Short-term heating load forecasting based on principal component analysis and Bayesian optimization XGBoost
ZHENG Yi
Abstract：Aiming at the problems such as a large number of characteristic variables and poor prediction accuracy in heating load forecasting,a short-term heating load forecasting method based on Principal Component Analysis(PCA)and Bayesian optimization XGBoost is proposed.The historical data set is processed,and PCA is used to reduce the dimension of the data set.The Bayesian algorithm is used to optimize the hyperparameters of XGBoost.Effectively improve the accuracy of the prediction model.The results show that combined with the actual load data,the PCA-Bayesian optimization XGBoost combined prediction method has better prediction performance and high prediction accuracy,and its R2 can reach 0.997.
Keywords：heating loadprediction modelprincipal component analysis
Publication Date：2025-05-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：4( 80-83 )