A novel reinforcement learning method based on generative adversarial network for air conditioning and energy system control in residential buildings

Abstract

Residential buildings account for a significant portion of global energy consumption, making the optimal control of air conditioning and energy systems crucial for improving energy efficiency. However, existing reinforcement learning (RL) methods face challenges, such as the need for carefully designed reward functions in direct RL and the dual training phases required in imitation learning (IL). To address these issues, this study proposes a Generative Adversarial Soft Actor-Critic (GASAC) framework for controlling residential air conditioning and photovoltaic-battery energy storage systems. This framework eliminates the need for predefined reward functions and achieves optimal control through a single training process. An accurate simulation model was developed and validated using real-world data from a residential building in Japan to evaluate the proposed method’s performance. The results show that the proposed method, without requiring a reward function, increased the time the temperature remained within the comfort range by 11.43 % and reduced electricity costs by 14.05 % compared to baseline methods. Additionally, the training time was reduced by approximately two-thirds compared to direct RL methods. These findings demonstrate the effectiveness of GASAC in achieving optimal temperature control and energy savings while addressing the limitations of traditional RL and IL methods.