Meta-Reinforcement Learning for Building Energy Management System
Abstract
The building sector is one of the largest contributors to global energy consumption. Improving its energy efficiency is essential for reducing operational costs and greenhouse gas emissions. Energy management systems (EMS) play a key role in monitoring and controlling building appliances efficiently and reliably. With the increasing integration of renewable energy, intelligent EMS solutions have received growing attention. Reinforcement learning (RL) has recently been explored for this purpose and shows strong potential. However, most RL-based EMS methods require a large number of training steps to learn effective control policies, especially when adapting to unseen buildings, which limits their practical deployment. This paper introduces MetaEMS, a meta-reinforcement learning framework for EMS. MetaEMS improves learning efficiency by transferring knowledge from previously solved tasks to new ones through group-level and building-level adaptation, enabling fast adaptation and effective control across diverse building environments. Experimental results demonstrate that MetaEMS adapts more rapidly to unseen buildings and consistently outperforms baseline methods across various scenarios.
Cite
@article{arxiv.2210.12590,
title = {Meta-Reinforcement Learning for Building Energy Management System},
author = {Huiliang Zhang and Di Wu and Arnaud Zinflou and Benoit Boulet},
journal= {arXiv preprint arXiv:2210.12590},
year = {2025}
}
Comments
arXiv admin note: text overlap with arXiv:1909.10165 by other authors