  The building sector has been recognized as one of the primary sectors for
worldwide energy consumption. Improving the energy efficiency of the building
sector can help reduce the operation cost and reduce the greenhouse gas
emission. The energy management system (EMS) can monitor and control the
operations of built-in appliances in buildings, so an efficient EMS is of
crucial importance to improve the building operation efficiency and maintain
safe operations. With the growing penetration of renewable energy and
electrical appliances, increasing attention has been paid to the development of
intelligent building EMS. Recently, reinforcement learning (RL) has been
applied for building EMS and has shown promising potential. However, most of
the current RL-based EMS solutions would need a large amount of data to learn a
reliable control policy, which limits the applicability of these solutions in
the real world. In this work, we propose MetaEMS, which can help achieve better
energy management performance with the benefits of RL and meta-learning.
Experiment results showcase that our proposed MetaEMS can adapt faster to
environment changes and perform better in most situations compared with other
baselines.
