The DOI matches its Crossref record; 366 of the 366 references it cites were found in a registry or an index.
Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review
Open accessAbstract
Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time decisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and preferences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area.
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“AIoT has been deployed in multiple ways to help users efficiently manage energy to reduce cost as well as energy producers optimize their equipment for better service delivery [219].”
From Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review (2024) · cites this paper as [219]
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“Previous work has used RL and dynamic programming to set real-time electricity prices [30,90] and more broadly for power market design [30,881].”
From Tackling climate change with machine learning (2023) · cites this paper as [30]
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“In this case, ML can help both to design real-time prices and to respond to these prices.”
From Tackling climate change with machine learning (2023) · cites this paper as [30]
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Cites
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Kennedy, J., & Eberhart, R. (2002) · Proceedings of ICNN'95 - International Conference on Neural Networks · doi:10.1109/icnn.1995.488968 · Cited by 58,192
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053
Adaptation in Natural and Artificial Systems
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,340
Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
The Elements of Statistical Learning
Hastie, T., Tibshirani, R., & Friedman, J. (2009) · Springer Series in Statistics · doi:10.1007/978-0-387-84858-7 · Cited by 25,970
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,268
Artificial intelligence: a modern approach
Kumar, D. A., Balasubramanian, S., Byeon, D. H., & Manerkar, P. G. V. (1995) · Choice Reviews Online · doi:10.5860/choice.33-1577 · Cited by 22,191
Research works printed but not linked: 4
- Watkins, 1992, Q-learning, Machine Learning, 8, 279
- Lee, 2014, Forecasting and modeling of electricity demand using NARX neural network in smart grid environment, International Information Institute (Tokyo). Information, 17, 6439
- La Poutré, 2012, Agent-mediated electronic negotiation (bargaining), vol. 5, 1
- Liu, 2013, An agent-based modelling for price-responsive demand simulation, vol. 1, 436
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Strielkowski, W., Vlasov, A., Selivanov, K., Muraviev, K., & Shakhnov, V. (2023) · Energies · doi:10.3390/en16104025 · Cited by 237
Tackling climate change with machine learning
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Karijadi, I., & Chou, S.-Y. (2022) · Energy and Buildings · doi:10.1016/j.enbuild.2022.111908 · Cited by 159
Zhu, H., Goh, H. H., Zhang, D., Ahmad, T., Liu, H., Wang, S., Li, S., Liu, T., Dai, H., & Wu, T. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2021.129809 · Cited by 131
More sustainable artificial intelligence systems through stakeholder involvement?
Kunkel, S., Schmelzle, F., Niehoff, S., & Beier, G. (2023) · GAIA - Ecological Perspectives for Science and Society · doi:10.14512/gaia.32.s1.10 · Cited by 20
Heidari Yazdi, S. S., Rahimi, T., Khadem Haghighian, S., Gharehpetian, G. B., & Bagheri, M. (2022) · Frontiers in Energy Research · doi:10.3389/fenrg.2022.920654 · Cited by 20
Fault Detection and Fault Diagnosis in Power System Using AI: A Review
Nasim, S. F., Aziz, S., Qaiser, A., Kulsoom, U., & Ahmed, S. (2024) · Sir Syed University Research Journal of Engineering & Technology · doi:10.33317/ssurj.598 · Cited by 17
Integrating active demand into the distribution system using metaheuristic techniques
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Metadata
- Authors
- Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S., & Wattam, S.
- Affiliations
- Heriot-Watt University (GB)
- University of Edinburgh (GB)
- Office of Gas and Electricity Markets (GB)
- Corresponding author
- Antonopoulos, Ioannis
- Venue
- Renewable and Sustainable Energy Reviews
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 130
- Pages
- 109899
- ISSN
- 1364-0321 (ISSN-L)
- OpenAlex
- W3034272367
- Cited by
- 656
- References
- 366
- Citations by year
- 2026: 104 · 2025: 126 · 2024: 135 · 2023: 113 · 2022: 101 · 2021: 72 · 2020: 5
- FWCI
- 3.61
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Renewable and Sustainable Energy Reviews (publishedVersion, cc-by)
- Centrum Wiskunde & Informatica (CWI), the national research institute for mathematics and computer science in the Netherlands (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- HAL (submittedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Funders
- Engineering and Physical Sciences Research Council
- Innovate UK
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Vehicles and Infrastructure
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Keywords
- demand response, artificial intelligence, machine learning, energy flexibility, dynamic pricing, consumer choice, consumer preferences, incentive design, energy system reliability
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