电力信息与通信技术2025，Vol.23Issue(2)：11-17,7.DOI:10.16543/j.2095-641x.electric.power.ict.2025.02.02
基于双层优化的度夏负荷预测模型
Summer Load Forecasting Model Based on Two-layer Optimization
摘要
Abstract
With the frequent occurrence of extreme high temperatures during the summer,the loads have been growing dramatically and rapidly in recent years.As an important link to support the supply of electricity and the stable operation of power grids,the requirements for the accuracy of summer load forecasting have gradually risen.The current prediction algorithms are often not timely in tracking the rapid increase of load,and the prediction results are much lower than the actual value.Therefore,this paper proposes a two-layer optimized summer load forecasting model,which adopts a differential evolution algorithm to optimize the hyperparameters of LightGBM model in the inner layer,and optimizes the annual growth coefficients in the outer layer to reduce the impact of historical low load.The load data of the State Grid during the summer period of each year from 2021 to 2023 are selected for example verification,and the average absolute percentage error of prediction decreases by 1.82%compared with that of the LightGBM single model,which proves the validity and accuracy of the prediction model.关键词
负荷预测/度夏负荷/差分进化算法/LightGBMKey words
load forecasting/summer load/differential evolution algorithm/LightGBM分类
信息技术与安全科学引用本文复制引用
刘洋,白雪峰,陈宋宋,高际惟,赵波,胡长斌..基于双层优化的度夏负荷预测模型[J].电力信息与通信技术,2025,23(2):11-17,7.基金项目
国网北京市电力公司科技项目"虚拟电厂参与辅助服务技术研究"(520204230005). （520204230005）
