 ], phase control [ 68 ], optimisation-based control [ 53 ], adaptive control [ 169 , 170 ]. This section only discusses some basic concepts of WEC control, major milestones in the literature, and their influence on WEC commercialisation. Detailed control reviews are given in [ 51 , 53 - 55 ].

4.2.1 Classical control strategy

In addition to strong assumptions of monochromatic wave, linear hydrodynamics and ideal PTO, physical constraints, for example, WEC stroke, cannot be easily handled by classical control strategies and, thus, their value is limited. However, Equations ( 26 )–( 28 ) have established the theoretical foundation for WEC control.

4.2.2 Modern control strategies

Ocean waves are generally panchromatic, and the optimal control laws in Equations ( 26 )–( 28 ) can be extended to the approximate complex-conjugate (ACC) and approximate velocity tracking (AVT) structures [ 55 ], respectively, as shown in Figure  8 . The ACC structure, which is optimal only for the predominant wave frequency, does not require any estimation or forecasting of wave excitation force, but cannot handle constraints directly, and hard to improve its robustness to modelling errors [ 172 ]. Meanwhile, the AVT framework is more flexible than the ACC one, permitting the incorporation of physical constraints, and is generally used for the majority of optimal control strategies, but requires knowledge of the excitation force, and is significantly more computationally complex.

Numerical optimisation in Equation ( 29 ) is time consuming, which may cause some difficulty in real-time implementation. In addition, the WSI is so complex that the convexity of Equation ( 29 ) is not generally guaranteed. Further considering physical constraints in WEC stroke and PTO force, the existence of optimal control solutions is also not guaranteed. Once wave excitation exceeds a certain level, there may be no control solution that simultaneously satisfies PTO constraints in force and displacement [ 55 , 178 ]. Thus, the existence of an optimal solution depends on wave conditions, WSI and the PTO specification. On the other hand, a well-controlled WEC tends to oscillate significantly, resulting in large body motion which, in turn, exaggerates non-linear hydrodynamics [ 27 , 28 ]. Thus, control should be considered in WSI and PTO modelling, requiring a relatively high-fidelity W2W model, inherently considering non-linear WSI and non-ideal PTO. However, such a model is typically complex, and systematic complexity reduction is inevitable [ 164 , 166 , 167 ].

4.2.3 New trends in control

To advance WEC control implementation, recent R&D efforts have been devoted to robust control, model-free control (MFC), and system complexity reduction.

Since WSI is somewhat of a ‘blackbox’, modelling error and uncertainty are inevitable. Thus, control strategies have been designed to address the robustness to: modelling error [ 80 , 179 - 183 ], external disturbance [ 184 - 186 ], and estimation/prediction error of excitation force [ 187 ]. Model sensitivity issue for different WEC control system architectures is analytically and numerically studied in [ 172 ], revealing that the AVT architecture is relatively insensitive to inertial and stiffness terms, and generally has superior robustness compared with the ACC framework [ 172 ].

MFC is an alternative to robust control, aiming to make WEC performance more immune to model uncertainty, external perturbation and unmodelled dynamics. Among various MFC control methods, some notable examples are extremum-seeking algorithms [ 188 ], artificial neural networks [ 189 ], deep reinforced learning control [ 190 ], machine learning [ 191 ], least-squares policy iteration [ 192 ] and adaptive control [ 170 ]. Some of these MFC methods inherently contain a large number of optimisation iterations, resulting in real-time implementation challenges.

A high-fidelity W2W model, with non-linear WSI and non-ideal PTO, is naturally complex, with complexity reduction required for control and optimisation [ 43 , 45 , 54 , 165 ]. For real-time implementation, an interesting aspect is to avoid some of the particular problems, including (i) excitation force estimation, (ii) excitation force forecasting, and (iii) numerical optimisation [ 55 ].

4.3 Influence of PTO and control on commercialisation

For advancing PTO designs and implementation, R&D activities should use a realistic and non-ideal model for numerical investigations in modelling, performance evaluation, control development and design optimisation. For practical testing, attention should be paid to PTO optimisation with respect to wave climate, to improve its reliability for long-term operation. In addition, a durable PTO should have a decoupling design to survive in extreme waves.

Control plays the most important role in advancing WEC economic performance by reducing LCoE. Although WEC controllers increase CapEx by a small margin, annual energy production can be improved significantly. A typical example is the SEAREV G21 device [ 26 ], where a properly designed control system increases the annual energy production from 730 MWh to 1300 MWh, with the CapEX only increasing from 5 M€ to 5.3 M€. In general, hydrodynamics, PTO dynamics and control are inherently coupled in a non-linear manner, and such a coupling is not yet fully understood [ 27 , 28 ]. Thus, it is imperative to establish a co-design framework to accurately address such a coupling in its true form.

Supervisory control, to switch WEC system between operation and survival modes according to sea sates, and fault tolerant control, to improve system reliability, are seldom tackled. Several studies have addressed the array control problem [ 193 - 197 ], but are limited to small device numbers and simple array layouts.

5 WEC DEVELOPMENT TRAJECTORIES

To take a high-tech product from lab to market, it is critical to evaluate the maturity of the technology, mainly represented by TRL. However, a successful commercial strategy also requires that the technology is marketable and investable, represented by TPL. Based on a TRL-TPL-matrix, it is possible to find an ideal development trajectory for WEC projects, even at very early development stages. To date, some WEC projects have achieved either high TRL or TPL. However, high TRL and TPL do not naturally indicate successful commercialisation, and many new ventures have failed to bridge the VoD. In this section, the development trajectory of R&D WEC projects will be discussed in the TRL–TPL space, together with potential measures to bridge the VoD for new ventures.

5.1 Technology readiness level

TRL was initially developed by NASA to estimate the maturity of a technology of high risk, novelty and complexity, for example, for space programmes. For WEC technology, the technology readiness assessment is tailored by Fitzgerald and Bolund [ 198 ], in the categories of functional readiness and lifecycle readiness, as shown in Table  1 . Functional readiness is more commonly used than lifecycle readiness, as there only exists very limited experience of long-term WEC operation. Hereafter, TRL refers to the functional readiness only. Table  1 clearly shows that TRL significantly relates to the scale ratio, mainly according to the Froude number, with successful demonstration of a small WEC array at TRL 9 indicating that the technology is mature, or ready for commercialisation.

TRL	Function readiness	Lifecycle readiness
1	Basic principles observed and reported.	Potential uses of technology identified.
2	Technology concept formulated.	Market and purpose of technology identified.
3	Analytical/experimental key function and/or characteristic proof-of-concept ( ).	Initial capital cost and power production estimates or targets established.
4	Technology component and/or basic technology subsystem validation in a laboratory environment ( ).	Preliminary lifecycle design: targets for manufacturable, deployable, operable, and maintainable technology.
5	Technology component and/or basic technology subsystem validation in a relevant environment ( ).	Supply-chain mobilisation: procurement of subsystem design, installation feasibility studies, cost estimations, etc.
6	Technology system prototype demonstration in a relevant environment ( ).	Customer interaction: consider customer requirements to inform type design. Inform customer of likely project site constraints.
7	Technology system prototype demonstration in an operational environment ( ).	Ocean operational readiness: management of ocean scale risks, marine operations, etc.
8	Actual product completed and qualified through test and demonstration ( ).	Actual marine operations completed and qualified through test and demonstration.
9	Operational performance and reliability demonstrated for an array of type machines ( ).	Fully de-risked business plan for utility-scale deployment of arrays.

A WEC development roadmap, from design to commercialisation, is also discussed in [ 199 ], with specific foci on TRL-related development activities and assessment criteria for single WECs and WEC farms. By summarising existing R&D WEC projects, the WEC development plan (in 2010) is divided into five stages [ 200 ] and six stages in 2021 [ 66 ], aiming to comprise the best practices and recommended procedures for wave energy technology. One big lesson learnt for the TRL-roadmap in [ 200 ] is that the development cost and time are high, as shown in Figure  9(a) .

5.2 Technology performance level

Since TRL was initially used for the NASA space program, development cost and time are not as important as technology maturity. However, this is not the case for WEC technology. Marketability and affordability are as important as technology maturity, which are not considered in the TRL assessment of Table  1 . Thus, the concept of TPL was proposed by Weber [ 201 , 202 ], to address the importance of technology performance, power capability, system availability, CapEx and OpEx. The characteristics and categories of each TPL level are detailed in Table  2 . To assess TPL at early stages of WEC projects, guidance is given in [ 203 ].

TPL	Characteristics	Category
1	Majority of key performance characteristics and cost drivers do not satisfy and present a barrier to potential economic viability.	: Technology is economically viable and competitive as a renewable energy form.
2	Some of key performance characteristics and cost drivers do not satisfy potential economic viability.
3	Minority of key performance characteristics and cost drivers do not satisfy potential economic viability.
4	In order to achieve economical viability under distinctive and favourable market and operational conditions, some key technology implementation and fundamental conceptual improvements are required.	: Technology features some characteristics for potential economic viability under distinctive market and operational conditions. Technological or conceptual improvements may be required.
5	In order to achieve economic viability under distinctive and favourable market and operational conditions, some key technology implementation improvements are required.
6	Majority of key performance characteristics and cost drivers satisfy potential economic viability under distinctive and favourable market and operational conditions.
7	Competitive with other renewable energy sources given favourable support mechanism.	: Technology is not economically viable.
8	Competitive with other energy sources given sustainable support mechanism.
9	Competitive with other energy sources without special support mechanism.

With more emphasis on LCoE drivers, an updated TPL assessment method was used for the Wave Energy Prize [ 204 ]. For successful WEC commercialisation, stakeholder requirements on WEC technology are identified in [ 64 ], as: (i) having market-competitive cost of energy, (ii) providing a secure investment opportunity, (iii) being reliable for grid operations, (iv) benefiting society, (v) being acceptable to permitting and certification; (vi) being safe, and (vii) being deployable globally. A third TPL assessment update is provided in [ 205 ], where TPLs can be applied to all TRLs and development stages of WEC devices and farms. At low TRL, TPL assessment is very effective, as it considers a wide range of techno-economic performance criteria. At high TRLs, TPL assessment is more strict. More detailed methods to score TPL is given in [ 206 ].

5.3 Development trajectory

Based on TRL and TPL assessment, a TRL-TPL matrix is established in [ 65 , 202 ], to discuss possible development trajectories for a WEC project. Intuitively, there are two simple development trajectories: the TRL-first trajectory (blue curve) and the TPL-first trajectory (green curve), as shown in Figure  9(b) . The TRL-first trajectory is conventionally used in WEC development, where WEC development concentrates on improving the TRL first, and then attempts to improve TPL at a high TRL. In contrast, the TPL-first trajectory prioritises TPL over TRL by evaluating WEC techno-economic performance at low TRLs, with significantly lower design cost implications. In addition, another combined trajectory is discussed also in [ 65 ], and demonstrated well by the SEAREV case study [ 26 ], shown as the black curve in Figure  9(b) . The first generation device, SEAREV G1 , reaches (TRL,TPL)=(4,2), but its LCoE is high. Shape optimisation and control are implemented for the second-generation device, SEAREV G21 , at (TRL,TPL)=(3,3). Further design optimisation for the third-generation device, SEAREV G3 , prioritises TPL over TRL, arriving at (TRL,TPL)=(2,6).

The traditional TRL-first trajectory requires several technology steps at full technology readiness. Development costs, and time required for each TRL, are estimated in [ 200 ], and the mean values are illustrated in Figure  9(a) . As the TRL increases from 3 to 8 (see the points P1 and P2), the accumulated budget and time increase from 0.1 M€ to 25.8 M€  and from 0.5 years to 6.4 years, respectively. By projecting P1 and P2 to the TPL-first and TRL-first trajectories in Figure  9(b) , respectively, it is clear that the TPL-first trajectory is significantly cheaper in achieving a high TPL [ 202 ]. A comparison study of the TRL-first, TPL-first, and combined trajectories in [ 65 ] strongly recommends the TPL-first trajectory, as the other requires double the development time and cost, and is more prone to project failure. Based on the TRL concept, a framework for evaluating WEC performance, and a development process, is identified in [ 66 ]. Within this framework, the WEC development process is divided into six TRL-related stages, given in Table  3 . For each stage, technology performance is evaluated in terms of power capture, power conversion, controllability, reliability, survivability, maintainability, installation ability, manufacturability, and affordability, to define evaluation criteria, methods, thresholds, activities and stage entry requirements. R&D projects should proceed further to the next TRL-based stage only when all stage gate metrics are met.

Stage	TRL	Verification
0: Concept creation	1	Analytical and numerical models
1: Concept development	2–3
2: Design optimisation	4	Experimental tests in controlled environment
3: Scaled demonstration	5–6
4: Commercial-scale single device demonstration	7–8	Experimental tests in representative environment
5: Commercial-scale array demonstration	9

In the TRL-TPL matrix, market entry conditions are defined as (TRL,TPL)=(9,>7) [ 202 ]. However, this does not indicate successful commercialisation. According to the commercial readiness index (CMI) defined in [ 207 , 208 ], market-entry WEC technology only arrives at the commercial trial stage, far away from successful commercialisation.

