基于改进ART2网络的电力负荷脏数据辨识与调整

为提高电力负荷预测和特性分析的精度，应首先对负荷历史数据的脏数据进行辨识和调整。文中提出了基于改进ART2网络的脏数据辨识与调整模型。该模型首先基于类内样本与类中心距离不同会对类中心的偏移产生不同影响的思想，改善了传统的ART2模式漂移的不足，然后根据残差理论以及电力负荷曲线固有的特征，增加了鉴别修正子系统。利用模型中传统的ART2部分对负荷曲线进行分类并提取其特征曲线，然后再利用鉴别修正子系统对输入的负荷数据进行脏数据辨识与调整。实例分析说明了该方法的有效性。

Identification and Justification of Dirty Electric Load Data Based on Modified ART2 Network

In order to improve the forecasting precision of electric load data and the precision of characteristic analysis,dirty electric load data must be identified and justified early.A model for dirty data identification and justification is put forward based on the modified ART2 network.Pattern drifting phenomenon of the traditional ART2 neural network is improved in this model according to the idea that different distances between the sampling data and the clustering center of the same class exert different influences on the excursion of the clustering center.An identification subsystem is added to the ART2 model according to the residual error theory and inherent characteristics of the electric load curve.In the model proposed,electric loads are classified and the feature curves are picked up using the traditional ART2 neural network.Then the identification subsystem is used to identify and justify the dirty electric load data.Test results of actual data demonstrate the validity and feasibility of the modified ART2 network.