The large industrial users of an industrial park in Zhangjiang

Shanghai are studied to analyze the relevance between power consumption benchmark value of typical industry users and power price policy using big data technology. To solve problems of traditional kmeans clustering algorithm

a kmeans ++ algorithm based on composite distance is proposed

which takes into account the spatial and morphological similarities of electricity curves

and optimizes the selection of initial values

so clustering effect is better. This algorithm is used to cluster large industrial users. Finally

five typical industrial users are found in large industry and their reasons are analyzed. Based on the transformer capacity and power consumption data of typical industries

the power consumption benchmark values of each typical industry are calculated and their fluctuations before and after the implementation of the new tariff policy are analyzed. These help electricity companies to identify the key direction of follow up work and put forward reasonable opinions to different users.