  Heating, Ventilation, and Air Conditioning (HVAC) is extremely
energy-consuming, accounting for 40% of total building energy consumption.
Therefore, it is crucial to design some energy-efficient building thermal
control policies which can reduce the energy consumption of HVAC while
maintaining the comfort of the occupants. However, implementing such a policy
is challenging, because it involves various influencing factors in a building
environment, which are usually hard to model and may be different from case to
case. To address this challenge, we propose a deep reinforcement learning based
framework for energy optimization and thermal comfort control in smart
buildings. We formulate the building thermal control as a cost-minimization
problem which jointly considers the energy consumption of HVAC and the thermal
comfort of the occupants. To solve the problem, we first adopt a deep neural
network based approach for predicting the occupants' thermal comfort, and then
adopt Deep Deterministic Policy Gradients (DDPG) for learning the thermal
control policy. To evaluate the performance, we implement a building thermal
control simulation system and evaluate the performance under various settings.
The experiment results show that our method can improve the thermal comfort
prediction accuracy, and reduce the energy consumption of HVAC while improving
the occupants' thermal comfort.
