Research on energy management of building operation and maintenance based on multiple prediction algorithms

With the continuous promotion of national energy conservation and emission reduction, the in-depth application of information technology has gradually triggered in-depth changes in the development mode of the country, city and industry. This paper mainly starts from the status quo of national building energy consumption and related energy saving and green development plan, introduces the research status quo of building energy management, and aims at the more popular machine learning algorithm models in recent years, including Random Forest Regression Model, XGBoost Model and Stacking Multi-Algorithmic Fusion Model, and combines with CITIC Design Digital Intelligent Building System in the statistics of a certain office building with a total of 321 days of Combined with the raw data of measured energy consumption of an office building counted in the CITIC Design Digital Intelligent Building System for a total of 321 days, the prediction learning of building operation and maintenance energy consumption is carried out respectively, the prediction effects of the three prediction algorithms are compared and analyzed, and it is recommended to use the Stacking Multi-Algorithmic Fusion Model for predicting the energy consumption of building operation and maintenance and the operation and maintenance mode of building operation and maintenance energy consumption control in advance warning is proposed by combining with energy consumption prediction model.