摘要
由于短期电力负荷、用电量受众多复杂的非线性因素影响,传统单一BP神经网络预测方法存在精度不高、收敛速度慢等问题。为了提高收敛速度和预测精度,根据影响因素特性将其分为长期、短期性影响因素,根据负荷、用电量曲线特性分别将其分为基准量和敏感量,并用决定系数法确定所需短期影响因素。应用遗传算法对BP神经网络的初始权值和阈值进行优化,将BP神经预测误差作为遗传算法的适应度函数,建立了基于特性分析的改进BP神经网络短期电力预测方法。选取中部某省2015--2019五年“迎峰度冬”期间数据进行验证,结果表明,该预测方法的精度和收敛速度都得到了提高。
Because the short-term power load and power consumption are affected by many complicated non-linear factors, the traditional single BP neural network overall prediction method has problems such as low accuracy and slow convergence. In order to improve the convergence speed and prediction accuracy, it is divided into long-term and short-term influencing factors according to the characteristics of the influencing factors, and it is divided into reference and sensitive quantities according to the characteristics of the load and power consumption curves.Finally the required short-term influencing factors are determined by using the determination coefficient method.The genetic algorithm is used to optimize the initial weight and threshold of the BP neural network, and the BP neural prediction error is used as the fitness function of the genetic algorithm. An improved BP neural network short-term power prediction method based on characteristic analysis is established. The data of a winter period during the five-year peak period of from 2015 to 2019 in a central province is selected for verification, and the results show that the accuracy and convergence rate of the prediction method are improved to meet the requirements.
作者
张武军
程远林
周捷
潘轩
无
ZHANG Wujun;CHENG Yuanlin;ZHOU Jie;PAN Xuan;无(China Energy Engineering Group Hunan Electrie Power Design Institute Co.,Ltd.,Changsha 410007,China;Hunan Province Collaborative Innovation Center of Clean Energy and Smart Grid,Changsha 410004,China;Changsha University of Science and Technology,Changsha 410004,China)
出处
《湖南电力》
2020年第3期17-22,共6页
Hunan Electric Power
关键词
负荷特性
BP神经网络
遗传算法
短期预测
load characteristics
BP neural network
genetic algorithm
short-term prediction
