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《Proceedings of the Csee》 2001-08
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ZHANG Guo jiang 1,QIU Jia ju 1,LI Ji hong 2 (1.Zhejiang University, Hangzhou 310027, China; 2.Zhejiang Electric Company, Hangzhou 310007, China)  
A new method is presented to identify outliers in load data by fully utilizing the features of electrical load curves. First, the day load curves are clustered by a Kohonen neural network, and a typical load curve is thus obtained for each cluster. Then a BP neural network is trained with each typical load curve and some other curves derived from it with some outliers included. Owing to its generalization ability, the network can identify the outliers in the curves included in the corresponding cluster. At last, the outliers are adjusted with typical curves. The off line trained neural networks can be used to identify the outliers on line. Test results using actual data are served for demonstrating the feasibility of the proposed method.
【CateGory Index】: TM714
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