Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review
Name
AI_EnergyDemandResponse_RSER.pdf
Size
2.59 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Antonopoulos, Ioannis
Robu, Valentin
Couraud, Benoit
Kirli, Desen
Norbu, Sonam
Kiprakis, Aristides
Flynn, David
Elizondo-Gonzalez, Sergio
Wattam, Steve
Date Issued
June 2020
Journal
Renewable and Sustainable Energy Reviews
Publisher
Elsevier BV
Citation
Antonopoulos, Ioannis et al. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review." Renewable and Sustainable Energy Reviews 130 (September 2020): 109899 © 2020 The Authors
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Center for Collective Intelligence
Sloan School of Management
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1016/j.rser.2020.109899
