Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review

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…

• NC
  National Centre for Energy Systems Integration

  Award: EP/P001173/1
• EA
  Engineering and Physical Sciences Research Council

  Awards: 2123508, EP/P001173/1, EP/P001173/1, EP/R008655/1
• IU
  Innovate UK