Applications of reinforcement learning in energy systems

A.T.D. Perera and Parameswaran Kamalaruban

Renewable and Sustainable Energy Reviews, 2021, vol. 137, issue C

Abstract: Energy systems undergo major transitions to facilitate the large-scale penetration of renewable energy technologies and improve efficiencies, leading to the integration of many sectors into the energy system domain. As the complexities in this domain increase, it becomes challenging to control energy flows using existing techniques based on physical models. Moreover, although data-driven models, such as reinforcement learning (RL), have gained considerable attention in many fields, a direct shift into RL is not feasible in the energy domain irrespective of the ongoing complexities. To this end, a top-down approach is used to understand this behavior by reviewing the current state of the art.

Keywords: Energy systems; Reinforcement learning; Renewable energy; Building energy; Machine learning (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (41)

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DOI: 10.1016/j.rser.2020.110618

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