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The building sector has been recognized as one of the primary sectors forworldwide energy consumption. Improving the energy efficiency of the buildingsector can help reduce the operation cost and reduce the greenhouse gasemission. The energy management system (EMS) can monitor and control theoperations of built-in appliances in buildings, so an efficient EMS is ofcrucial importance to improve the building operation efficiency and maintainsafe operations. With the growing penetration of renewable energy andelectrical appliances, increasing attention has been paid to the development ofintelligent building EMS. Recently, reinforcement learning (RL) has beenapplied for building EMS and has shown promising potential. However, most ofthe current RL-based EMS solutions would need a large amount of data to learn areliable control policy, which limits the applicability of these solutions inthe real world. In this work, we propose MetaEMS, which can help achieve betterenergy management performance with the benefits of RL and meta-learning.Experiment results showcase that our proposed MetaEMS can adapt faster toenvironment changes and perform better in most situations compared with otherbaselines.

MetaEMS: A Meta Reinforcement Learning-based Control Framework for Building Energy Management System