Research on AI Detection Model for Optimizing Building Energy Consumption Control Based on Reinforcement Learning

This paper proposes an AI detection model for optimizing building energy consumption control based on reinforcement learning. It uses deep reinforcement learning algorithms (such as deep Q learning) to analyze and process various environmental data inside the building in real time, including indoor temperature, humidity, external climate conditions, and building usage. The model minimizes building energy consumption by intelligently adjusting systems such as air conditioning and lighting, while ensuring that the user's comfort is not affected. Compared with traditional fixed rules and schedule control methods, the proposed reinforcement learning algorithm can adaptively adjust energy use strategies according to real-time environmental changes and dynamic needs in the building, thereby improving energy efficiency and reducing unnecessary energy consumption. In addition, the model can flexibly optimize energy efficiency in different building environments and usage scenarios, and has strong adaptability. Experimental results show that the optimization control method based on reinforcement learning can reduce building energy consumption by 18% and improve energy efficiency by 10%. Most importantly, after the algorithm is optimized, the response time is reduced by 15%, which improves the real-time feedback capability and efficiency of the system.