5.4 Valley of death

In Figure  10 , basic success is represented by the black curve, where a positive CF is achieved at the end of commercialisation. For some specific WEC projects or devices, failure may occur before operation or generation of any revenue (see the red curve), mainly due to a low TPL, for example, low in manufacturability, installability or grid accessibility. After successful commissioning, a WEC project starts to operate, generating electricity, and bringing revenue. Still, commercialisation may fail if the deployed WEC technology is of low survivability, shown by the brownish red curve. WEC devices may also be destroyed by extreme waves conditions, for example, storms. Another big concern is marketability. Even though a positive CF is achieved, project failure may still occur (see the grey curve) if the LCoE from wave is not as competitive as other renewable energy technologies. Such a failure may be caused by frequently required maintenance, disappearance of feed-in tariff (FIT), or a dramatic reduction in the LCoE of other renewable energy technologies.

To successfully commercialise wave energy technology, the IAG collaboration plays the major role to bridge the VoD. Technically, novel WEC concepts, PTO innovation and robust control show great potential in LCoE reduction. In addition, the TPL-first development trajectory can further reduce the LCoE by saving development cost and time, and mitigating development risk. With these technical measures, the black CF curve can be raised to the green curve, thus relieving the dearth of investment during the VoD. On the other hand, market incentives from government-related sectors, like FIT, can benefit revenue and CF directly, lifting the black curve to the blue one. FITs have a significant influence on overall WEC economic performance [ 63 ], and an appropriate FIT rate can even drive a defective commercial project into a profitable one. Beyond market incentives, government-related sectors can further develop regional and national strategies to reinforce IAG collaboration, to achieve a significant commercial success, shown as the dark green curve in Figure  10 .

To bridge the VoD via IAG collaboration, public or government-related sectors play a most important role to bring together researchers and investors, by developing national strategies and using market incentives, even at the early stage of a specific WEC technology for better two-way knowledge transfer and communication [ 8 , 209 ]. Current national strategies and market incentives are discussed in Section  8 . Given that the development and commercialisation of WEC technology require significant time and funding levels, and are of high risk, public investment should be prolonged even after market entry, to mitigate investment risk by building sea test sites, establishing logistics chains, providing grid connection and easing legal permitting. With better understanding of WEC technology and its mitigated risk, private investment, that is, seed investment, venture capital and stock investment, may get involved at early stages of WEC technology development. Thus, such a reinforced IAG collaboration shows a possibility to bridge the VoD for commercial success.

6 HISTORICAL AND COMMERCIAL EFFORT

This section summarises the historical and ongoing development of wave energy technology, to address both academic and commercial milestones.

6.1 Historical development of wave energy technology

The idea to transfer wave energy into a useful form is not new, dating back to 1799. Since then, the historical development of wave energy technology is divided into six eras, shown in Figure  11 . A notable overview of WEC history development is summarised in [ 138 ]. The years of 1973, 1985, 1998, 2012 and 2016 are treated as turning points for WEC development, also used in Figure  11 . R&D and commercial activities in each era are detailed in the following subsections.

6.1.1 ‘Pre-history’ Era, before 1973

In this era, R&D and commercial activities are not well documented, and most research work is typified by trial-and-error methods. However, there are still some significant fundamental achievements, including: (i) the first patent published in France in 1799 [ 210 ]; (ii) the first practical wave motor device in the United States, operating from 1898 to 1910 [ 138 ]; (iii) the OWC-based navigation buoy developed in Japan from 1945 to 1965, is successfully commercialised with more than 1000 devices deployed worldwide [ 138 ]. To the best of the authors' knowledge, the OWC-based navigation buoy is the only successfully commercial WEC device to date.

6.1.2 Modern Era, 1973–1985

With oil price rising sharply from 21 to 57 $ per barrel in 1973, several countries invest heavily in renewable energy technologies. The landmark of WEC technology entering into the modern era, is the proposal of Salter's Duck , whose hydrodynamic efficiency is tested up to 80% [ 211 ]. In this era, theoretic fundamentals are established, including: (i) the concepts of ‘resonance’, ‘absorption length’ and ‘power optimisation’ are defined for the first time in [ 168 ]; (ii) the theoretical maxima of absorption are derived in [ 212 ]; (iii) latching control [ 213 ] and reactive control [ 214 ] are tested; (iv) the constructive park effect of WEC arrays is studied for the first time in [ 215 ]; and (v) the first economic study of WEC technology is given in [ 216 ]. In addition, there is also some practical progress, represented by the sea trial of the KAIMEI OWC and Cockerell Raft  concepts.

6.1.3 Trough Era, 1985–1998

In 1985, the oil price drops from 71 to 25 $ per barrel, and activity in renewable energy decreases dramatically. However, there are still some noteworthy milestones. In theory, feed-forward control is studied to overcome the non-causality [ 217 ], while several devices are tested in the open ocean, including the TAPCHAN [ 218 ], Kvaerner column [ 219 ], OSPREY OWC [ 32 ], Islay OWC [ 32 ] and Pico OWC [ 220 ].

6.1.4 Explosion Era, 1998–2012

The Kyoto Protocol is signed in 1998 and carbon emission reduction becomes an international imperative, marking 1998 as the start of the explosion era of WEC technology. A significant milestone is the development of the Pelamis device, considered as the most promising WEC technology for commercial application. In this era, pre-commercial activities are stimulated by regional and national support programmes and market incentives, characterised by: (i) WEC companies developing some well-known pre-commercial devices, further detailed in Table  4 ; (ii) WEC technology evolves from onshore to offshore, from small to large capacity; (iii) a number of open sea testing sites are commissioned with grid connections, with EMEC opening in 2004 as probably the first and most developed test site; and (iv) a number of structure failures are reported, with high financial loss, or total device loss, leading to adverse publicity.

WEC	Country	Developer	Type	Stage	Capacity (kW)	Year	Status	Reference
	Japan	JAMSTEC	OWC	4	40	1978-1979	decommissioned	[ , ]
	Norway	Norway A.S.	TWEC	4	385	1985-1989	destroyed by storm	[ , ]
	Norway	Kvaerner Brug A/S.	OWC	4	500	1985-1989	destroyed by storm	[ , ]
	UK	QUB, Wavegen	OWC	4	75	1991-2000	replaced by	[ ]
	UK	Wavegen	OWC	4	2000	1995	lost during installation	[ , ]
	Japan	JAMSTEC	OWC	5	110	1998-2000	decommissioned	[ , ]
	Portugal	IST, WavEC	OWC	4	400	1999; 20016-2018	turbine fault;
damaged by storm	[ ]
	UK	QUB, Wavegen	OWC	4	500	2000-2012	stopped	[ , ]
	Netherlands	Teamwork Technology	PA	4	1000	2001;2002; 2004	lost due to
pump failure	[ , ]
	UK	Pelamis Wave Power	AWEC	5	750	2004-2007; 2010-2011	structure failure 2011	[ ]
	Sweden	Seabased Industry	PA	5	10 10	2005-2007	decommissioned	[ , ]
	Finland	AW-Energy OY	OWSC	4	350	2007-2008	decommissioned	[ ]
	United States	OPT	PA	4	40	2009-2010	decommissioned	[ ]
	Spain	EVE	OWC	5	296	2009-	active	[ , ]
	UK	Aquamarine Power	OWSC	4	800	2012-2015	stopped	[ ]
	Norway	Fred. Olsen	PA	4	30	2015-2016	stopped	[ , ]
	Australia	Oceanlinx	OWC	4	1000	2014	damaged during transportation	[ ]
	China	GIEC	AWEC	4	100	2015-2016	stopped	[ ]

6.1.5 Distrust Era, 2012–2016

In 2012, Statkraft terminated its ocean energy programme, tipping the first domino and opening the distrust era. Several companies, even some highly rated ones, fail to pass through the VoD and go into bankrupcy or liquidation, for example, Wavebob Ltd ., AWS , Wavegen , Pelamis Wave Power , Oceanlinx , and Aquamarine Power . This bad news reduces public and private investor trust, given that the total investment in these companies was significant, for example, about 64 M€ for Oceanlinx , and about 81 M€ for Pelamis . One positive trend in this era is that some companies turned to niche markets, for example, the OPT PB3 and PH4S buoys for powering ocean observation devices.

6.1.6 Reboot Era, 2016–present

In 2016, the Paris Agreement entered into force, producing consequent activity increases in regional and national support schemes. Meanwhile, WaveStar becomes inactive after 13 years of operation, following 40 M€ of investment. Then, Carnegie Clean Energy receives more than 39 M€ total investment, and wave energy was successfully applied to an aquaculture platform. However, Seabased closes its production facility in Sweden and the Pico plant suffers from structural damage in a storm. Following the Paris Agreement, more than 110 countries pledge to reach zero carbon emission by 2050, suggesting a boom in public and private renewable energy investment. Even though wave energy is more challenging to harvest than other renewable energy resources, WEC technology is well poised to be rebooted by increased national support strategies and market incentives, and reinforced IAG collaboration.

6.2 Pre-commercial development of wave energy

As mentioned in Section  6.1 , the OWC navigation buoy developed in Japan is the only successful commercialisation case, with no full-scale commercial WEC farm in operation. Commercial devices refer to WECs that are: (i) characterised by high TRL and TPLs, that is, (TRL,TPL)=(9, > 7); (ii) fully functioning, with affordable electricity for utility markets or reliable power supply for niche markets; and (iii) market accessible with specific product availability. Current WECs cannot fully satisfy (ii) and (iii), though some can meet (i), referred as pre-commercial devices.

This subsection only summarises pre-commercial WEC projects and related R&D activities, with some well-known pre-commercial WECs listed in Table  4 and Figure  12 . In Table  4 , the development stage is defined according to the TRL [ 66 ], as shown in Table  3 . It can been seen that only the Mighty Whale , Pelamis , Mutriku and SeaBased devices are at stage 5. Most pre-commercial devices are based on OWC or PA concepts, showing high consistency to the R&D foci reviewed in [ 11 , 62 , 221 ].

6.3 Prospects for commercialisation

Learning from the operational experience of pre-commercial WECs in Table  4 , some recommendations for commercialisation may be made: (i) System survivability should be the most important concern for commercialisation, as several WECs suffered from structure failure in storms. (ii) The WECs in Table  4 require significant development funding and time, mostly developed along the TRL-first trajectory. Without considering TPL at early stage of WEC development, WEC projects may fail purely due to poor installability or transportability, for example, the OSPREY and greenWAVE devices. Thus, the TPL-first development trajectory is strongly recommended to save development cost and time, and to mitigate development risk. (iii) The real performance of various pre-commercial WECs is not as optimistic as expected, potentially due to optimistic power production estimates (from linear models) or capacity factors. The capacity factor in long-term testing is low [ 220 , 236 ], for example, 0.11 [ 236 ], but is over-optimistically estimated as 0.3 in LCoE assessments [ 59 , 245 ]. Efforts to reduce the uncertainty in LCoE assessment are important in improving investment decisions and investor confidence.

7 MARKET OPPORTUNITY FOR WAVE ENERGY

The dominant target market for wave energy technology is utility-scale electricity, though R&D and commercialisation activities towards niche markets are also emerging. Both of these market opportunities are now separately considered.

7.1 Utility market

As mentioned in Section  1 , there exists a conflict between the increasing global energy demand and carbon reduction promises [ 3 ], with a focus on renewable energy technologies to provide carbon-free electricity. However, the current LCoE of wave energy is estimated at a high level, ranging 120–470 $/MWh (about 100–400€/MWh) [ 59 ]. Compared with other mature renewable energy resources, for example, solar and wind power, or fossil fuels, wave power is not competitive or market viable in the utility market [ 246 ], as shown in Figure  13 . If carbon pricing is applied to the energy technologies in Figure  13 , renewable energy resources will have lower an LCoE than fossil-based resources. In this scenario, wave energy technology is, indeed, marketable.

As wave energy technology is untapped, its LCoE can be further reduced to 100–300 $/MWh (about 84–252 €/MWh) for GWs of installed capacity, and to 100–150 $/MWh (about 84–126 €/MWh) for 10 GW installed capacity [ 59 ]. With accumulated operation experience, a recent OES annual report projected that the LCOE of wave energy can be reduced to 100–150€/MWh by 2030–2035 [ 247 ]. Thus, electricity from wave energy is expected to be competitive in the utility market.

In addition, wave energy can be treated as a complementary source for offshore wind farms [ 14 , 17 ], and the combination of wind and wave energy results in legislative and technical synergies for both technologies [ 15 ], to reduce the LCoE further. However, such a combination strongly depends on installation sites [ 18 ], and an ideal site, characterised by less extremes, elevated mean values, stable behaviour and low correlation, will result in a more smooth output and fewer hours of zero production. The Irish coast is such an ideal site for wind-wave integration [ 14 , 17 ], where wind and wave resources are low correlated, and joint wind-wave farms can mitigate against the high-frequency variability in both resources.

WEC technology, with accumulated install capacity at GWs, shows a great potential for the utility market by providing carbon-free and affordable electric, but still unattractive to private investors, as its R&D and commercialisation activities are still costly and risky to invest in.

7.2 Niche market

As mentioned in Section  6.1 , the only successful commercialisation of WEC technology is the OWC navigation buoy, which belongs to a niche market rather than the utility market. For the past decade, many countries have established regional or national strategies to develop their ‘blue economy’. Thus, ocean-based applications, for example, ocean observation and desalination, and fish farming, are growing, and require an economical and clean power supply [ 248 ]. Wave energy can meet such energy demands to advance the blue economy, where alternative (especially conventional) energy sources can be prohibitively expensive, due to the relatively remote consumption point.

