Abstract: Faced with the energy development situation in the new epoch, it is key measures for promoting the development of power energy Internet to effectively integrate the information of power users, grid companies and suppliers, and to comprehensively analyze the source network load and storage characteristics. Therefore, it is crucial to simulate the typical electricity load in buildings. This study focuses on the electricity consumption of different households in district scale and proposes a preprocessing method of electricity load of district-scale users, and a residential daily electricity load simulation method. The electricity consumption data of urban scale has high-dimensional characteristics in both spatial scale and temporal span. Characterizing the district-scale electricity consumption is the principal difficulty. To this end, this study proposes a method integrating auto-encoding and neighbor clustering algorithms for data outlier detection for data with high-dimensional characteristics at the spatial and temporal scales. Based on the results of preprocessing, the study proposes a district-scale residential electricity load simulation model based on clustering analysis, and the evaluation method. Clustering analysis was performed on the average daily electricity consumption and the daily maximum load of the whole year of single household. Based on the clustering analysis, a random electricity model is proposed to simulate the daily electricity consumption of residential buildings on the district scale. In this study, part of the household in one key city in East China is used as a case study to demonstrate the proposed method. The raw data comes from the daily electricity consumption measurement of multi-family smart meters, lasting for one year. To conclude, this study builds a complete research method of data outlier preprocessing, clustering analysis, model construction and model verification, it can effectively solve the needs of power energy Internet construction for the end use energy consumption analysis of a large amount.