The area of building energy management has received a significant amount of interest in recent years. This area is concerned with combining advancements in sensor technologies, communications and advanced control algorithms to optimize energy utilization. Reinforcement learning is one of the most prominent machine learning algorithms used for control problems and has had many successful applications in the area of building energy management. This research gives a comprehensive review of the literature relating to the application of reinforcement learning to developing autonomous building energy management systems. The main direction for future research and challenges in reinforcement learning are also outlined.
@article{arxiv.1903.05196,
title = {A Review of Reinforcement Learning for Autonomous Building Energy Management},
author = {Karl Mason and Santiago Grijalva},
journal= {arXiv preprint arXiv:1903.05196},
year = {2019}
}