Design of Intelligent Management Technology for Hotel Air Conditioning Based on Coupling Model and Deep Neural Network

Abstract

Variable air volume air conditioning systems have the advantages of low energy consumption and easy control, making them an important object in the field of air conditioning. This study uses deep belief networks to predict and control the temperature and humidity of variable air volume air conditioning systems in hotel buildings. It analyzes the heat transfer characteristics of building envelope structures by constructing mathematical models and optimizes deep belief network models to improve prediction accuracy. By using the proportional integral control algorithm, the system dynamically adjusts the air valve based on the difference between the predicted indoor temperature and the set target temperature, achieving precise control of the indoor environment. The results showed that indoor temperature could quickly adapt to outdoor temperature changes, and the average absolute relative error of the deep belief network model was 1.555%, with a determination coefficient of 0.9975. In practical applications, the room temperature successfully reached the predetermined target within 300 minutes, maintaining stability even in the presence of interference. The research results provide an efficient intelligent control method for building energy management.