Machine Learning Algorithm in Intelligent Building Equipment Automation Control

In order to improve the self-learning ability of intelligent control system in green building, this paper analyzes the rules of user's setting of intelligent home operation. In this paper, the influence of external factors (indoor and outdoor temperature, humidity, illumination, etc.) and internal factors (historical setting data) on user satisfaction setting is studied. In this paper, an improved SVM model with adaptive PSO optimization is adopted, and the particle swarm optimization algorithm is used to optimize the penalty factor and kernel function parameters of SVM model. Taking an office building as an example, this paper discusses the application of artificial intelligence technology in intelligent building management, including the use of face recognition to judge personnel and their authority, and the use of voice recognition to judge user instructions. Machine learning is used to analyze the operation status of central air conditioning, and the intelligent building management system is used to control the access control, lighting, video, air conditioning unit and other equipment, so as to improve the user's office experience and realize the building energy-saving operation. The experimental results show that the algorithm can improve the self-learning ability of green building intelligent control system.