Bayesian Energy Consumption Prediction Method and Application of Campus Buildings Based on Monitoring Data

In view of the limitations of the traditional gray wolf algorithm in the Bayesian energy consumption prediction method and application of campus buildings, a Bayesian energy consumption prediction method and application scheme based on monitoring data algorithm were proposed. Firstly, the influencing factors is accurately located through the data set theory, and the indicators is reasonably divided to reduce interference, and the Bayesian energy consumption prediction method and application scheme is constructed by using the monitoring data algorithm. Experimental results show that under certain evaluation criteria, the proposed scheme is superior to the traditional gray wolf algorithm in terms of Bayesian energy consumption prediction method, application accuracy, and processing time of influencing factors, and has obvious advantages. Bayesian energy consumption prediction methods and applications play an extremely important role in campus buildings, which can accurately predict and optimize the growth characteristics and product generation of campus buildings. However, the traditional gray wolf algorithm has certain limitations in solving the simulation problems of prediction methods, especially when dealing with complex problems. In this paper, a Bayesian energy consumption prediction method and application scheme based on monitoring data algorithm is proposed to better solve this problem. The scheme accurately locates the influencing factors through the data set theory, so as to determine the division of indicators, and uses the monitoring data algorithm to construct the scheme. Experimental results show that under certain evaluation criteria, the accuracy and speed of the scheme is significantly improved for different problems, and it has better performance. Therefore, the simulation scheme based on monitoring data algorithm can better solve the limitations of the traditional gray wolf algorithm and improve the simulation accuracy and efficiency by using the Bayesian energy consumption prediction method and application of campus buildings.