Research on Energy Consumption Prediction of Ultra-Low Energy Public Building Heating System in Severe Cold Region

In this paper, the energy consumption of the heating system of ultra-low energy public buildings in the cold area is predicted. Firstly, the multiple linear regression method is used to determine the factors that have a relatively large impact on the energy consumption of the heating system in the meteorological parameters and environmental parameters. Secondly, the random forest energy consumption model is constructed to predict energy consumption, and the hyperparameters of the random forest are optimized by using the Dung beetle optimization algorithm. The results show that the mean square error, mean absolute error and determination coefficient of the optimized model are better than that of the single random forest model, which verifies the effectiveness of the model for predicting the energy consumption of the building internal heating system.