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Building energy management is one of the core problems in modern power gridsto reduce energy consumption while ensuring occupants' comfort. However, thebuilding energy management system (BEMS) is now facing more challenges anduncertainties with the increasing penetration of renewables and complicatedinteractions between humans and buildings. Classical model predictive control(MPC) has shown its capacity to reduce building energy consumption, but itsuffers from labor-intensive modelling and complex on-line controloptimization. Recently, with the growing accessibility to the building controland automation data, data-driven solutions have attracted more researchinterest. This paper presents a compact review of the recent advances indata-driven MPC and reinforcement learning based control methods for BEMS. Themain challenges in these approaches and insights on the selection of a controlmethod are discussed.

Data-driven Model Predictive and Reinforcement Learning Based Control for Building Energy Management: a Survey