Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review. (September 2020)

• Record Type: Journal Article
• Authors: Antonopoulos, Ioannis; Robu, Valentin; Couraud, Benoit; Kirli, Desen; Norbu, Sonam; Kiprakis, Aristides; Flynn, David; Elizondo-Gonzalez, Sergio; Wattam, Steve
• Is Part Of: Renewable & sustainable energy reviews. Volume 130(2020)
• Publication Date: 2020-09
• Subjects: Artificial intelligence -- Machine learning -- Artificial neural networks -- Nature-inspired intelligence -- Multi-agent systems -- Demand response -- Power systems; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2020.109899
• Languages: English
• ISSNs: 1364-0321

Abstract

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. Highlights: Review of Artificial Intelligence/Machine Learning for energy demand-side response. Sub-areas of energy demand response for which AI/ML techniques have been used. Discussion of pros and cons of using specific AI/ML techniques in each sub-area. Insights into commercial initiatives/industrial R&D projects using AI techniques. Discussion of the field's evolution and potential future research paths.