基于VMD-BES-LSSVM模型的短期电力负荷预测研究  

Study of Short-term Power Load Forecasting Based on the VMD-BES-LSSVM Model

作　　者：王铭超 WANG Mingchao(School of Electrical and Information Engineering,Anhui University of Science and Technology,Huainan 232001,China)

机构地区：[1]安徽理工大学电气与信息工程学院,安徽淮南232001

出　　处：《电工技术》2025年第6期74-77,共4页Electric Engineering

摘　　要：针对短期负荷预测难度大、精度低的问题,提出了一种基于VMD-BES-LSSVM模型的短期负荷预测方法。该方法首先借助变分模态分解(VMD)将负荷数据分解为各个IMF分量,以减少数据的非平稳性和噪声;然后,采用秃鹰搜索算法(BES)优化最小二乘支持向量机(LSSVM)惩罚参数和核函数参数,以提高模型的泛化能力和预测精度;最后,将优化后的LSSVM模型应用于各个IMF分量的预测,并将每个子序列得出的预测结果进行重构,从而获得最终的负荷预测结果。通过泉州真实负荷数据仿真表明,该方法在短期负荷预测能够有效应用,预测精度优于传统的预测方法。To address the challenges of short-term load forecasting,including difficulty and low accuracy,a short-term load forecasting method based on the VMD-BES-LSSVM model is proposed.This method first uses Variational Mode Decomposition(VMD)to decompose load data into individual intrinsic mode functions(IMFs)to reduce data non-stationarity and noise.Then,the Bald Eagle Search(BES)algorithm is employed to optimize the penalty and kernel parameters of the Least Squares Support Vector Machine(LSSVM),improving the model′s generalization ability and prediction accuracy.Finally,the optimized LSSVM model is applied to predict each IMF component,and the prediction results of each subsequence are reconstructed to obtain the final load forecasting result.Simulation results based on real load data from Quanzhou demonstrate that this method can be effectively applied to short-term load forecasting,with accuracy superior to traditional forecasting methods.

关 键 词：短期负荷预测 预测精度 变分模态分解 支持向量机 秃鹰算法 

分 类 号：TM714[电气工程—电力系统及自动化]