Optimization Control of HVAC System and Building Energy Management Based on Machine Learning

This paper discusses the optimal control and building energy management of building heating, ventilation and air conditioning (HVAC) systems based on deep Q networks (DQNs). Traditional control strategies often have difficulty adapting to the dynamic changes of the environment and the personalized needs of users. This paper studies a DQN algorithm based on reinforcement learning to learn the optimal control strategy through interaction with the environment to achieve the best balance between energy saving and comfort. First, the HVAC system is modeled as a Markov decision process (MDP), including three elements: state, action, and reward. Then, a DQN model is designed and implemented to improve the efficiency and stability of learning through experience replay and target network. Experience replay reduces the correlation between samples by randomly sampling past experience, while the target network reduces the oscillation during training by stabilizing the estimation of Q values. Finally, the application effect of DQN in HVAC system control is verified by verification. The specific data of the experimental results show that DQN performs well in energy efficiency. In terms of response speed, the response time of DQN to indoor and outdoor temperature fluctuations is 64.4 seconds, which is significantly faster than other traditional methods. In the adaptability score, DQN's highest adaptability score can reach 91.1 points. The DQN algorithm has significant advantages in HVAC system optimization control and building energy consumption management, and can achieve lower energy consumption and higher control accuracy.