  In this paper, we conduct a set of experiments to analyze the limitations of
current MBRL-based HVAC control methods, in terms of model uncertainty and
controller effectiveness. Using the lessons learned, we develop MB2C, a novel
MBRL-based HVAC control system that can achieve high control performance with
excellent sample efficiency. MB2C learns the building dynamics by employing an
ensemble of environment-conditioned neural networks. It then applies a new
control method, Model Predictive Path Integral (MPPI), for HVAC control. It
produces candidate action sequences by using an importance sampling weighted
algorithm that scales better to high state and action dimensions of multi-zone
buildings. We evaluate MB2C using EnergyPlus simulations in a five-zone office
building. The results show that MB2C can achieve 8.23% more energy savings
compared to the state-of-the-art MBRL solution while maintaining similar
thermal comfort. MB2C can reduce the training data set by an order of magnitude
(10.52x) while achieving comparable performance to MFRL approaches.
