Optimized XGBoost based sparrow search algorithm for short-term load forecasting

Abstract

To address the problem that the difficulty of selecting parameters in the XGBoost model makes it difficult to optimize the regression effect, a short-term load forecasting model based on the sparrow search algorithm to optimize XGBoost is proposed. Similar days are selected as the training set by the GRA algorithm, the mean absolute error obtained by cross-validation is used as the fitness function, the sparrow search algorithm (SSA) is used to optimize the XGBoost covariate selection process, and the SSA-XGBoost load forecasting model is constructed, and finally the load is corrected by the compensation model to obtain the final load forecasting data. Taking the load data of a region in Zhejiang Province from January 2019 to December 2020 as an example, the prediction ability of the SSA-XGBoost load forecasting model is examined through five experiments. The experimental results show that (i) the parameters of SVM, RF, and XGBoost models can be optimized using the SSA algorithm, and SSA-SVM, SSA-RF, and SSA-XGBoost can quickly calculate the load forecasting data, among which the SSA-XGBoost model has the highest accuracy. Compared with kmeans and other clustering methods, this paper uses the GRA algorithm to select similar days more reasonably, with smaller prediction errors and a controllable number of training sets. The compensation model improves the prediction accuracy of the model by correcting the SSA-XGBoost load prediction data.