Short-term Power Load Forecasting Algorithm Based on LGWO-XGBoost-LightGBM-GRU
WANG Haiwen
TAN Aiguo
PENG Sai
HUANG Jiaxinyi
TIAN Xiangpeng
LIAO Honghua
LIU Jun
Abstract：To address the problem of low precision of short-term power load forecasting caused by the difficulty of historical load feature extraction,a logistic grey wolf optimizer-extreme gradient boosting-light gradient boosting machine-gated recurrent unit(LGWO-XGBoost-LightGBM-GRU)short-term power load forecasting algorithm was proposed based on the idea of stacked generalisation integration.The grey wolf optimizer(GWO)algorithm was enhanced through the application of the logistic map,resulting in the LGWO algorithm.Subsequently,the LGWO algorithm was employed to fine-tune the parameters of XGBoost,LightGBM and GRU algorithm.The XGBoost and LightGBM were utilized to extract distinct features from the dataset.These features were then integrated into the historical load dataset as input for further analysis.The GRU was leveraged for the final load forecasting,generating prediction results.The efficacy of the algorithm was validated through load forecasting in an industrial park.The results showed that,in comparison to the least squares support vector machines(LS-SVM)algorithm,the proposed algorithm decreased the root mean squared error by 68.85%,the mean absolute error by 69.57%,and the mean absolute percentage error by 69.97%,and improved the coefficient of determination by 8.42%.The proposed algorithm significantly enhanced the precision of short-term electricity load forecasting.
Keywords：short-term load forecastingintegrated learninggrey wolf algorithmextreme gradient boostinglightweight gradient boosting machinegated recurrent unit
Publication Date：2025-03-19
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 73-79 )