Ocean-related niche markets include: (i) ocean navigation and observation [ 249 ], (ii) coastal protection [ 250 - 252 ], (iii) desalination [ 253 , 254 ], (iv) island micro-grid [ 138 ], and (v) marine aquaculture, (vi) multi-function offshore platforms [ 255 , 256 ], and (vii) other applications, for example, underwater vehicle charging, disaster recovery and resiliency, seawater mining and marine algae [ 248 ]. These potential niche markets are well summarised in [ 22 , 221 , 257 ].

Compared with the utility market, the rated capacity for niche markets is much smaller, ranging from several Watts to hundreds of kW. Such a relatively small capacity may result in a small geometric dimension, consequently reducing development time, cost and risk, which make it more appealing to public and private investment, with potential investors already coming from financially secure application domains, showing strong potential to pass through the VoD to a commercial success. It is also expected that rapid growth in wave energy niche market applications can assist the development effort for the utility market, by accumulating operation and maintenance experience, as well as WEC system design expertise.

7.3 Prospects for commercialisation

To sum up, the potential size of the utility market is up to TWs, but current wave energy technology has not fully demonstrated its competitiveness with respect to other energy technologies. As technical, economical and administrative challenges co-exist, reinforced IAG collaboration is strongly recommended to advance the TPL of wave farms for the utility market. Niche markets in ocean applications have been emerging, and wave energy shows promising potential in providing clean and economic power supply for ocean-based applications. However, the market size is still unknown, and only limited operational experience is available, creating cost uncertainties. Longer term operations are required to further quantify the commercial potential of wave energy technology for niche markets.

8 FACTORS AFFECTING THE DEVELOPMENT OF WAVE ENERGY

Recalling the historical development of wave energy technology, as documented in Figure  11 , key factors that significantly influence the development of wave energy technology can be separated into external factors, for example, fossil fuel price, development of other renewable energy technologies, and national/community factors, for example, supporting strategies and market incentives, which are discussed in the following two subsections.

8.1 External factors

In Figure  11 , 1973 and 1985 can be clearly identified as turning points, mainly due to the rapid changes in oil price in those years. Oil prices surged from 21 to 57 $ per barrel in 1973, resulting in a corresponding surge in wave energy development, while the price collapsed from 71 to 25 $ per barrel in 1985 consequently disincentivised R&D development. Although fossil fuel prices comprise one of the most important factors, such a causal factor is somewhat unpredictable, though (despite new recovery methods such as fracking) one can only imagine that fossil fuels will become increasingly more expensive, with dwindling supply.

In Figure  11 , 2012 is also marked as a tipping point, in which a series of WEC company failures were indicative of the currently low marketability of WEC technology in the utility market. Compared with other renewable energy technologies, for example, wind and solar power, current WEC technology is untapped and uncompetitive with a high LCoE, as shown in Figure  13 . One hard lesson learnt from some failed projects is that the TPL-first development trajectory should be used, to address technology performance at early development stages. With costs scaling up exponentially with scale, a premature rush to high TRL levels has been shown to be costly. The repercussions of some high profile WEC company failures are still felt in the wave energy community. Perhaps, over-optimistically, wave energy technology is projected to achieve a competitive LCoE at 100-150 €/MWh by 2030–2035 [ 247 ].

A further external factor is the rapid rise in other renewable energy technologies, for example, in offshore wind (including floating offshore wind) [ 258 , 259 ], which builds on many years of expertise experience in wind energy, with incremental technical problems only to be solved, while wave energy still wrestles with fundamental issues. The rapid acceleration in offshore wind has garnered both offshore technologists and investors from the wave energy sector.

8.2 National strategies and market incentive

For public investors, for example, governments, the benefits of investing in wave energy are many, including (i) environmental benefit, achieving carbon neutrality; (ii) broadening of the energy mix and provision of greater energy security; and (iii) economic benefit, for example, industry and job creation, blue economy. Based on the OES annual report in 2017, some national supporting schemes for ocean energy are detailed in Table   5 , within which UD means under development.

	National strategy				Market incentive
Country	Capacity Target	National Action Plan	Technology Roadmap	Marine Spatial Plan	Feed-in Tariff	Contract for Difference	Green Certificate	Quota obligation	Renewable Energy Auction
Belgium
Canada
China
Denmark
France				UD
Germany
Ireland				UD
Italy
Japan
Korea
Mexico
Netherlands
Monaco
Norway
New Zealand
Portugal
South Africa
Spain
Sweden				UD
UK
United States

In Table  5 , national strategies include: (i) capacity targets, to express national commitment to ocean energy deployment; (ii) action plans agreed by public and private sectors to facilitate deployment; (iii) roadmaps, providing long-term frameworks for developing policies and supporting actions; and (iv) marine spatial plans, to remove administrative barriers. As ocean energy resources and market data vary from country to country, national strategies developed by each country also vary and are detailed in [ 260 ]. Roadmaps with specified long-term pathways are important to mobilise national efforts to improve ocean technology, which are articulated in a number of countries, for example, UK [ 261 ], Denmark [ 262 ], and Ireland [ 263 ]. Additionally, national policies for innovation, manufacturing and deployment of ocean energy are discussed in [ 264 ].

Table  5 also summarises several commonly applied market incentives, of which the FIT is the most common supporting measure, as it can directly improve the profitability of ocean energy projects. Since 2014, the UK has provided the ‘contract for difference (CfD)’ mechanism to replace its original used FIT scheme [ 260 ]. The FIT and CfD schemes belong to market-pull incentive, while the others fall into the market-push class [ 265 ]. FIT is one of the most successful incentive schemes for promoting the growth in wind and solar power [ 266 ], and is naturally expected to have significant influence on encouraging private investment in wave energy. For wave energy technology, sensitivity of net present value to FIT variation is analysed in [ 63 , 267 ], which also address the effectiveness of FIT schemes, in relation to its impact sensitivity on project profitability [ 63 ]. This reveals some of the rationale for policy makers to applying FIT schemes to encourage private investment. Some active FIT supporting schemes are summarised in Table  6 , which can prolong public investment and encourage corresponding private investment to bridge the VoD.

Country	Strategy	Capacity (MW)	rate (€/MWh)	duration (Year)
Canada	Ontario FIT	5.5	170	40
China	FIT	–	330	–
France	FIT	–	173	–
Germany	FIT	50; 50	124; 34.7	–
Ireland	FIT	–	260	–
Italy	FIT	5; 5	300; 190	15
20
Netherlands	Subsidy	–	130	–
Philippines	FIT	–	310	–
UK	CfD	–	360	–

Occasionally, governments invest directly in interventions and mechanisms that accelerate the development of wave energy intellectual property (IP), in addition to providing FIT support schemes. Such interventions are somewhat altruistic, since the benefits of directly supporting fundamental technology development can be potentially enjoyed by many other jurisdictions. However, maintaining and supporting wave energy IP development directly brings the capability to generate a significant export industry and supply chain, which is of potentially greater long-term value than the development of wave farms locally. Although many jurisdictions provide general funding schemes for both R&D and commercialisation, Wave Energy Scotland is somewhat unique in dedicating funding to wave energy IP development, originally founded to retain and manage the IP held by Scottish companies Pelamis Wave Power and Aquamarine Power , following their demise in 2014 and 2015, respectively. In a more measured way, some jurisdictions have included ocean energy as one of the national research priorities, for example by Science Foundation Ireland in 2013, giving some level of preferential treatment to research and R&D proposals in this area.

9 CONCLUSIONS

This review summaries the historical and ongoing research and commercialisation efforts devoted to wave energy technology. Significant spatial and temporal variability in the wave power resource has a fundamental role in diversifying the development of successful WEC concepts, with a need for a collective approach to common fundamental issues, such as modelling, PTO and control design, survivability, and performance metrics. Regarding technology development trajectories, TPL must clearly be prioritised over TRL, particularly at early project stages. Clearly, investor risk must be reduced by providing more certainty in national and international support programmes, focussing on common technological challenges, to reduce LCoE, LCoE uncertainty and to examine limitations in supply chains and marine licensing arrangements, and maximisation of the potential of IAG collaboration.

Historical analysis has shown that survivability and installability are key metrics, which not only affect the economics and success of individual wave energy projects, but also play a large role in sector confidence and investability. With increasing emphasis on the provision of carbon-free energy, and a need to diversify the mix of renewable energy sources, wave energy is well poised to supplement, and complement, existing and more mature renewable energy technologies. The next decade will be crucial in deciding if wave energy can make the breakthrough needed to become a mainstream renewable energy technology.

ACKNOWLEDGEMENTS

This document is the result of a research project funded by the Marie Skłodowska-Curie Action (Grant No. 841388) and Science Foundation Ireland (Grant No. SFI/13/IA/1886, and Grant No. 12/RC/2302-P2 for the Marine and Renewable Energy Ireland Centre).

CONFLICT OF INTEREST

The authors have declared no conflict of interest.

Open Research

Data availability statement.

Data sharing not applicable as no new data generated, or the article describes entirely theoretical research.

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Volume 15 , Issue 14

Special Issue:Advances in Wave Energy Conversion Systems

26 October 2021

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Intelligent energy management systems: a review

• Open access
• Published: 13 March 2023
• Volume 56 , pages 11635–11674, ( 2023 )

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• Stavros Mischos   ORCID: orcid.org/0000-0002-6290-1133 1 ,
• Eleanna Dalagdi 1 &
• Dimitrios Vrakas 1  

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Climate change has become a major problem for humanity in the last two decades. One of the reasons that caused it, is our daily energy waste. People consume electricity in order to use home/work appliances and devices and also reach certain levels of comfort while working or being at home. However, even though the environmental impact of this behavior is not immediately observed, it leads to increased CO2 emissions coming from energy generation from power plants. It has been shown that about 40% of these emissions come from the electricity consumption and also that about 20% of this percentage could have been saved if we started using energy more efficiently. Confronting such a problem efficiently will affect both the environment and our society. Monitoring energy consumption in real-time, changing energy wastage behavior of occupants and using automations with incorporated energy savings scenarios, are ways to decrease global energy footprint. In this review, we study intelligent systems for energy management in residential, commercial and educational buildings, classifying them in two major categories depending on whether they provide direct or indirect control. The article also discusses what the strengths and weaknesses are, which optimization techniques do they use and finally, provide insights about how these systems can be improved in the future.

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1 Introduction

Nowadays, electrical energy plays a vital role in various aspects of our life. However, the lack of ecological awareness along with the absence of energy-friendly infrastructures has led into increased energy consumption and waste. According to estimates of the United States Energy Information Administration USA ( 2019 ), 40% of the annual CO2 emissions are directly related to the electricity consumption. Out of these emissions, 40% of them concern residential and commercial consumers, and as Armel et al. ( 2013 ) and Darby ( 2006 ) mentioned, it is possible to achieve 20% savings if we use power more efficiently. Therefore, electricity consumption and wastage reduction can offer environmental and financial benefits to our society.

Different approaches and systems have been proposed in the literature that aim to reverse climate change and global warming. Intelligent energy management systems with incorporated automations is a promising approach towards the solution of these environmental problems. These systems convert a conventional home or building into a “smart” version of it. Smart Homes and Buildings, according to Energy ( 2021 ), include automations systems which provide the ability to monitor and control various services such as, lighting and heating-ventilation-airconditioning (HVAC), or devices such as fridges, ovens and washing machines. The set of installed sensors, actuators and smart devices constitute an Internet-of-Things (IoT) subsystem. When users are surrounded by microcontrollers and smart devices, they follow the paradigm of Ubiquitous or Pervasive Computing . When Artificial Intelligence (AI) methodologies enable the interaction of people with these devices, the environment embodies Ambient Intelligence (AmI) (Weiser 1993 ).

These environments play an important role in the Smart Grid . Smart grids consist of two parts, the supply-side and the demand-side , which optimize the energy production, transmission, distribution and consumption (Mir et al. 2021 ). Smart homes are a necessity for the demand-side of these grids because even if the supply-side is successfully optimized, a faulty demand-side, e.g. a conventional home/building, will decrease the total effectiveness of the system.

An immediate conversion of all residential and commercial buildings from conventional to smart, is a costly and time-consuming procedure. Even if governments around the world wanted to carry out this plan, the high deployment costs remain an impediment (García et al. 2017 ; Shigeyoshi et al. 2013 ). Therefore, research was expanded towards lower or no-cost energy saving solutions based on information and communication technologies (ICT) (Luo et al. 2017 ).

Research and development of energy management systems focused on new technologies that embody energy savings, and materials that decrease wastage of energy. However, the same attention wasn’t given at users’ behavioral change. Darby ( 2006 ), suggested that in order to improve the awareness on energy waste habits, consumers must firstly monitor their power consumption and then manage it after receiving appropriate advice. Governments and Non-Governmental Organizations support and facilitate energy-efficiency changes. However the impact of simple saving tips and peer devices’ comparison is low because of the wrong time and place these occur (Cattaneo 2019 ). End-users must alter their routine completely and adopt an environmental friendly behavior (Becchio et al. 2018 ).

