南华大学学报：自然科学版2011，Vol.25Issue(3)：42-45,4.
基于改进径向基函数网络的电力系统短期负荷预测
Power System Short Term Load Forecasting Based on Improved Radial Basis Function Network
摘要
Abstract
Power system Short term load forecasting is one important work of the electricity production sector. In this paper,radial basis function network （RBF） is used in load forecast ing. Load forecasting for the RBF in the hidden layer nodes is hard to find. An improved nearest neighbor clustering algorithm is proposed to solve the difficulties and improve RBF neural network convergence speed and load forecasting accuracy. According to the instance of a regional power grid study,we found that the minimum,maximmn relative error were reduced by 0. 14 and 1.12,if we used the improved algorithm to predict. Case study results prove its effectiveness and feasibility. It provides a new way for the power system load forecasting.关键词
电力系统/短期负荷预测/径向基函数/改进最近邻聚类Key words
power system/short term load forecasting/radial basis function/improved nearest neighbor clustering分类
信息技术与安全科学引用本文复制引用
赵宇红,汪普林,梁海滨..基于改进径向基函数网络的电力系统短期负荷预测[J].南华大学学报：自然科学版,2011,25(3):42-45,4.基金项目
湖南省科技计划基金资助项目 （）
