Reinforcement Learning: Theory and Applications in HEMS

Omar Al-Ani and Sanjoy Das ()
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Omar Al-Ani: Electrical & Computer Engineering Department, Kansas State University, Manhattan, KS 66506, USA
Sanjoy Das: Electrical & Computer Engineering Department, Kansas State University, Manhattan, KS 66506, USA

Energies, 2022, vol. 15, issue 17, 1-37

Abstract: The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article. It surveys the use of RL in various home energy management system (HEMS) applications. There is a focus on deep neural network (DNN) models in RL. The article provides an overview of reinforcement learning. This is followed with discussions on state-of-the-art methods for value, policy, and actor–critic methods in deep reinforcement learning (DRL). In order to make the published literature in reinforcement learning more accessible to the HEMS community, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using standard machine learning terminology. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. It suggests that research in this direction is still in its infancy. Lastly, the article proposes four performance metrics to evaluate RL methods.

Keywords: home energy management systems (HEMS); reinforcement learning (RL); deep neural network (DNN); Q-value; policy gradient; natural gradient; actor–critic; residential; commercial; academic (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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