Recommendation systems are information systems that assist users to discover personalized content, based on their preferences (Resnick and Varian 1997 ). They are used in many different real-world scenarios (Martin 2009 ) and recently some implementations were applied into energy profile reshaping (Alsalemi et al. 2020 ; Sardianos et al. 2019b ) using deep learning algorithms (Wei et al. 2020 ), data mining techniques (Ashouri et al. 2018 ), behavioral analytics and human decision-making processes to develop context-aware systems (Şimşek et al. 2016 , Sardianos et al. ( 2019a )). Despite the fact that these systems emerged in the mid 90’s (Resnick et al. 1994 ; Shardanand and Maes 1995 ), Himeur et al. ( 2021 ) reached the conclusion that the field of energy saving recommendation systems is still unexplored.

Intelligent Energy Management Systems (IEMS) are a necessary tool to reduce energy overconsumption in households, commercial, educational and industrial buildings and subsequently the total CO2 emissions that are produced. To be more precise, studies indicate that commercial buildings are consuming almost 40% of the total energy in most developed countries (Cao et al. 2016 ). Therefore, real-time energy usage monitoring, along with systems that can offer ways to manage energy consumption and, alternative sustainable energy sources (e.g. solar panels), are of the highest importance (Mir et al. 2021 ). This work provides a comprehensive review of IEMS of the literature over the last decade. Our goal in this article was first, to provide the readers an overview of the influential factors of energy overconsumption and also an overview of various approaches towards energy efficiency. Second, we present a high level architecture breakdown for these systems. Third, we provide a review of the state-of-the-art components of each module and we introduce a novel classification for the IEMS in Direct control systems, i.e. systems that provide automations to the environment in order to control functionalities and conditions, and in Indirect control systems, i.e. systems that aim in the behavioral modification of the occupants. Fourth, for these two novel classes, we discuss their respective advantages and disadvantages which class to conclude which class is more suitable for each environment. Finally, we provide a short discussion about their limitations and problematic aspects and also some future research orientations.

The remaining of our study is organized as follows. Section  2 presents our motivation towards studying and comparing these two types of energy management systems. Section  3 discusses related work on this topic and refers to surveys performed on smart environments and recommendation systems for energy efficiency. Section  4 provides background information of the energy efficiency topic from a researchers’ perspective. In Sect.  5 , we present necessary specifications for an intelligent energy management system and an overview of their architectures. Subsequently, Sect.  6 presents a discussion and an analysis of the advantages of each class, their problematic issues and some suggestions for future research. Finally, Sect.  8 concludes our findings.

2 Motivation

Energy management systems are a promising solution towards energy wastage reduction. The variety of studies on smart environments, and the plurality of algorithms and techniques developed over the last decade for automations and recommendations’ optimizations, are proofs of how important these systems are in our effort to reverse climate change and global warming. During our research, we noticed that in current literature, every discussion about smart environments involved mostly systems with integrated automations. Nevertheless, new systems emerged recently which incorporate recommendations mechanisms, aiming at occupants behavioral change rather than in automations. Therefore, we believe that a review was necessary in order to study both types of Intelligent Energy Management Systems.

From our perspective, studying, reviewing and eventually researching IEMS is an extremely important topic especially during the climate and energy crisis we have been experiencing in recent years. These systems can offer environmental solutions regarding efficient energy consumption in various building types, just by adjusting the type of actuation they will incorporate. Each type of building has different needs and capabilities to control its energy footprint and it is crucial for the community research to develop systems with the right approach for each case. For these reasons, a review of the state-of-the-art IEMS, an analysis and the extraction of useful insights is necessary in the literature. To the best of the authors knowledge, a review that include all these topics in the way this article does, does not exist and that is why we start this specific investigation and research. Almost every review until know was focusing either on systems that incorporated automations in the actuation module or systems that were focusing on behavioral management. Moreover, none of these articles was comparing these two types of systems in terms of their suitability on a specific building type. In order to successfully develop and choose an IEMS, a comparison of their advantages was necessary and will give the readers better perspective.

Before referring to IEMS, it would be useful to discuss about efficient energy consumption. There are multiple influential factors that cause energy overconsumption both in residential and in commercial environments. Moreover, a small discussion about various approaches towards energy efficiency. Our goal was to figure out which are the ways to achieve energy saving results and which implementations seem more promising for each installation environment.

Next, we wanted to proceed with the presentation of the IEMS architecture, and provide the reader with a categorization of their components and a classification of their sub-parts. During our research, a classification occurred for the IEMS in Direct Control IEMS and Indirect Control IEMS. Every IEMS can be classified in one of these classes based on the design of its actuation part. Besides various state-of-the-artcomponents we wanted also to show state-of-the-artcomplete prototypes that have been developed by research teams.

In the final parts of this article, we wanted to discuss about the advantages and disadvantages of each class of IEMS, compare their different aspects, investigate their major open problems and discuss about research gaps and future research orientations that will be helpful for researchers.

3 Related work

In this section, we present surveys and reviews that are related to IEMS (Table  1 ). Our intention is to provide the reader an extensive look in the field of Energy Management Systems in Residential, Educational and Commercial Buildings. One could read the work of De Paola et al. ( 2014 ) in order to understand which are the general approaches to energy efficiency and also the main architectural, technological and algorithmic aspects of an energy management system. Leitao et al. ( 2020 ) proposed a similar architecture that also incorporates smart appliances, while Lin et al. ( 2017 ) presented a more abstract architecture for IoT-based systems, consisting of three layers: a Perception layer , a Network layer and an Application layer. Boodi et al. ( 2018 ) dealt with a review of the state-of-the-art Building Energy Management Systems (BEMS) focusing on three model approaches: White box, Black box and Grey box models. They also performed a comparative analysis of the factors that have the highest impact in energy consumption. Himeur et al. ( 2020 ) surveyed a large number of databases with data that refer to building power consumption, with several features compared and examined, such as geographical location, number of monitored households, sampling rate, etc. Moreover, this research team presented a novel dataset for power consumption anomaly detection. Such a dataset will be very useful for training and testing models that aim to detect anomalies in order to reduce energy wastage. Finally, they performed review on current trends and new perspectives in the field of anomaly detection of energy consumption systems. According to them, detecting an anomaly in the energy data can help us prevent a problem before it gets big and spreads (Himeur et al. 2021 ).

When IEMS are installed, they provide AmI at the environment. According to Cook et al. ( 2009 ), there are various definitions about AmI depending on the features that are included. These environments offer environmental, comfort, safety and financial benefits. AmI is also an umbrella term which applies into technologies embedded into a physical space to create an invisible user interface augmented with AI (Dunne et al. 2021 ). They presented an comprehensive survey on Ambient Intelligence (AmI) and Ambient Assistive Living (AAL) while referring to the state-of-the-art AI techniques and methodologies to implement these systems.

An interesting study was performed by Shareef et al. ( 2018 ) reviewing load scheduling controllers which integrate AI techniques such as, artificial neural networks (ANNs), fuzzy logic, adaptive neural fuzzy inference and heuristic optimization. Al-Ani and Das ( 2022 ) surveyed the use of Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) techniques in home energy management systems (HEMS). They analyzed multiple RL algorithms, their objectives, and also their testing environments. RL and DRL seem like a very promising approach in simulation platforms but until now they are too slow during training and that is why only 12% of these approaches have been tested in the real world. Mason and Grijalva ( 2019 ) have also performed a review article about RL applications for autonomous building energy management. They showed that RL algorithms improve significantly the energy efficiency in domestic environments and also that DRL algorithms are usually preferred more than RL algorithms. Nevertheless, they found out that most of the RL approaches are tested mostly at simulation platforms and that an accurate simulation design is needed before these systems are installed in real world scenarios. An example of such a DRL algorithm was developed by Lissa et al. ( 2021 ). They proposed an algorithm for indoor and domestic hot water temperature control, aiming to reduce the total energy consumption by optimizing the way that solar produced energy is being used.

Leitao et al. ( 2020 ) conducted a survey about home energy management systems, where they discussed about the demand-side management, i.e. the collection of techniques applied to reduce energy costs on the consumption-side and improve energy efficiency. Furthermore, they discussed about dominant scheduling methodologies which are grouped into five categories. Additional energy saving techniques are presented by Mir et al. ( 2021 ), including statistical models, cloud computing-based solutions, fog computing, smart-metering-based architectures and also some IoT inspired solutions. Beaudin and Zareipour ( 2015 ) reviewed methods employed to model various aspects of residential energy management systems. Moreover, they discussed about complexity in such systems and presented also an overview of techniques for scheduling approaches, as well as a classification in mathematical programming, meta-heuristic search and heuristic scheduling techniques. Finally, a recent study by Ali et al. ( 2021 ) summarized research opportunities created by open issues in the field such as, blockchain-enabled IoT platforms for distributed energy management, deep learning models to handle, use and evaluate big energy data, peer-to-peer energy trading and demand-side energy management, context-aware pervasive future computing, resilience-oriented energy management, forecasting models, user comfort and real-time feedback systems as well as, Internet of Energy (IoE)-based energy management.

Multi-agent systems (MAS) is an approach used to model components of IEMS. An interesting article was published by González-Briones et al. ( 2018 ) reviewing state-of-the-art developments in MAS and how they are used to solve energy optimization problems. They discussed about the types of MAS architectures and also the reasons why they must be used as modeling tools. Asare-Bediako et al. ( 2013 ) proposed a multi-agent architecture of distributed intelligence to solve the complex and dynamic decision process of optimal energy management. Their architecture was based on four groups of agents: control and monitoring, information, application and management and optimization agents. According to the definition of MAS (Poole and Mackworth 2010 ) an agent is comprised by a coupling of perception, reasoning and acting components. Mekuria et al. ( 2018 ) conducted a comprehensive literature review aiming to identify and characterize the reasoning systems in MAS-based smart environments and also presented the strengths and limitations of them.

During the last decades, researchers studied means in order to transit into more sustainable energy usage. Because of their daily consumption, households are responsible for 72% of global greenhouse gas emissions (Hertwich and Peters 2009 ). Saving energy and being more efficient in our daily power consumption is important to reduce our greenhouse gas emissions. As previously mentioned, occupants’ behavior influences energy wastage, therefore behavioral changes of occupants remains a key objective for energy scientists. Steg et al. ( 2018 ) discussed of ways to promote the active engagement of people in a sustainable energy transition. According to them, some relevant behaviors must be managed and manipulated. Household consumption and behavioral decisions manipulation can offer a substantial reduction of greenhouse gas emissions if regulatory framework is set that supports behavioral changes (Dubois et al. 2019 ).

Recommendation systems are an important research field since the mid 90’s. Their goal is to help users find online content based on personalized preferences using collaborative or content-based filtering along with AI techniques such as, association rules, clustering, decision trees, k-nearest neighbor, neural networks, regression, etc.(Park et al. 2012 ). Over the last decade, the research community began to integrate recommendation modules into the components of smart environment systems to persuade users to adopt a more Eco-friendly behavior. Steg et al. ( 2018 ) discussed on ways to make people more engaged into actions and habits that are more energy sustainable. Furthermore, they analyzed how these actions will affect their daily lifestyle and comfort. Based on recent studies, Zhang et al. ( 2018a ) estimated that occupants behavior affect 10–25% of the consumption in residential buildings and around 5–30% in commercial buildings. Moreover, there are four topics where researchers must focus in order to identify the effects of occupants behavior in daily power consumption: understand occupants’ actions that affect space heating or cooling, develop methods and techniques to collect data on behavior and building performance, model quantitatively occupants’ behavior and building energy performance and finally, create an evaluation technique for the occupants to identify their energy saving potential.

For there recommendations systems and their applications, an extensive literature survey was conducted by Himeur et al. ( 2021 ) on energy saving recommendation systems in buildings, discussing how they evolved and also providing a taxonomy based on the nature of the recommender engine, their objectives, computing platforms and evaluation metrics. Furthermore, a critical analysis was also conducted to understand what the limitations of these systems are when they aim in energy efficiency. Additionally, Law et al. ( 2018 ) studied a set of recommendations published by companies and agencies, and designed micro-models to estimate how popular recommendations affect energy savings and conducted also a followup study to understand which types of recommendations were chosen and adopted more often.

From a theoretical point of view, Cattaneo ( 2019 ) analyzed the barriers towards the adoption of technologies for efficient energy usage and introduced ways to overcome them. Moreover, McIlvennie et al. ( 2020 ) indicated in their meta-review, that automation and control technologies are only “one piece of the puzzle”. The other one is systems that embed humans in the procedure of excessive consumption reduction using behavioral change techniques through recommendations and feedback. They suggested that the integration between techno-centric and user-centric approaches in more holistic implementations will be more effective.

In this section we mentioned various surveys that have been performed and are related to IEMS. In Table  2 we present a comparison between these surveys and our work. In the next sections, we will show that each IEMS, regardless of its type, follows a similar architecture. Our work breaks down their components and studies state-of-the-art designs, devices and algorithmic approaches. These IEMS implementations are based on requirements extracted from our need to tackle the factors that cause energy overconsumption. To the best of our knowledge, until now, no survey has been performed that both discusses approaches towards energy efficiency, factors that affect it and at the same time present a overview of the architecture, the components and their categories. Moreover, our work presents a novel classification on IEMS based on the type of actuation they incorporate and discusses major open problems of these classes and suggests research directions that will help community to develop better and more efficient IEMS, specialized for each specific installation environment.

4 Efficient energy consumption

4.1 influential factors.

The first step to achieve energy waste reduction is to understand where it originates from. According to Ashouri et al. ( 2018 ), there are four major influential factors of this phenomenon:

Building characteristics Construction materials and insulation levels are obvious factors that increase energy waste in all types of buildings. van den Brom et al. ( 2018 ) conducted a research on performance gaps in energy consumption, revealing that recent buildings, constructed with modern materials, consume less energy than recently renovated older buildings. Furthermore, a difference between actual and theoretical (simulated) consumption was also noticed.

