Evaluating Reinforcement Learning Algorithms in Residential Energy Saving and Comfort Management
Author:
Lazaridis Charalampos Rafail12ORCID, Michailidis Iakovos1, Karatzinis Georgios12, Michailidis Panagiotis12ORCID, Kosmatopoulos Elias12
Affiliation:
1. Department of Electrical and Computer Engineering, Democritus University of Thrace, 67100 Xanthi, Greece 2. Information Technologies Institute (I.T.I.), Centre for Research & Technology—Hellas (CE.R.T.H.), 57001 Thessaloniki, Greece
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.
Funder
European Union’s Horizon Europe programme European Union
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction
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