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Power Load Data Preprocessing Based on PFCM Algorithm and Neural Network

Load data of power system are easily affected by various random factors to generate the abnormal data, which affects the accuracy of load forecasting and the effectiveness of load dispatch, even damages the safe and stable operation of power grid. In response to these anomalous data, a method of outliers modification integrated by PFCM clustering algorithm and RBF neural network is proposed in this paper. Firstly, the FPCM algorithm adaptively determines the number of clusters is used to cluster load curve according to the similarity and identify the abnormal data; Secondly, Secondly, the RBF network which optimized by genetic algorithm optimization is trained by classification of normal data where abnormal data belong to; Finally, the trained neural network is used to correct the abnormal data. The experiment indicates: the method can obtain the clustering result of the ideal load curve, and correct outliers more accurately at same. Compared with the traditional correction method, the average relative error of the method is reduced by 1.08% and 1.51% respectively.