Poster Abstract: Data Efficient HVAC Control using Gaussian Process-based Reinforcement Learning

Model-based Reinforcement Learning (MBRL) has been widely studied for energy-efficient control of the Heating, Ventilation, and Air Conditioning (HVAC) systems. One of the fundamental issues of the current approaches is the large amount of data required to train an accurate building system dynamics model. In this work, we developed a data-efficient system capable of excellent HVAC control performance with only days of training data. We use a Gaussian Process (GP) as the dynamics model which provides uncertainty for each prediction. To improve the data efficiency, we designed a meta kernel learning technique for GP kernel selection. To incorporate uncertainty in the control decisions, we designed a model predictive control method that considers the uncertainty of every prediction. Simulation experiments show that our method achieves excellent data efficiency, yielding similar energy savings and 12.07% less human comfort violation compared with the state-of-the-art MBRL method, while only trained on a seven-day training dataset.