基于RBF神经网络的短期负荷预测

本科毕业设计（论文）

基于RBF神经网络的短期负荷预测

学院自动化学院

专业电气工程及其自动化__

_（电力系统自动化方向）

年级班别 2007级（3）班

学号 3107001208

学生姓名郭祝帆

指导教师彭显刚

2011年 5 月

基于RBF 神经网络的短期负荷预测

郭祝帆

自动化学院

摘要

电力系统负荷预测的水平已成为衡量电力系统运行管理现代化的标志之一。精确的短期负荷预测，对电力系统的生产安排、经济调度和安全分析都起着十分重要的作用，也直接影响着电力企业的经济效益。因此，短期负荷预测结果成为制定电力市场交易计划的重要依据，这就对短期负荷预测提出了更高的要求。

由于常规算法不能较好地反映气象条件等外界因素对负荷的影响，而近年来人工神经网络法等智能算法具有高度的非线性映射能力，可以较好地考虑气象条件等因素对电网负荷的影响，所以本文采用了基于RBF(Radial Basis Function)神经网络的电力系统短期负荷预测方法。该模型训练速度快，收敛性好，而且可以大大地减少隐含层神经元的数目，有效地提高了预测精度。

本文在分析了目前短期电力负荷预测的现状及各种预测方法、预测模型的基础上，根据电力负荷特性的变化规律，通过对河源地区的历史负荷数据分析，考虑了日期类型、温度、天气状况等影响负荷预测的因素，结合神经网络的预测算法，建立RBF神经网络的短期负荷预测数学模型，并在此基础上，利用面向对象的编程方法实现短期负荷预测程序。

本文讨论了影响负荷的各种因素，在输入变量中考虑临近日负荷特点，以及各种气象因素，对输入负荷值进行归一化处理，对温度、天气和日期等因素进行了量化处理。利用河源地区的历史负荷数据比较未含天气因素的神经网络和具有天气因素的神经网络的预测效果，根据本文所介绍的方法编程，其结果表明预测精度是符合要求的，从而说明了该方法的可行性和实用性。

关键词：短期负荷预测，RBF神经网络，编程

Abstract

The level of load forecasting is one of the measures of modernization of Power system management. Accurate short-term load forecasting plays an important role for planning, economical scheduling and security analysis in production, which directly influences the profit of the electric utility enterprises. Therefore, short-term load forecasting reseult become importance basis of drawing up the electric power market bargain plan. So these put short-term load forecasting forward a higher request.

The normal calculate way can not reflect goodly weather condition and other outside factors to the influence for load forecasting. In recent years, the artificial neural network method etc have height nonlinear to reflect the ability of shoot, can reflect goodly the weather factor etc. So this paper presents a short-term load forecast method based on RBF(Radial Basic Function) neural network for power system. This model speeds rapidly,improves convergence property in training process and the number of neurons in the hidden layer can be significantly decreased. So the forecasting accuracy can be increased effectively.

This text analyze the present condition and various methods and mathematics model of the short-term load forecasting. According to the rule of change of load characteristic, the RBF models for the short-term load forecasting are proposed by combining the artificial neural networks and electric load characteristics on HeYuan Power Markets, after calculating the factors such as date type, temperature,weather status etc which influencing the load forecasting. Based on the models, the load forecasting software has programmed by Object Oriented method.

This thesis analyzes every kind of factor which impacts load. In its input features, the load characteristic of neat days every kind of weather factors that considered. Then we unify the input variables, quantify the temperature, weather and date etc. The forecasting accuracy of neural networks models including climate factors and no those factors is compared by the load data from HeYuan. The testing results illustrate that the forecasting accuracy is satisfactory, accordingly it shows the validity and practicability of the method.

Keywords: Neural network, RBF, Short-term load forecasting