Application of multivariate statistical analysis to classify electricity customers

This article applies principal component analysis (PCA) and cluster analysis to solve the customer classification problem of an actual power supply network. First, the article defines the index system which will influence the cost of power supply significantly. The index system includes power supply, maximum need, load rate etc. Second, the article uses PCA to filter the source data from these index items into several main components. Third, these main components will be analyzed with system clustering algorithm and k-means clustering algorithm to make clustering analysis and comparison and provide the calculation method about how to make the efficiency test of these clustering results. The clustering results prove that the source data can be well classified with PCA method with the new indexes reflecting the characteristics of the whole source data. At last the article also proves the similarity of the results obtained from system clustering and K-means clustering separately and verifies the two clustering algorithm all can be used to actual analysis. The multivariate statistical analysis proposed in this article can reflect the characteristic of customer´s electrical behavior can be more objective and comprehensive that traditional methods and can a good tool for power price decision maker.