电力系统保护与控制2011，Vol.39Issue(17)：144-148,5.
基于RBF神经网络的短期负荷预测方法综述
Review on grid short-term load forecasting methods based on RBF neural network
摘要
Abstract
This paper introduces the concepts of the power system short-term load forecasting methods based on RBF neural network, and discusses its specific implementation approach. The continuous improving process of this method is reviewed by analogy analysis, and the progress made in practice is pointed out. Then, the basic principles and technical characteristics of several maturer RBF neural network prediction models are given with comparison and evaluation. According to the actual characteristics and new situations of power system operation, the method is analyzed from three aspects of improved algorithm, the original load data selection and combination of the actual load characteristics. The development space of continuous improvement in the field is discussed, finally the further development trend of technology in this field is prospected.关键词
短期负荷预测/人工神经网络/RBF径向基神经网络/粒子群优化/智能单粒子优化Key words
short-term load forecasting: the artificial neural network/ RBF(radial basis function) neural network/ particle swarm optimization/ intelligent single particle optimization分类
信息技术与安全科学引用本文复制引用
彭显刚,胡松峰,吕大勇..基于RBF神经网络的短期负荷预测方法综述[J].电力系统保护与控制,2011,39(17):144-148,5.基金项目
广东省自然科学基金研究项目(10151009001000045) （10151009001000045）