Occupants behavior Occupants affect the overall energy consumption, especially in residential buildings (Bourgeois 2005 ). Even in buildings with the same energy labeling, discrepancies can occur in consumption, depending on heater/cooler set temperature, hot water wastage, requirements of indoor environmental quality, lighting usage, etc.

System efficiency and operation Many buildings or households are equipped either with low efficiency or outdated appliances and devices. Systems’ efficiency affects dramatically the total power consumption, as well as, neglected appliances such as oven, microwave, washing and drying machines.

Climatic conditions Outdoor temperature, solar radiation, humidity and wind velocity can affect energy consumption, especially combined with the aforementioned factors. Even though, these conditions cannot be managed, it is important to realize how they increase energy wastage in order to search for effective solutions.

4.2 Approaches towards energy efficiency

4.2.1 general approaches.

In order to confront climate change, society has to adopt a more energy efficient mindset. Corucci et al. ( 2011 ) identified four approaches towards energy efficiency: user awareness about energy consumption , reduction of standby consumptions , plan and scheduling of flexible activities and adaptive control .

Firstly, user awareness is a straightforward way to achieve energy wastage reduction (De Paola et al. 2014 ). Providing appropriate feedback, advice and recommendations along with detailed information about daily power consumption and total cost can encourage users to follow a more eco-friendly behavior (Darby 2006 ). However, aggregated measurements of energy consumptions make it difficult to understand which device or behavior causes the biggest waste (De Paola et al. 2014 ). Moreover, various studies (Himeur et al. 2021 ; Law et al. 2018 ; Jiang et al. 2009 ) show that behavioral change is still an ineffective strategy in the long term, therefore more research is required.

Secondly, standby devices and appliances are hidden sources of energy wastage. It was first identified as a new challenge in the early ’90 s (Sandberg 1993 ) when analysts began to study the number of appliances that were “leaking” electricity. According to Gram-Hanssen ( 2010 ), any plugged-in device in standby mode can consume some amount of energy, but the increased number of such devices in households and buildings lead to substantial increments in the total consumption. TVs, PCs, Coffee Machines and Printers, are some of the devices in every home, consuming energy without being used for long periods of time (De Paola et al. 2014 ). An interesting study was conducted by Hess et al. ( 2022 ), showing that education was positively correlated with the reduction of standby energy consumption. Moreover, households with children were using less multi-sockets and were less likely to waste standby energy, whereas high-income households were correlated with higher energy consumption.

Thirdly, activity planning and scheduling using modern smart automations systems can offer reduction of energy consumption during energy demand peak (De Paola et al. 2014 ). Scheduling activities, offers financial benefits when energy fares vary between day and night. Furthermore, as Bouakkaz et al. ( 2021 ) proposed, users can also save energy when a house is connected, through a hybrid energy system, into battery storage units.

Finally, another approach to reduce wasted energy is the installation of adaptive control mechanisms. HVAC and lightning systems waste energy in order to preserve user’s comfort. However, the incorporation of AI techniques such as user-presence detection, behavior prediction or reinforcement learning control can tune activation times of these services to avoid unnecessary consumption (De Paola et al. 2014 ; Eller et al. 2018 ).

4.2.2 Energy hubs and microgrids

Besides the four aforementioned approaches, another way to move towards energy efficiency, on a higher level, is the deployment of micro-grids infrastructures in community areas, commercial areas, etc. A micro-grid is a local electrical grid with defined electrical boundaries, acting as a single and controllable entity (Hu and Lanzon 2018 ). Microgrids are important to successfully transform existing grids into smart grids. These grids lead to decrease of the operational costs, reduced emissions and increase of energy efficiency system reliability (Bandeiras et al. 2020 ). These interoperable energy systems consist of local energy production units to increase self-sufficiency using solar cells, heat pumps and recycling of wasted water to achieve sustainability. Another approach towards sustainable energy consumption are the energy hubs which are multi-generation systems that supply different types of energy demands simultaneously by both converting energy carries and using energy storage systems (Nasir et al. 2022 ).

Various research works have been done in recent years on these systems In order to improve energy efficiency of energy hubs, decrease pollution and improve their reliability, Zhang et al. ( 2015 ) devised in a novel framework for the optimal planning of energy hub systems, whereas Mansouri et al. ( 2022 ) developed a two-stage stochastic model for the design and operation of an energy hub in the presence of electrical and thermal energy storage systems. As mentioned earlier, energy storage systems are crucial parts towards energy efficiency. Energy hubs incorporate these subsystems along with different energy carriers and demand response programs. Javadi et al. ( 2022 ) described in their study the joint operation and planning problem as a two-stage optimization problem to successfully design a model that will achieve optimal sizing and siting of an electrical energy storage device, along with electricity tariffs due to demand response program. In order to optimize the efficiency of these multi-systems and their emissions, Mansouri et al. ( 2022 ) developed a multi-objective model to design a hub considering the variable efficiency of its converters, the degradation of its equipment and the annual growth both in load and in energy prices. Nasir et al. ( 2022b ) studied the day-ahead scheduling of these infrastructures considering also the uncertainties on each energy carrier. Finally, Mansouri et al. ( 2021 ) designed also a scenario-based framework for an energy hub that includes a power-to-gas system, proving that the implementation of an integrated demand response program, along with renewable energy sources, reduced energy costs and CO2 emissions.

Similar frameworks have been developed also for microgrids aiming to reduce operating costs for users. An optimization framework for planning active distribution networks have been developed by Matin et al. ( 2022 ). Mansouri et al. ( 2022 ) presented a framework for the scheduling of microgrids considering also the load demand, the market prices and the renewable power generation level. Last but not least, another two-stage stochastic optimization problem was solved by the researchers to design successfully microgrids with dispatchable generators and wind turbines for energy production (Jordehi et al. 2022 ).

4.2.3 Energy saving based on behavioral change

Many research works are focusing into the modification of human habits and are looking for ways to manipulate them towards energy efficiency. A recent example is a system aiming to detect repeated usage patterns from consumption logs, developed by Sardianos et al. ( 2021 ). Another energy-saving recommendation system is presented in Varlamis et al. ( 2022a ), which fuses data from sensors, with users’ habits and their feedback, to provide personalized advises to occupants at the right moment. This system is implemented in the (EM) \(^3\) platform, which will be introduced in the next chapter. Two different edge-approaches were implemented by Sayed et al. ( 2021 ) and Alsalemi et al. ( 2021 ) that incorporate recommendation systems for energy efficiency into a home assistant and an edge-based custom device.

One major problem that energy related recommendations systems face is the user engagement. When actions are not automated and users must act, it is really difficult for them to retain engagement in the recommendations platform. Sardianos et al. ( 2020a ) implemented real-time personalized recommendations system that also provided energy saving facts that aim to increase the persuasiveness of the recommendations. Besides the aforementioned apporaches, another way to produce optimized recommendations is Reinforcement Learning. Shuvo and Yilmaz ( 2022 ) proposed a DRL method which integrated human feedback and activity in the decision process to optimize electricity cost and users’ comfort. This system was developed to be used in domestic environments. RecEnergy is recommender system aiming at energy consumption reduction in commercial buildings by human behavior modification (Wei et al. 2020 ). The overall testing over a four-week period showed energy reduction between 19% and 26%.

5 Intelligent energy management systems (IEMS)

A number of computer-aided tools and technologies were proposed in the last decade in order to effectively optimize energy consumption in our daily life. According to De Paola et al. ( 2014 ), each system or model that was developed must fulfill some basic functional and nonfunctional requirements. Each system has to perceive the environmental conditions of the place it will be installed, use the input data to learn users’ habits, behaviors, preferences, consumptions per device or appliance and also detect or predict existing context. Moreover, it must provide a way for the users to monitor the consumption and at the same time interact with them using notifications to gather feedback and commands. Finally, it should have the ability to modify its environment through actuation after planning optimized sequences of actions that will both reduce energy wastage and satisfy comfort preferences. In respect to the non-functional requirements, these systems have to maintain intrusiveness at low level in the matter of interaction with the user and the physical infrastructure. Furthermore, scalability and extensibility is desirable in such cases, meaning that the level of abstraction during the design should be high. Also, an intelligent system have to be easily deployed by the users and not not require installation from an expert. In the software engineering part of the implementation, the principle of modularity is really important to avoid problematic behaviors of the system. Finally, it is required to be interoperable, with respect to physical devices and other software components.

Energy management systems are developed in a unique way fulfilling the aforementioned requirements following the approaches of the previous section and also following a specific framework architecture (Leitao et al. 2020 ; De Paola et al. 2014 ). The main components of an IEMS are depicted in Fig.  1 :

Sensing and Measuring Infrastructure

Actuation mechanisms

Processing Engine

User Interfaces

Another classification is shown in Fig.  2 . Each IEMS requires at least one type of each component to work effectively.

Information that emerges from the sensing components is saved and processed by the Processing Engine. The engine is the specialized subsystem with components responsible for the process of all acquired data and also performs the optimization tasks based on the end-users preferences. It should also learn and recognize occupants’ activity patterns, communicate with the actuators and manage anomalies or outlier events. When decisions are made, they are transferred into the actuators to modify each appliance or device contextually. Along with the action commands, sometimes, the process engine provides recommendations to the end-users through the user interface to change behaviors that affect the total energy consumption. All these modifications are focused on the persuasion of a smaller energy footprint of our society, however economic impacts remain also a motive (Sardianos et al. 2020a ). Furthermore, through the user interface, users have access to graphs showing daily consumptions. The most common form of a user interface is a computer or smartphone application.

Intelligent energy management system

Classification figure of the components of an IEMS

A widespread approach to model state-of-the-art energy management systems is Multi-Agent Systems (MAS). MAS architecture is often used as a tool to model subsystems of an IEMS and is composed of multiple interacting intelligent agents (Hu et al. 2021 ). Each agent can be considered as “Intelligent” because it incorporated AI techniques such as decision-making or machine learning algorithms.

According to Wooldridge ( 2009 ), each agent in a multi-agent system has some important characteristics:

Autonomy The ability to be at least partially independent, self-aware and autonomous.

Local view The perception of the agent has boundaries and no agent has a global view. Otherwise, the agent will not be able to process that large amount of information.

Decentralization No control authority exists inside the MAS.

Wooldridge ( 2009 ) called the ability of an agent to act at a local level, “Sphere of Influence”. Each agent in a MAS has the ability to interact within a specific range. However, there are “spheres” which coincide, rising dependent relationships and creating a unified model. Because of that, MAS architectures are considered appropriate to model IEMS. González-Briones et al. ( 2018 ) argued that MAS are commonly used as models because of the communication, coordination and cooperation capabilities of the agents, and also because this design provides robustness to the system, when different tasks are assigned to each agent.

5.1 Sensors and measuring infrastructure

Sensors and measurement devices are installed on every smart environment, providing data about temperature, humidity and luminance levels, whereas different sensors are monitoring the presence of occupants. There are two types of IoT devices used for these tasks: Custom-made and Commercial . Arduino or Raspberry Pi microcontrollers are used by researchers to create custom modules that fit specific requirements, but in large-scale applications, commercial ones are a preferable option because of the default unified communication protocols.

5.1.1 Custom-made sensors

The most common sensors on these applications are the power consumption meters. Alsalemi et al. ( 2019a ) used SEN-11005 components on a microcontroller NodeMCU to build a custom energy monitoring device (Fig.  3 ). Eridani et al. ( 2021 ) built from scratch their own circuit for an electronic sensor consisting of several sub-circuits for, voltage and current metering, voltage regulation and operational amplification. This sensor was incorporated within an Arduino UNO that was processing the input data which were transferred through an ESP8266 chip to an application. Furthermore, Oberloier and Pearce ( 2018 ) created an open-source power monitoring system, designed around the Digital Universal Energy Logger (DUEL) Node. Ahmed et al. ( 2015 ) also designed their own smart plug using Zigbee protocol. Finally, Jamal et al. ( 2020 ) used ACS712 and ZMPT101B for current and voltage measurements, respectively.

Custom sensors by Alsalemi et al. ( 2019a )

Temperature and humidity sensors were also commonly used in energy management systems. Mataloto et al. ( 2019 ), Sardianos et al. ( 2020a ) and Alsalemi et al. ( 2019a ) used DHT-22 sensors to receive real-time contextual information from the environment. DHT-22 sensor can measure both temperature and humidity levels. On the contrary, Reddy et al. ( 2016 ) used an LM35 temperature-only sensor. LM35 has an analog communication protocol, while DHT-22 has one-wire. Therefore, LM35 is faster in data transmission but it is more sensitive to noise. Also, Kodali et al. ( 2015 ) used LM35 in an ambient intelligent system with an Intel Galileo board.

Light and motion sensors are extremely important components for systems that aim to reduce energy wastage. Rooms and spaces that remain unoccupied tend to have increased consumptions due to switched on lights. Wei et al. ( 2020 ) and Alsalemi et al. ( 2019a ) used a TSL2561 Adafruit sensor for light monitoring and HC-SR501 for motion sensing. Mataloto et al. ( 2019 ) also included photo-resistor sensors and motion sensors with passive infrared (PIR) in their custom sensor-board and Reddy et al. ( 2016 ), used a light dependent resistor (LDR) that reduces its resistance when light hits the surface of it.

