中长期电力负荷预测相关影响因素优化选择  

Optimization Selection of Correlative Factors for Long-term Load Prediction of Electric Power

作　　者：朱继萍 戴君 

机构地区：[1]西安文建学院机电工程系,陕西西安710065

出　　处：《计算机仿真》2008年第5期226-229,共4页Computer Simulation

基　　金：西安文理学院专项科研基金资助申请项目(自然科学)项目(KY200530)

摘　　要：为了更好地反映各种相关因素对负荷的影响,采用人工神经网络进行中长期负荷预测。基于人工神经网络原理,设计了一个由输入层、隐含层和输出层组成的三层BP网络模型,利用神经网络高度非线性建模能力,选取国内生产总值、重工业生产总值、轻工业生产总值、农业生产总值、第一产业、第二产业和第三产业等七个因素作为神经网络的输入变量。采用排除法对这些输入变量进行优化选择,并对预测精度的影响进行了探讨。仿真结果证明利用排除法后预测精度明显提高,故提出的方法是可行和有效的。In order to reflect the influence of each element on the load forecasting result, an Artificial Neural Network （ANN） based approach for long-term load forecasting is investigated. Based on the theory of arificial neural network, a three-layer back propagation （BP） network is proposed. The idea is to forecast medium and long term load using the ability of ANN to nonlinear system. Seven factors are selected as inputs for the proposed ANN. The factors include GDP, heavy industry production, light industry production, agriculture production, primary industry , secondary industry and tertiary industry. Elimination method is used for the optimization selection of correlative factors, and the forecasting accuracy is discussed. Simulation results show that predicting precision is elevated notably after using elimination method, so the method brought forward is feasible and effective.

关 键 词：中长期电力负荷预测 人工神经网络 排除法 优化选择 

分 类 号：TM715[电气工程—电力系统及自动化]