Deep Reinforcement Learning on HVAC Control

Abstract

Due to increase of computing power and innovative approaches of an end-to-end reinforcement learning (RL) that feed data from high-dimensional sensory inputs, it is now plausible to combine RL and Deep learning to perform Smart Building Energy Control (SBEC) systems. Deep reinforcement learning (DRL) revolutionizes existing Q-learning algorithm to Deep Q-learning (DQL) profited by artificial neural networks. Deep Neural Network (DNN) is well trained to calculate the Q-function. To create comprehensive SBEC system it is crucial to choose appropriate mathematical background and benchmark the best framework of a model based predictive control to manage the building heating, ventilation, and air condition (HVAC) system. The main contribution of this paper is to explore a state-of-the-art DRL methodology to smart building control.