基于 XGBoost-神经网络的建筑负荷预测模型构建.

In response to the problem of heavy workload and difficulty in improving the generalization ability of feature selection in building load prediction models, a method based on extreme gradient boosting(XGBoost)-neural network for building load feature selection and prediction was proposed. The XGBoost algorithm was used to train the filtered data, and the optimal feature subset was determined based on the mean absolute percentage error(MAPE) to improve the model accuracy and generalization ability. The Bayesian regularization algorithm was used to train the feedforward neural network to reduce network structure complexity during training optimization and prevent network overfitting, thereby further improving its generalization ability. Experimental results of load prediction for a commercial building show that the mean squared error(MSE) of the model is reduced by 43. 29% after feature selection compared to before, effectively improving the model prediction accuracy. The neural network is trained using both Bayesian regularization and Levenberg-Marquardt(L-M) algorithms, with the former achieving an average reduction of 87. 08% and 85. 33% in root mean squared error (RMSE) and MAPE after 5 experiments, respectively, leading to effective improvement of the prediction model's generalization ability. [ABSTRACT FROM AUTHOR]