The Dynamic Character Curve Adjusting Model of Electric Load Based on Data Mining Theory

Abstract

AbstractThere are a number of dirty data in the load database produced by SCADA system. Consequently, the data must be adjusted carefully and reasonably before being used for electric load forecasting or power system analysis. This paper proposes a dynamic and intelligent curve adjusting model based on data mining theory. Firstly the Kohonen neural network is meliorated according to fuzzy soft clustering arithmetic which can realize the collateral calculation of Fuzzy c-means soft clustering arithmetic. The proposed dynamic algorithm can automatically find the new clustering center, namely, the character curve of data, according to the updating of swatch data. Combining an RBF neural network with this dynamic algorithm, the intelligent adjusting model is introduced to identify the dirty data. The rapidness and dynamic performance of model make it suitable for real-time calculation. Test results using actual data of Jiangbei power supply bureau in Chongqing demonstrate the effectiveness and feasibility of the model.KeywordsCluster CenterFuzzy ClusterLoad CurveMembership GradeLoad ForecastThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.