可再生能源2017，Vol.35Issue(1)：80-85,6.
基于ACFOA优化RBF的短期风电功率预测
Short-term wind power prediction based on the optimization of radial basis function by adaptive chaos fruit fly optimization algorithm
摘要
Abstract
In order to improve the prediction precisions of short-term output of the wind power,this paper put forward an advanced prediction method,based on the basic prediction methods,optimizing the neural network of radial basis function (RBF) using adaptive chaos fruit fly optimization algorithm (ACFOA).This optimization method adopted the adaptive chaos to optimize the evolutionary mechanism of the fruit fly algorithm,using ACFOA to improve the structure parameters of RBF neural network to enhance the generalization ability of the network,meanwhile validated and analyzed the historical data of a wind plant.The results of the simulation show the modified prediction model proposed in this paper can effectively reduce the probabilities of occurrence of large error compared with PSO-RBF prediction method and can greatly improve the prediction precisions of output wind power.关键词
风电功率/预测模型/RBF神经网络/ACFOA算法/参数优化Key words
wind power/prediction model/radial basis function (RBF) neural network/adaptive chaos fruit fly optimization algorithm(ACFOA)/parameter optimization分类
能源科技引用本文复制引用
崔闪,彭道刚,钱玉良..基于ACFOA优化RBF的短期风电功率预测[J].可再生能源,2017,35(1):80-85,6.基金项目
上海市“科技创新行动计划”社会发展领域项目(16DZ1202500). （16DZ1202500）
