基于混沌学习算法的配电网输电线路负荷预测分析

Analysis of Transmission Line Load Forecasting in Distribution Network Based on Chaotic Learning Algorithm

阐述基于混沌学习算法的配电网输电线路负荷预测分析,探讨处理输电线路负荷数据,辨识与调整负荷坏数据,反映输电线路负荷曲线真实情况,基于负荷水平及其曲线形状选取最优相似日,以确定的最优相似日负荷数据为依据,采用最大Lyapunov指数识别负荷数据混沌特性。以此为基础,训练混沌学习算法,确定算法最优参数值,将历史负荷数据输入至训练好的混沌学习算法,输出结果即为负荷预测数值,从而实现配电网输电线路负荷的预测。

This paper describes the analysis method of transmission line load forecasting in distribution network based on chaotic learning algorithm.It explores the processing of transmission line load data,identifies and adjusts bad load data,reflects the true situation of the transmission line load curve,selects the optimal similar day based on the load level and curve shape,and uses the maximum Lyapunov index to identify the chaotic characteristics of load data based on the determined optimal similar day load data.Based on this,train a chaotic learning algorithm to determine the optimal parameter value of the algorithm.Input historical load data into the trained chaotic learning algorithm,and the output result is the load prediction value,thereby achieving load prediction of distribution network transmission lines.