LAUSR

Abstract Cooling load prediction can provide reliable data support for the development of low energy consumption building. It is difficult for traditional models to achieve stable prediction accuracy under different load mode when the amount of data is limited. To resolve this problem, gradient boosting decision tree (GBDT) model based on selection of input variables was proposed to predict the cooling load of commercial buildings. First, the input variables that affect prediction cooling load were determined through correlation analysis on the original data set. Then, the Pearson analysis was done on the original data to get the optimal combination of input variables. Next, the ensemble learning technology was used to build GBDT model. In order to improve the prediction accuracy of the model, the grid search method was applied to determine the hyperparameter combination of GBDT. Experimental results showed that in the high-load mode, the mean absolute error (MAE) of the GBDT model was reduced by about 24% compared with the SVM, and was reduced by about 37% compared with the DNN model. Under the general load mode, MAE was reduced by about 30% and 53% respectively. Compared with the other models, the prediction accuracy of GBDT model has been significantly improved under different load mode. The proposed cooling load prediction model can provide reliable data support for the predictive control of buildings.