一种基于径向基函数的短期负荷预测方法

A DETERMINISTIC ANNEALING CLUSTERING BASED SHORT-TERM LOAD FORECASTING METHOD WITH RADIAL BASIS FUNCTION NETWORK

为克服传统K均值聚类法局部寻优的缺陷,提出了基于确定性退火聚类选取径向基函数(RBF)网络隐层节点中心的方法,并采用遗传算法有效地解决了径向基函数网络的学习问题。在选择学习样本时,根据相似度方法,综合考虑了日期类型、星期类型、天气因素和曲线特性的影响。实际应用表明本方法能够改善预测精度,提高预测速度。

To overcome the defects in local search by traditional K-means method, a method based on deterministic annealing clustering to determine the 'center' position of the hidden layer of radial basis function (RBF) network is proposed and the learning of RBF network is effectively solved by genetic algorithm. In the selection of learning samples, according to the similarity method the influences of the type of date, the type of weeks, the whether condition and the characteristics of the curves are comprehensively considered. The results of practical applications of the proposed method show that both the precision and speed of load forecasting can be improved.