Abstract: Building a precise portrait of electricity consumption in the substation area, depicting the load characteristics and electricity consumption patterns of the substation area, can provide precise guidance for refined load forecasting, load fluctuation analysis and tracing, and substation business scenarios. Therefore, this paper proposes a precision profiling technology of transformer areas clustering based on VAE(variational auto-encoder)-Kmeans algorithm. Firstly,VAE, Kmeans clustering and time series similarity algorithms are used to extract the typical curves of load for station areas and industries from multiple dimensions. Secondly, the most similar industry load is determined through comprehensive evaluation, and the corresponding portrait label is assigned to the transformer area. Finally, taking the relatively large residential transformer area as an example, a deep mining and accurate description strategy based on cluster analysis and decision tree algorithm are constructed, and 14 sets of accurate portrait categories of urban and rural residential transformer areas are formed, which provides a solid foundation for fine load forecasting and differentiated transformer operation and maintenance management strategies.

Key words: precision portrait in the substation area, typical curve, cluster analysis, load curve