摘要
针对短期负荷预测精度低、准确性差等问题,将猫群算法CSO和BP神经网络相结合用于短期负荷预测,模型的输入因子是负荷数据和气象信息等,利用猫群算法对BP神经网络的权值和阈值进行优化,得到BP神经网络预测模型的最优解,建立了短期预测模型。通过实例验证了预测模型的有效性和有效性,结果表明,改进模型能够有效降低BP神经网络模型的预测误差,提高预测精度,为我国电力系统短期负荷预测的发展提供了参考和借鉴。
Aiming at the problems of low accuracy and poor accuracy of short-term load forecasting,a short-term load forecasting method based on cat swarm optimization(CSO)algorithm and BP neural network is proposed in this paper.The input factors of the model are load data and meteorological information,cat swarm optimization algorithm is used to optimize the weight and threshold of BP neural network,so that the solution of BP neural network forecasting model can be optimized,a short-term load forecasting model is established.The accuracy and validity of the prediction model are verified by simulation,the results show that the improved model can effectively reduce the prediction error of BP neural network model and improve its prediction accuracy.This study provides a reference for the development of short-term load forecasting of power system in China.
作者
王克杰
张瑞
Wang Kejie;Zhang Rui(Huaibei Power Supply Company,State Grid Anhui Electric Power Co.,Ltd.,Huaibei 235000,Anhui,China)
出处
《电测与仪表》
北大核心
2019年第24期115-121,共7页
Electrical Measurement & Instrumentation
关键词
短期负荷预测
猫群算法
BP神经网络
预测模型
short-term load forecasting
cat swarm algorithm
BP neural network
forecasting model
