摘要
针对短期负荷预测精度低、准确性较差等问题,本文提出将鸽群优化算法和误差反向传播(Back Propagation,BP)神经网络模型相结合用于短期电力负荷预测研究。本文介绍了鸽群优化算法的原理以及算法模型,并利用鸽群优化算法对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参数,构建负荷预测模型。对某市7月的平均负荷预测来验证预测模型的有效性,其结果表明,改进的模型能够降低BP神经网络的预测误差,提高预测精度,并具有一定的普适性。
Aiming at the problems of low accuracy and poor accuracy of short-term load forecasting,this paper proposes the combination of pigeon swarm optimization algorithm and back propagation(BP)neural network model for short-term power load forecasting.This paper introduces the principle and algorithm model of pigeon swarm optimization algorithm,optimizes the weight and threshold of BP neural network by using pigeon swarm optimization algorithm,obtains the optimal parameters of BP neural network prediction model,and constructs load prediction model.The results show that the improved model can reduce the prediction error of BP neural network,improve the prediction accuracy,and has certain universality.
作者
白苏赫
张铭飞
李丁
彭丹阳
BAI Suhe;ZHANG Mingfei;LI Ding;PENG Danyang(North China University of Water Resources and Electric Power,Zhengzhou Henan 450045,China)
出处
《信息与电脑》
2021年第19期39-42,共4页
Information & Computer
关键词
负荷预测
鸽群算法
BP神经网络
load forecasting
pigeon swarm algorithm
BP neural network
作者简介
白苏赫(1995-),女,满族,辽宁抚顺人,硕士研究生在读。研究方向:电力系统与控制。
