Abstract:Aiming at the problems of non-unique and low accuracy of typical analysis results of multi-source electricity consumption big data, The electricity consumption association analysis algorithm for optimal selection of multivariate data clustering is proposed. The daily load clustering of wavelet transform is used to realize the similarity clustering of the daily load of multi-source electricity consumption, thereby improving the accuracy of data analysis;Then,a single fine-grained canonical correlation analysis is carried out on the obtained group, and the accuracy of canonical weight is verified by the predictability of canonical correlation analysis, which obtain the optimal selection of single analysis result and realize the uniqueness of analysis result.The algorithm is simulated on the electricity, gas and weather data sets of non residential electricity customers in Beijing.The results show that there are basically stable, seasonal and periodic changes in the typical correlation curve of the three data sets in different user groups.Compared with the other eight algorithms, it can be seen that the association mining of the algorithm in this paper is the most in-depth and accurate, in which the average correlation coefficient is increased by at least 1.52%, and the mean square error is reduced by at least 2.09%.