Gomes et al. ( 2017 ) created EnAPlug (Fig.  4 ), a multi-sensor smart plug with the ability to switch on/off devices, and monitor power, reactive power, voltage and current. It also included four sensors for temperature, humidity, outside temperature and a door opener detector.

EnAPlug implementation, a custom multi-sensor smart plug by Gomes et al. ( 2017 )

5.1.2 Commercial sensors

Many companies develop smart plugs that are used in energy management systems for environmental sensing, containing usually multiple sensors on a single device. These devices are utilized in smart homes and buildings and are easier to be used by the average user. Furthermore, researchers are selecting these devices when performing large scale experiments to save time from building custom sensor boards.

In their work, Gomes et al. ( 2018 ) used the following smart plugs with metering abilities and on/off control: the DSP-W125 by D-Link, the SP-2101W by Edimax and the TP-link HS110. The DSP plug has also the ability to monitor temperature. Papaioannou et al. ( 2018 ) employed Fibaro 4-in-1 sensor (Fig.  5 ) at the site of the experiment to receive data about temperature, humidity, luminosity, motion and presence simultaneously. Schweizer et al. ( 2015 ) used digitalSTROM systems which were acting as power meters communicating with other nodes inside a smart environment.

Fibaro 4-in-1 sensor

Popa et al. ( 2019 ) utilized three multi-sensors Aeon Gen5 to detect movement, read temperature, luminance and relative humidity values. Furthermore, they used a smart plug Aeon Smart Switch 6 to control devices and measure instant consumed power and energy and also provide with power consumption graphs for appliances. Last, another Aeon Gen6 multi-sensor was installed that could also provide ultraviolet light sensing data and also home energy meter was installed in the fuse box to measure instant consumption and energy for the entire home without noise.

5.2 Actuators

5.2.1 direct control.

Actuators are the components of an IEMS that execute decisions and commands in order to perform actions so as to optimize power consumption. There are two possible ways to interact with the devices and appliances. The first one is a set of electronic actuators. Actuators are electrical components that interact with the appliances following the decisions of the process engine after the optimization is performed. All systems with the ability to modify their environment using actuators are called Direct Control IEMS . This term encloses every system able to process data, take decisions and execute them on its own, without the intervention of an human being.

Elettra was an innovative system proposed by Cristani et al. ( 2014 , 2015 ), allowing users to monitor their power consumption. It incorporated AmI techniques and algorithms to successfully measure and forecast consumptions of devices providing also direct control to sockets using smart plugs and sensors. Stavropoulos et al. ( 2014 ) proposed the framework Smart IHU, which was deployed at the International Hellenic University (IHU), an application with two components, a Manager and a Rule app. Their actuation infrastructure implementation had custom sensor boards and Z-Wave devices providing automations based on preferable statistics selected by the users.

Chojecki et al. ( 2020 ) implemented an energy management system in a smart meter device. They incorporated a fuzzy logic controller to perform automated actions on the appliances which were divided into two groups, a group of low power devices such as, consumer electronics and multimedia equipment, and a group of medium and high power devices such as, HVAC, water heaters and washing machines.

Another platform with direct controls was implemented by Luo et al. ( 2019 ). Their system was designed to minimize the costs per day of a home by optimally scheduling operations. To achieve this, it included controllable household appliances (smart devices) such as pool pump, dish washer, washing machine, clothes dryer, coffee machine, dehumidifier and bread maker. The users were selecting preferred time range for operations and were providing information about their lifestyle.

ReViCEE prototype by Kar et al. ( 2019 )

5.2.2 Indirect control

Some recent approaches put humans in the position of the actuator forming human-in-the-loop architectures. Their purpose is to change the behavior of the end-users to stop energy wasting habits. In order to accomplish that, they use recommendations engines to send suggestions and advice through interfaces in order to motivate people to act optimally. These systems can also be extended to propose replacements of inefficient devices and appliances that waste energy (Leitao et al. 2020 ). They are called Indirect Control IEMS.

There are different types of recommendations. The most typical of them are the personal resources recommendations which advise occupants to turn the lights or the HVAC appliances off in empty rooms or shut down idle devices such as, computers and printers. However, three more types of recommendations have been proposed by Wei et al. ( 2020 ). Move recommendations encourage occupants to change their working/living space to reduce services’ requirements. Schedule change recommendations are extensions of move recommendations aiming to shift the period of time an occupant spends within a space and not the duration. Finally, coerce recommendations suggest to the building managers when to force people to evacuate rooms if the occupancy is small in relation to the size of the space.

Alsalemi et al. ( 2019 ); Sardianos et al. ( 2020a , b ) designed (EM) \(^3\) , a framework aiming at occupants’ behavioral change. Using recommendations from its engine, REHAB-C, the human actuator is getting trained by repetition to behave efficiently. ReViCEE by Kar et al. ( 2019 ) follows the same logic by predicting energy consumption ratings and offering personalized recommendations, stimulating user-engagement towards energy conservation and sustainability. ReViCEE’s prototype implementation is show in Fig.  6 .

Popa et al. ( 2019 ) designed a modular platform named SHE (Smart Home Environment). The on-premises control was executed using some Z-Wave controllers, TKB Wall dimmer to control dimmable lights and the Aeon Z-Sticks, Gen-5 and S2, each designed to function on a specific location (US or Europe) depending on the allowed radio frequency for such devices. SHE was advising inhabitants about how they can improve their lifestyle and reduce the costs of energy consumption. Therefore, it provided notifications on a mobile device to motivate them to remotely turn on and off services.

García et al. ( 2013 ) used the framework CAFCLA to develop a recommendation system for usage in homes to promote efficient energy usage. The system was identifying behavioral patterns and along with CAFCLA’s real-time localization system and wireless sensor network it was used to provide personalized recommendations(García et al. 2017 ). KNOTES was a system developed by Shigeyoshi et al. ( 2011 ) that was proposing to the users how to change their life style using notifications, in order to save energy. The system was taking their personal data such as consumption, owned appliances, percentage of advice acceptance and evaluation history to find appropriate suggestions. Gamified approaches seemed also promising in terms of indirect control, especially in education facilities, due to the increased user engagement they provide, through an achievement system with rewards and leaderboards Papaioannou et al. ( 2018 , 2017 ).

5.3 Processing engine

Processing engine of an IEMS is designed to optimize the energy usage on each compartment of a smart environment and manage the actions that have to be performed. After years of research on this field, different techniques have been developed. The majority of the state-of-the-art systems employ Rule Engines, Data and Pattern mining algorithms, Machine Learning and Deep Learning models

5.3.1 Rule engine

The most frequently encountered technique on IEMSs is Rule Engines . Cuffaro et al. ( 2017 ) introduced a general-purpose Rule Engine that pushes notifications or reports to the end-users based on a resource graph model. In (EM) \(^3\) (Sardianos et al. 2020a ) a goal-based context-aware rule-based system (REHAB-C) was implemented with a rule mining algorithm, a process responsible to gather data about frequency of users actions. Papaioannou et al. ( 2018 ), developed an event-driven rule process on a gamified system aiming to reduce energy-wasting behaviors where each challenge assigned to the end-users is represented by a specific rule. Stavropoulos et al. ( 2014 , 2015 ), implemented a Rule App in their application in the form of a Hybrid Intelligent Agent. The agent had two interchangeable layers, the deliberative and the reactive. The reactive layer applies and maintains all energy-saving policies, while the deliberative layer incorporates a reasoner, based on defeasible logic to manage conflicting rules, responsible to optimize energy consumption while maintaining users’ comfort. A similar rule-based architecture using defeasible logic was also implemented by Cristani et al. ( 2016 ). Chojecki et al. ( 2020 ) designed a system that combined a rule-based implementation along with a fuzzy logic algorithm, incorporated on a smart meter to perform direct control. Last, Papaioannou et al. ( 2018 ) proposed an event-based rule engine to change energy waste behaviors in public buildings.

5.3.2 Machine learning and data mining

Smart environments produce a lot of data by the IoT components. Even though this data is processed in order to control the environment remotely, until recently they were rarely used from the system to train the models and achieve autonomy. Meurer et al. ( 2018 ), designed a system that takes advantage of contextual meta-data that originate from smart devices, using extra-trees classifiers, a technique that combines machine learning and data mining. That way, the dimensionality of the produced data was reduced, without loss of important features. Subsequently, an artificial neural network was trained to complete a context-aware engine, with a continuous learning capability based on feedback from the end-users.

Data mining techniques are also used to monitor inhabitants’ usage patterns. Schweizer et al. ( 2015 ), proposed a sequential pattern mining algorithm aiming on smart environments that predict future needs of their inhabitants. Thus, the system could avoid actions that lead to a comfort decrease.

Another system for energy management based on mining algorithms was developed by Dahihande et al. ( 2020 ). Their system provided personalized recommendations about turning on and off appliances at specific timestamps based on household profiles produced by association rule mining apporaches, such as Apriori and FP-Growth and also sequential pattern mining apporaches like RuleGrowth, TRuleGrowth, CMRules, ERMiner and CMDeo from a library created by Fournier-Viger et al. ( 2014 ).

5.3.3 Deep learning

On a smart environment where automated actions must be performed, human activities must be monitored. Recognizing patterns of a room occupant will provide necessary information to the back-end system, leading in more effective predictions. Deep learning techniques such as, convolutional and recurrent neural networks, showed great performance compared to others on human activity recognition Lentzas et al. ( 2019 ); Lentzas and Vrakas ( 2020 ). All this information combined with specific sensor measurements can also grant a context model. Using these models, anomalies and outliers can be detected which would affect energy consumption. Moreover, on more sophisticated systems, using all above can lead to residents’ identification. That way the system can initialize optimizations and actions based on resident’s profile. The aforementioned actions require complicated calculations, therefore Deep Neural Networks were employed (Popa et al. 2019 ).

The problem of the efficient energy consumption consists of two sub-problems, the non-intrusive load monitoring (NILM) and the energy load forecasting (ELF), which were resolved using deep learning models (Popa et al. 2019 ). NILM is a method used to monitor the energy profile of an environment and extract information about appliances consumption by disaggregating the total power consumption (Nalmpantis and Vrakas 2019 ). On the other hand, ELF is used to forecast patterns on energy consumption and detect anomalies that might increase energy consumption (Popa et al. 2019 ).

Another deep learning method used for energy saving is the deep reinforcement learning (DRL). Wei et al. ( 2020 ) used a DFL agent, trained along with the end-users’ decisions. For each successful reduction of consumption, the agent received a reward aimed at maximizing the amount of energy saved. Lissa et al. ( 2021 ) developed such a model, based on Markov Decision Processes (MDP) to control the temperature of domestic hot water. Their goal was to reduce the consumption by optimizing the usage of energy produced by photo-voltaic panels. Yu et al. ( 2019 ) suggested also a model using MDP to schedule optimally HVAC appliances and the energy storage system of a smart home. Finally, Shuvo and Yilmaz ( 2022 ), proposed a DFL model that incorporated human feedback in the objective function and human activity data in the reinforcement learning part of it to enhance optimization of energy.

5.4 User interface

IEM systems include necessarily a User Interface (UI) to allow interaction between them and the users. First of all, UI displays information about total power consumption or consumption per appliance. Secondly, it provides a mean for indirect or direct control of the devices in a smart space. Moreover, it is the only way for the users to change comfort parameters in direct control systems, schedule functions and set rules. Finally, the interface platform sends notifications stimulating recommended behaviors and receive feedback.

Nowadays, interfaces used by smart systems range from simple command-line environments, SMS texts to smartphone and smartwatch applications. There are also differences on the approaches of a user interface, meaning that, it could be a simple one just for system manipulation, or a complicated gamified environment, especially in systems aimed at behavioral changes.

5.4.1 Monitoring and management applications

A standard characteristic on every user interface application is the monitoring component. It usually consists of statistical graphs about consumptions or expenses. Zacharioudakis et al. ( 2017 ) designed a visualized performance graph of a building allowed the users to compare measurements from two different time periods. Moreover, the interface was used to provide alerts if outliers were detected. A simple monitoring agent was also introduced by González-Briones et al. ( 2018 ) providing statistical data about hourly consumptions and the ability to compare different days’ consumptions while it displayed estimated costs for the total kWh consumed and calculated CO2 emissions amount for a month. Another example of a monitoring application is shown in Fig.  7 , implemented by Sayed et al. ( 2021 ) for an intelligent edge-based recommendation system.

Monitoring application

A different approach in monitoring was given by Stavropoulos et al. ( 2014 ), where every room had its own section at the implemented Rule App, displaying statistics such as current power consumption, temperature, humidity, luminance and CO2 levels, and an indication about motion detection. However, their implementation allowed direct manipulation of these variables so that the agent could adjust comfort levels. Stavropoulos et al. ( 2015 ), a new agent with a GUI was introduced allowing the management of the rule engine through a user-friendly interface. In Figs. 8 , 9 and 10 , we present the user interface that allowed users of Smart IHU (Stavropoulos et al. 2014 ) to select preferred statistics, desired rule sets and monitor the environmental conditions. A similar approach was implemented also by Cristani et al. ( 2014 ), Tomazzoli et al. ( 2020 ) with three main menu choices, allowing manual activation of physical devices, setting of rules, actions and scenarios and also measurement of energy consumption and displaying along with real-time and stored data.

