Meta-Reinforcement Learning for Building Energy Management System
This work addresses the practical deployment challenge of energy management systems for building operators by improving learning efficiency, though it is incremental as it builds on existing meta-RL approaches.
The paper tackles the problem of slow adaptation in reinforcement learning-based energy management systems for buildings by introducing MetaEMS, a meta-reinforcement learning framework that transfers knowledge to new tasks, resulting in faster adaptation to unseen buildings and consistent outperformance of baseline methods.
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.