湖北民族大学学报（自然科学版）2025，Vol.43Issue(1)：73-79,7.DOI:10.13501/j.cnki.42-1908/n.2024.12.019
基于LGWO-XGBoost-LightGBM-GRU的短期电力负荷预测算法
Short-term Power Load Forecasting Algorithm Based on LGWO-XGBoost-LightGBM-GRU
摘要
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.关键词
短期负荷预测/集成学习/灰狼算法/极限梯度提升/轻量级梯度提升机/门控循环单元Key words
short-term load forecasting/integrated learning/grey wolf algorithm/extreme gradient boosting/lightweight gradient boosting machine/gated recurrent unit分类
信息技术与安全科学引用本文复制引用
王海文,谭爱国,彭赛,黄佳欣怡,田相鹏,廖红华,柳俊..基于LGWO-XGBoost-LightGBM-GRU的短期电力负荷预测算法[J].湖北民族大学学报（自然科学版）,2025,43(1):73-79,7.基金项目
国家自然科学基金项目(62163013). （62163013）
