Research on the Responsiveness of Residential Electricity Users to the Electricity Accumulation Points Strategy Based on Data Mining and Profiling Analysis

This study constructs a selective ensemble clustering model based on evidence reasoning, following a comprehensive evaluation of various clustering methods. The model is tested using electricity consumption data from residential users in a provincial capital city to ensure the accuracy of the analysis results. Based on this data, a comprehensive system of segmentation indicators for residential electricity consumption behavior is established, covering multiple dimensions such as basic user information, general statistics, and behavioral information. The analysis explores various factors, including electricity consumption, long-term fluctuation trends, seasonal patterns, fluctuation stability, tiered factors, peak-valley differences, and changes in accumulation points. By analyzing the characteristics of residential electricity consumption groups in the city, the study develops detailed profiling for different user groups. Based on the profiling analysis, an application is proposed to assess the responsiveness of different residential user groups to the electricity accumulation points strategy. Effective and reliable differentiated recommendations are provided.