Evaluating Reinforcement Learning Algorithms in Residential Energy Saving and Comfort Management

Charalampos Rafail Lazaridis, Iakovos Michailidis (), Georgios Karatzinis, Panagiotis Michailidis and Elias Kosmatopoulos
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Charalampos Rafail Lazaridis: Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Iakovos Michailidis: Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Georgios Karatzinis: Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Panagiotis Michailidis: Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece
Elias Kosmatopoulos: Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece

Energies, 2024, vol. 17, issue 3, 1-33

Abstract: The challenge of maintaining optimal comfort in residents while minimizing energy consumption has long been a focal point for researchers and practitioners. As technology advances, reinforcement learning (RL)—a branch of machine learning where algorithms learn by interacting with the environment—has emerged as a prominent solution to this challenge. However, the modern literature exhibits a plethora of RL methodologies, rendering the selection of the most suitable one a significant challenge. This work focuses on evaluating various RL methodologies for saving energy while maintaining adequate comfort levels in a residential setting. Five prominent RL algorithms—Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Deep Q-Network (DQN), Advantage Actor-Critic (A2C), and Soft Actor-Critic (SAC)—are being thoroughly compared towards a baseline conventional control approach, exhibiting their potential to improve energy use while ensuring a comfortable living environment. The integrated comparison between the different RL methodologies emphasizes the subtle strengths and weaknesses of each algorithm, indicating that the best selection relies heavily on particular energy and comfort objectives.

Keywords: reinforcement learning; energy efficiency; thermal comfort; buildings; residents; Energym (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: 2024
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