山东电力技术2025，Vol.52Issue(7)：68-75,8.DOI:10.20097/j.cnki.issn1007-9904.2025.07.007
基于L-M优化BP算法的多因素协同电力负荷预测
Multi-factor Collaborative Power Load Forecasting Based on L-M Optimized BP Algorithm
摘要
Abstract
Power load forecasting is particularly important for the planning and operation of the power grid,as it can help power grid companies to make reasonable scheduling of power resources,better balance power resources,and thus improve the economy and stability of the power system.This article proposes a multi-factor collaborative power load forecasting method based on Levenberg Marquardt(L-M)optimized backpropagation(BP)algorithm to address the shortcomings of existing algorithm optimized BP neural networks,such as poor accuracy and low convergence.On the basis of improving the BP algorithm,the L-M algorithm is used to integrate the steepest descent method and Gaussian Newton method to overcome the disadvantages of slow training,easy fall into local optima,and large prediction errors.Trainlm,Traingd,Trainrp,etc.,are used to train the L-M algorithm,considering factors that affect power loads such as temperature,air temperature,and date,and to predict power load in conjunction with cooling and heating loads.Finally,the effectiveness of the proposed method was verified using a residential community as an example.Compared with other methods,the L-M optimized BP algorithm for multi-factor collaborative power load forecasting performs well in solving nonlinear and complex forecasting problems with high accuracy,faster calculation speed,and smaller errors.关键词
L-M/BP算法/协同/负荷预测/误差Key words
L-M/BP algorithm/coordination/load forecasting/error分类
信息技术与安全科学引用本文复制引用
白卓,郭啸,孙华忠..基于L-M优化BP算法的多因素协同电力负荷预测[J].山东电力技术,2025,52(7):68-75,8.基金项目
国网山东省电力公司科技项目(520604230004). Science and Technology Project of State Grid Shandong Electric Power Company(520604230004). （520604230004）
