Abstract
Massive information on residential consumption provides data support for the mining and analysis of load patterns. This paper presents a complete framework including clustering module and classification module for load pattern identification. Firstly, an innovative mixture model based on R-vine copula (RVMM) is proposed for clustering load profiling data and obtaining typical load patterns. Then, random forest constructed with certain load characteristic indexes and RVMM clustering result is employed as a supervised classification model to predict the category of new customers. It is demonstrated that the proposed RVMM algorithm exhibits better performance in the clustering validity evaluation and a higher accuracy is achieved by random forest classifier.
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