Short Term Load Forecasting Based IGA-RBF Neural Network

摘要：

According to the premature shortcoming of genetic algorithm,a radial basis function (RBF) neural network short term load forecasting model based on improved genetic algorithm that uses Disruptive selection strategy,adaptive crossover and mutation probability increased population the diversity of the iterative process to prevent prematurity is presents in this paper;the adaptive crossover and mutation of the improved genetic algorithm and gradient descent method mixed interactive computing as the learning algorithm of RBF network,applying the model to predict short-term load power system made a more satisfactory results.

关键词： Genetic Algorithm RBF neural network adaptive load forecasting

作者: Yuhong Zhao Zhennan Hong Xuecheng Zhao

作者单位: Institute of Electrical Engineering,University of south China,Hengyang,Hunan,China 421001 Mechanical and Electrical Engineering Department,Shaoyang Vocatioonal & Technical,Shaoyang,Hunan,Chi

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Computer and Network Technology(ICCNT 2011)(2011第三届IEEE计算机与网络技术国际会议)

会议地点: 太原

会议语种:英文

页码: 424-427

在线出版日期: 2011-02-26（万方平台首次上网日期，不代表论文的发表时间）