Alsalemi et al. ( 2019 ) implemented (EM) \(^3\) , a system with an end-user web application contained on-line daily consumption monitoring, displaying also indoor and outdoor levels of temperature and humidity, providing at the same time recommendations about energy efficiency. Additionally it had also a control menu with switches to modify devices’ activities. A similar user interface was prototyped also by Dahihande et al. ( 2020 ).

Smart IHU Manager App

Smart IHU Rule App

Smart IHU UI to set desired rule sets

5.4.2 Smart devices notifications

Many IEMS are using smartphones and smartwatches as their user interface. Mobile devices offer the convenience of monitoring and managing consumption on the go. Sardianos et al. ( 2020b ) and Schweizer et al. ( 2015 ), used the default text message services in order to push notifications with recommendations expecting feedback from the user to proceed in further actions.

Wei et al. ( 2017 ) proposed a real time energy usage tracking software, along with a web app, two mobile applications for iOS and Android devices and also one for Android wearables. The main dashboard of the system was in a more compressed form in the smartphone app, whereas the smartwatch app displayed only energy footprint breakdown and notifications when alarms were triggered. In Fig.  11 we show the user interface of the ePrints prototype.

ePrints apps by Wei et al. ( 2017 )

5.4.3 Serious games: gamified approaches

For systems that aim at behavioral changes, gamified interfaces and applications seem to have impact. Papaioannou et al. ( 2018 ), Papaioannou et al. ( 2017 ) proposed a gamified approach was introduced based on challenges for public buildings. End-users had a mobile application plus an NFC chip installed on their smartphones that showed available challenges, e.g. use staircases instead of the elevator or turn off your PC before leaving your office, and rewarded points in case of completion. The user interface contained also a leaderboard, where users were competing each other (Fig.  12 ).

Charged gamified application by Papaioannou et al. ( 2017 )

6 Discussion

In our survey, we elaborated over the topic of Intelligent Energy Management Systems. Our goal in this work was to report state-of-the-art approaches of the area (Table  3 ). However, our work differs from previous surveys because it merges smart automation and recommendation systems under the umbrella term of IEM systems, considering them as two individual sub-classes, whereas in current literature, most articles about Home/Building Energy Management Systems (HEMS/BEMS) refer to systems with incorporated automations.

In this article, first, we referred to the major influential factors that increase energy consumption and wastage, and also presented some general approaches that should be followed in order to pursue energy efficiency. Second, we provided an IEMS architecture overview, based on some functional and non-functional requirements which arise from the aforementioned influential factors and discussed about the state of the art of these systems. Third, we showed that each IEMS can be classified as a Direct or Indirect Control IEMS based on the type of actuation it incorporates. When a system contains automation mechanics and the end-users choose only environmental preferences, the system is controlling the environment directly. On the contrary, when the end-users receive recommendations to perform actions, the performed control is indirect. More recent studies proved that recommendations systems provide improvements in terms of energy savings. For this reason, we classified automations and recommendation systems as Direct and Indirect Control IEMS, respectively, a novel classification that addresses the lack of a unified structure and helps new researchers to obtain a more accurate overview of the field. Following, we will discuss about the advantages of each IEMS control type (Table  4 ), as well as their major open issues, providing also some future research orientations.

6.1 Advantages of direct control IEMS

Direct control IEM systems have the ability to automate procedures and actions. Their major advantage is that the occupants of a smart environment are not obliged to alter their routine. Once preferences are set, the system receives input data, optimizes and sends action commands to all required appliances, without the need for further interaction. For example, when an occupant leaves a room and no human presence is detected, the lights can be turned off. Similarly, the TV can automatically be turned off if the room is empty.

Another argument in favour of direct control is the plan and scheduling ability, allowing users to set long-term ecological or economical goals on the system. Moreover, scheduling techniques can offer consumption shifting on various operations to find optimal time frames in order to reduce energy demand and cost.

Furthermore, smart environments with automations can protect occupants from emergency situations that can occur, e.g. an excessive power consumption caused by electricity “leakage” from a faulty appliance. A leaky device is wasting energy and is also hazardous for the occupants because it can cause accidents, such as fire or electrocution. Automated actuators have the ability to turn off appliances when necessary, reducing the energy wastage and offering also a safer environment.

Finally, fully automated IEMS can be applied in households where handicapped or elderly people live. A properly designed user interface along with ambient assisted living devices, such as Amazon’s Alexa, which support voice commands, will allow these people to modify easily the comfort parameters of their environment and the energy consumption will remain optimized.

6.2 Advantages of indirect control IEMS

The application of recommendation modules in IEMS is a very promising field of research. As it was mentioned in chapter 4, occupants’ behavior is one major influential factor of inefficient energy consumption. Therefore, recommendation systems are useful tools to confront this issue. There is one significant advantage towards these systems. When the occupants follow recommendations for a long time, they acquire an eco-friendlier mindset. Thus, people trained from a home recommendation system will also apply the same aware behavior on every other aspect of their life, e.g. their workplace. Therefore, indirect control will eventually have wider impact.

A major difference from direct control systems is the lack of actuation infrastructure, which provides three important advantages. Firstly, these systems are cheaper and easier to buy. Deducting the cost of smart devices and appliances from the total cost of an IEMS, these systems become more affordable. Secondly, a system with less electrical parts connected to the internet, is less vulnerable to cyberattacks. Even if a false data injection attack is performed on them, the worst case scenario is a system that produces irrational recommendations. Moreover, these systems can also run using a local area network or a Bluetooth area network, which are more secure. Thirdly, the architecture of indirect control systems allows non-controllable appliances to take part into the optimization procedures without any modification on their hardware.

Last, an additional feature of these systems is that it is possible to extend their design so that they can help consumers acquire appliances, based on input data from existing appliances in a household, power consumption of available models on the market, their price, manufacturer and other specifications (Himeur et al. 2021 ).

6.3 Open issues

There has been great progress in the development of energy management systems, both towards direct and indirect control. However, various issues and limitations hinder the wide installation of such systems in commercial, residential and educational buildings. We will discuss the most important problematic aspects of each class.

6.3.1 Security

Direct control IEMS convert conventional buildings into smart ones. Therefore, they can be part of the demand-side of a smart grid, one of the most complex cyber-physical systems. According to He and Yan ( 2016 ), every infrastructure based on cyber-physical systems is vulnerable to various types of attacks.

False Data Injection (FDI) is the most frequent type of attack. Sethi et al. ( 2020 ) proved that these attacks affect electricity bills and load consumption drastically, proposing afterwards a resilient scheduling algorithm to overcome these effects. Furthermore, Dayaratne et al. ( 2019 ) showed that small fluctuations in energy demand from FDI attacks significantly increased the unit price and provided financial benefits to the attacker. Because of the nature of the demand response scheme, these situations lead to inefficient energy consumptions by the system, increasing the energy footprint.

Additionally, another type of attack is the Denial of Service (DoS) attack. Yi et al. ( 2016a ) revealed a vulnerability of metering infrastructure using “Puppet Attack”, a DoS attack that exhausts the communication bandwidth, proposing also detection and prevention mechanisms. Moreover, Yi et al. ( 2016b ) demonstrated that some DoS attacks can cause disruption in the whole smart grid. However, they proposed an algorithmic solution to isolate the attacked nodes to continue data transmission.

Other types of attacks are, Control Signal Adulteration (Esfahani et al. 2010 ), which affects the automatic generation controller that regulates the frequency and power exchange between controlled areas and Information Leakage (Sankar et al. 2012 ). Data leakage of smart metering data can lead malicious user to detect the absence of occupants, a sensitive information that can offer chances for attacks on households.

He and Yan ( 2016 ) provided a classification of the attacks based on the part of the grid they occur.

Generation systems attacks Attacks against power generation and power lines of the smart grid damaging the balance between generation and supply.

Transmission systems attacks Attacks aiming to damage and interrupt the delivery of the generated energy through power stations and lines. These attacks can be classified as (a) Interdiction attacks and (b) Complex network attacks. (He and Yan 2016 )

End-user attacks Attacks on IoT devices and appliances at the end-user side, i.e. smart devices, appliances and electrical actuators. These attacks are serious, according to Pearson ( 2011 ), because in smart metering and monitoring devices there are stored private information about users, such as user’s activities, consumption and idle time and when user’s location is empty or not.

Electricity market attacks Attacks that exploit vulnerabilities in the transmission management that affect the price of the electricity. That way, they make illegal profits and cause congestion to the power lines.

6.3.2 Cold start problem and data sparsity

Cold Start Problem (CSP) refers to the lack of initial data on a recommendation system. This issue occurs for various reasons, mostly in collaborative filtering models. Content-based and knowledge-based systems tend to be more robust (Aggarwal 2016 ).

According to Bobadilla et al. ( 2012 ), there are three cases that cause CSP. First, the new community problem refers to the lack of ratings from the recommendation database. This usually occurs when there are not enough users to rate or vote for the proposed advice, therefore the precision of the recommendation can’t be calculated. Second, the new item problem. The new item problem occurs when new actions or recommendations are imported in the database of the system. These recommendations contain no rating, therefore it is rare for them to be chosen. However, if a recommendation remains unnoticed for a long time, it acts like it doesn’t exist at all, even though it could be useful. Finally, the greatest CS problem is the new user problem. Someone who uses the system for the first time has zero votes on contained recommendation. The filtering methods of the system have no prior information about the new user and no history of ratings to calculate a “neighborhood” of appropriate recommendations (Son 2016 ). Therefore, the performance of the system is negatively affected because it cannot produce meaningful recommendations (Safoury and Salah 2013 ).

Some recommendation systems are based in collaborative filtering (CF), which can be viewed as a classification and regression generalization (Aggarwal 2016 ) and it is the most mature and commonly implemented technique (Jain et al. 2020 ). This technique is making predictions about users’ preferences based on data collected from users with similar profiles. Ratings between users and items are stored in a matrix which is sparse, sometimes up to 99% (Guo 2012 ). This problem is known as Data Sparsity in CF recommendation systems.

Sparsity exists when there is lack of knowledge about new users who start using the system and also, because they ignore the evaluation process after a recommendation. Moreover, new users rarely report their feedback on received suggestions. In CF systems, data sparsity is what causes the cold start problem (Guo 2012 ).

To tackle these problems, some approaches have been proposed in the literature. Himeur et al. ( 2021 ) proposed that some explicitly stated preferences by the newcomers can be used as metrics to include them in some preexisting cluster of older users. Lika et al. ( 2014 ) proposed a model consisting of classification algorithms and similarity techniques that retrieved optimized recommendations. Furthermore, Liu et al. ( 2014 ) mentioned that promoting new items into new users is not very effective, but promoting new items into less active users showed some performance improvement. Jain et al. ( 2020 ) suggested an algorithm based on Sequence and Set Similarity Measure that utilized Singular Value Decomposition removes sparsity from the user-item-ratings matrix and Natarajan et al. ( 2020 ) proposed two methods to overcome sparsity using linked open data from the “DBpedia” knowledge base to create a recommendation system using Matrix Factorization.

6.4 Comparison of direct and indirect control IEMS

Every developed IEMS design has advantages and disadvantages that occur from the actuation type it incorporates. Both direct and indirect control can offer benefits to the end users, however, the installation environment that is most suitable for each class, varies. In Tables  5 and 6 we compare these systems with respect to the following characteristics: cost, daily life intrusiveness, security, comfort, efficiency, ease of installation, accessibility, radius of influence, planning and scheduling features.

In terms of cost, IEMS with automations require a greater number of equipment to control all necessary devices, appliances and services of the installation environment. Each one of these needs an exclusive actuator, which most of the times increases the cost considerably. Because of that, systems which produce recommendations are more affordable options for daily user.

Regarding the intrusiveness of such a system in users daily life, indirect control tend to be more intrusive. People must take actions every time a new recommendation arrives in order to get the best out of their system’s effectiveness. However, this means that the system is highly intrusive in their life, so in this case a system with automations is a better choice.

Security issues are a main concern for all users especially in recent years. Both direct and indirect control systems incorporate a sensing infrastructure, i.e. smart meters and the rest of the sensors that measure environmental variables. Data collected from these devices are extremely sensitive and multiple security measures should be taken into account. As mentioned earlier, absence detection from residential meter data is an extremely dangerous situation, if this information reaches in dangerous people. Nevertheless, the fact that indirect control systems do not incorporate digital actuators make them more resilient to cyber-attacks and no-one can interfere with the functionality of a device or a service.

In every aspect of their life, people are looking for conditions that increase their comfort, whether at home, at work or out for shopping. An energy management system tends to interfere with peoples comfort in all environments in order to achieve energy savings. A system with automations allows the end users to keep their daily routine intact, requiring only an initial setup and minor adjustments during its use. On the other hand, recommendation modules use humans as actuators, therefore their comfort is more affected.

The fact that indirect control systems have a human-in-the-loop design, decreases also their final efficiency. According to the aforementioned studies, user engagement is a major influential factor for the decrease of their efficiency, which makes systems with automations a better choice from this viewpoint.

In terms of ease of installation, direct control systems consist of many more different devices, such as metering, actuators and sensors rather than indirect control systems. On the contrary, an indirect control system can function even with one metering device in the power supply line and one application on a smartphone. Thus, these systems are easier to be installed and they usually are not requiring technical assistance.

The aspect of accessibility is rarely being considered during the design of an IEMS. Systems with automations are more suitable for environments where elderly or handicapped people live or work because usually it is more difficult for them to control the functionality of the services that need to be managed.

