  Building energy management is one of the core problems in modern power grids
to reduce energy consumption while ensuring occupants' comfort. However, the
building energy management system (BEMS) is now facing more challenges and
uncertainties with the increasing penetration of renewables and complicated
interactions between humans and buildings. Classical model predictive control
(MPC) has shown its capacity to reduce building energy consumption, but it
suffers from labor-intensive modelling and complex on-line control
optimization. Recently, with the growing accessibility to the building control
and automation data, data-driven solutions have attracted more research
interest. This paper presents a compact review of the recent advances in
data-driven MPC and reinforcement learning based control methods for BEMS. The
main challenges in these approaches and insights on the selection of a control
method are discussed.