There are cases in our daily life where electricity can cause dangerous incidents. A typical example is the malfunction of an appliance. During the monitoring of the energy consumption an appliance can appear to consume energy, even though occupants know that it is not functioning. That might be a case of energy leakage from an old or faulty device. A direct control system can automatically turn of the power supply of this apparatus and protect users from getting electrocuted.

Another aspect IEMS is the planning and scheduling of energy saving actions. Direct control IEMS are capable of lowering HVAC temperature when temperature and humidity reach certain levels autonomously. Therefore, systems with automations have an advantage over recommendation IEMS.

Finally, energy awareness is another aspect of these systems. Usually, designers and developers of IEMS are focused only to achieve energy consumption reduction on the installation environment. However, by focusing on users behavioral management through recommendations systems, people can become more energy aware. When they learn to follow a specific pattern of actions at home, they can act similarly in any other place. This implies that IEMS with incorporated recommendation modules can have a long term effect in a broader set of environments.

7 Future research

7.1 research gap.

This subsection discusses some insights about the research gap in the field of Intelligent Energy Management Systems.

In order to better understand some topics that are related to IEMS, more reviews focused are necessary. First, we found out that Machine and Deep learning techniques are, in most cases, the state-of-the-artapproaches towards the solution of a problem. IEMS are IoT based systems which produce high volume of data, therefore fast and accurate processing is required. Furthermore, these data are, in some cases, collected and processed in real time and decisions must be taken. Therefore, an extensive survey on Machine and Deep learning techniques with applications in IEMS needs to be done so the research community has a better perspective about the limitations of these techniques and how they can be overcame.

Second, another important issue of the IEMS is security . As previously mentioned, security issues occur in IEMS and can be dangerous both for the users and for the energy providers and producers. Incidents such as false data injection attacks or DoS attacks are serious and must be taken into consideration during the development. Until now, due to different standards and communication stacks involved in the IoT technologies, traditional measures against cyberattacks are not always applicable (Sicari et al. 2015 ). Threats like systems’ failure, smart meters’ data corruption, infection by malware, spoofing of usernames and addresses and unauthorized access, show the necessity for research towards threat and risk modelling, IoT forensics, intrusion detection and prevention techniques (Kitchin and Dodge 2019 ; Atlam and Wills 2020 ). Also, Energy Theft is another recent problem that is mostly important for the Distribution System Operators which are managing and distributing produced energy into customers. During our research, we concluded that there is a gap on the area of energy related cyber-security issues and we consider it of great importance for the evolution of IEMS.

Finally, another topic that requires to be studied are ways to increase user engagement in recommendation systems. Indirect control IEMS which involve recommendations through notifications systems seem to be very promising. However, no such system can guarantee that users will remain engaged into the suggested actions and that they will act respectively. Moreover, the fact that recommendations systems suffer from the cold-start problem decreases more the chances for engagement, thus the effectiveness of these systems drops compared to systems with automations. Because of that, we consider it necessary for a study to be carried out that will investigate all problematic aspects of behavioral modification systems, focusing on user engagement.

7.2 Research opportunities

The field of IEMS is still developing, therefore many aspects of these systems need to be improved. Regarding indirect control systems, explainability of recommendations remains an issue that needs further study. The lack of explainable recommendations lead users to ignore advice, reducing the effectiveness of a system and the trustworthiness of it (Zhang et al. 2020 ). Each recommendation should be accompanied by answers to the questions “why to perform an action” and “what the benefits are” (Himeur et al. 2021 ). In (EM) \(^3\) system, (Sardianos et al. 2020a ; Varlamis et al. 2022b ), each recommendation had a reasoning and a persuasion feature, which resulted in 20% increase in the acceptance ratio. Moreover, Wilkinson et al. ( 2021 ) presented that it is more effective to provide justifications on why an action should be performed rather than why not.

Another research opportunity is towards gamified frameworks for systems with recommendation modules. They are more engaging than conventional ones when they aim to change certain behaviors (Papaioannou et al. 2017 ). These approaches are preferred for school buildings, in order to improve energy awareness in students. An example by Mylonas et al. ( 2018 ), was implemented through the GAIA project, where IoT lab kits where used along with a serious game resulting in acceptance of energy aware behaviors.

Reinforcement learning is a popular AI technique to develop smart frameworks that perform control actions. These frameworks can be incorporated in smart home energy management devices to offer higher levels of saved energy. According to Mason and Grijalva ( 2019 ), RL frameworks are capable of learning more complex policies than other neural networks implementations and because of the always increasing data volume, these frameworks will become eventually a necessity. Another opportunity is to develop multi-tasking RL frameworks that will be trained to follow different policies. Until now, many multi-objective optimization methods exists, but none of them utilizes Reinforcement learning. Furthermore, RL control frameworks are not tested yet in extreme condition changes. Minor changes at the environmental conditions have been handled until know (Mason and Grijalva 2019 ) but cases like extreme weather conditions, increase or decrease of occupants, solar panel failure, etc, have not been tested yet. Finally, RL apporaches in IEMS are relatively new (Al-Ani and Das 2022 ). Another research direction would be to perform a comparison between these new approaches with the traditional ones such as rule-based frameworks and other neural network based implementations.

Regarding privacy issues, protecting sensitive users’ information is essential, therefore some research directions could be towards the improvement of resilience of IEMS to cyberattacks by developing frameworks with less vulnerabilities. Until now, due to different standards and communication stacks involved in the IoT technologies, traditional measure against cyberattacks are not always applicable (Sicari et al. 2015 ). Threats like systems’ failure, smart meters’ data corruption, infection by malware, spoofing of usernames and addresses and unauthorized access, show the necessity for research towards threat and risk modelling, IoT forensics, intrusion detection and prevention techniques (Kitchin and Dodge 2019 ; Atlam and Wills 2020 ).

8 Conclusions

This paper has reviewed state-of-the-art approaches of Intelligent Energy Management Systems. Within the area of energy efficiency, IEMS are considered as a way to confront climate change. These systems follow a similar architecture consisting of four components: Sensors, Actuators, Processing Engine and a User Interface.

There are two types of sensing infrastructures in the literature, custom-made and commercial. Researchers choose their preferred type based on the goals and the scale of the application. In large-scale projects, commercial sensors provided convenience and sometimes a unified communication protocol, whereas custom made sensors were preferred for small-scale projects because they could embed more components.

Moreover, this review proposed a novel classification, based on the type of actuation. IEMS can be divided into direct and indirect control systems, depending on who is performing the actions to optimize energy consumption. IEMS with incorporated automation modules are controlling the consumption directly, whereas IEMS aimed at behavioral changes suggest actions and allow the users to decide about actions. Direct control provides convenience through automations and also safety in case of emergency situations. However, improving energy awareness through indirect control can bring about changes in larger scale.

Nevertheless, all of these systems have weak points and vulnerabilities. Systems with automations are mostly vulnerable against cyberattacks. False Data Injection attacks in such systems, can cause an increase of consumed energy. Systems with recommendations suffer from the Cold Start Problem which occurs when new users begin to use the system and when new actions are imported. These problems must be addressed to ensure the effectiveness of these applications.

Our intentions are to keep researching in the field of IEMS. We consider the area of Reinforcement Learning very promising for applications in energy management using direct control. Trying to overpass problems such as the slow training rate is one of our goals. Moreover, another research opportunity regarding the recommendations’ modules is to implement an application or a serious game that will drastically improve users’ engagement in order to create a more affordable and interesting way for people to become more energy aware.

To conclude, IEMS are going through a constant evolution. Direct control approaches seem like a better option for commercial buildings, where a large number of people is present. In that scale, recommendation systems are not very promising. On the contrary, indirect control seems an appropriate choice for educational buildings because eventually, they will increase the awareness of students and will provide long term advantages. Finally, for residential environments, systems with automations are currently more advanced, however, the installation of a complete smart home is still very expensive and unaffordable for the majority of households. Therefore, we suggest that more indirect control applications must be developed for domestic environments in the future.

Data availibility

Data sharing not applicable to this article as no datasets were generated or analysed during the current study

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This research has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH–CREATE–INNOVATE (project code:95699 - Energy Controlling Voice Enabled Intelligent Smart Home Ecosystem).

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Mischos, S., Dalagdi, E. & Vrakas, D. Intelligent energy management systems: a review. Artif Intell Rev 56 , 11635–11674 (2023). https://doi.org/10.1007/s10462-023-10441-3

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Phys. rev. research, meng lu, xuerui mao, luca brandt, and jian deng.

Understanding whether a droplet adheres to or detaches from a flexible filament upon axial impact is of significant interest, particularly in the context of raindrop impact on natural surfaces. This process involves dynamic buckling followed by mode coarsening, dissipating the initial droplet kinetic energy and converting the remaining into elastic energy of the filament. To elucidate this phenomenon, we construct two phase diagrams, one while fixing the filament height and the other the droplet diameter. Notably, we observe that the energy conversion rate is inversely proportional to a Cauchy number, defining the transition between attached and detached droplet in the filament length-falling height diagram. This enables us to derive an expression for the critical falling height, as a function of the filament parameters, accounting for the energy conversion rate, which emerges as a key factor for droplet detachment.

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Quasi-boson approximation yields accurate correlation energy in the 2D electron gas

Tobias m. r. wolf and chunli huang, phys. rev. research 6 , 033296 – published 13 september 2024.

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We report the successful adaptation of the quasi-boson approximation, a technique traditionally employed in nuclear physics, to the analysis of the two-dimensional electron gas. We show that the correlation energy estimated from this approximation agrees closely with the results obtained from quantum Monte Carlo simulations. Our methodology comprehensively incorporates the exchange self-energy, direct scattering, and exchange scattering for a particle-hole pair excited out of the mean-field ground state within the equation-of-motion framework. The linearization of the equation of motion leads to a generalized random phase approximation (gRPA) eigenvalue equation whose spectrum indicates that the plasmon dispersion remains unaffected by exchange effects, while the particle-hole continuum experiences a marked upward shift due to the exchange self-energy. Using the gRPA excitation spectrum, we calculate the zero-point energy of the quasi-boson Hamiltonian, thereby approximating the correlation energy of the original Hamiltonian. This research highlights the potential and effectiveness of applying the quasi-boson approximation to the gRPA spectrum, a fundamental technique in nuclear physics, to extended condensed matter systems.

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Correlation energy vs electron gas parameter r s in the 2DEG obtained in the QBA for the generalized RPA spectrum. The quasi-boson approximation yields results in good agreement with the QMC and the local-field method (STLS) over a wide range of r s . The QMC fit and STLS data are from Refs. [ 17 ] and [ 13 ], respectively. For QBA, we used a Yukawa screening wave vector of κ = 0.1 d k , where d k is the discretization.

Diagrammatic interpretation for the matrix elements of A ,   B in the excitation eigenequation for the 2DEG [cf. Eq. ( 9a )]. (a) Bare noninteracting propagator and self-consistent propagator accounting for Fock self-energy renormalization. (b) TDHF matrix elements with direct and exchange contributions to the scattering ( A ̃ ) and to the double-excitations ( B ). Note that for A , we only show the contributions A ̃ without the trivial kinetic term. Spin summation leads to factors of 2 in the direct diagrams.

Spectrum, plasmon dispersion, and many-body stability of the 2DEG. (a)–(d) Excitation spectra, over an extended range of q including 2 k F , obtained using the gRPA equations [cf. Eq. ( 9a )] for time-dependent Hartree (TDH) and time-dependent Hartree-Fock with exchange self-energy (TDHF). The color is the normalized density response residue R ̂ ν q = R ν q / ∑ ν R ν q [cf. Eq. ( 11 )]. (e)–(h) The corresponding charge susceptibilities for each scheme. Results are presented for electron gas density parameters r s = 1.0 , 4.0 . Notably, in the expanded q range, TDHF shows a tendency to forming charge-density waves, see main text. Here, we used d k = 0.08 k F ,   κ = 0.15 d k , and q ≥ 3 d k .

Long-wavelength spectrum and plasmon modes of the 2DEG. (a)–(f) Excitation spectra obtained as eigensolutions of the gRPA equations [cf. Eq. ( 9a )] for three different approximation schemes: TDH, TDHF, and time-dependent Hartree with exchange self-energy ( TDH * ). The color is the normalized density response residue R ̂ ν q = R ν q / ∑ ν R ν q [cf. Eq. ( 11 )]. (g)–(l) The corresponding charge susceptibilities for each scheme. All results are plotted for two electron gas density parameters, r s = 1.0 , 4.0 , as indicated to the left of each row. Color intensities in the spectra and susceptibilities represent the magnitude of the spectral weight R ̂ ν ( q ) and the imaginary part of the charge susceptibility χ ( q , ω ) , respectively. The plasmon modes for TDH and TDHF are comparable as a result of a conserving approximation. Note that TDH * gives unphysical results, see main text. Here, we used d k = 0.03 k F ,   κ = 0.15 d k , and q ≥ 3 d k .

Comparison of the correlation function Re χ ( q , ω ) for momenta q and real frequencies ω of the 2DEG at density parameters r s = 1.0 (top) and r s = 4 (bottom) and different approximation schemes: (a) and (b) TDH, (c) and (d) TDHF, and (e) and (f) TDH * . Here, we used d k = 0.08 k F ,   κ = 0.15 d k , and q ≥ 3 d k .

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