Analysis of users’ electricity consumption behavior based on ensemble clustering

Due to the increase in the number of smart meter devices,a power grid generates a large amount of data.Analyzing the data can help in understanding the users’electricity consumption behavior and demands;thus,enabling better service to be provided to them.Performing power load profile clustering is the basis for mining the users’electricity consumption behavior.By examining the complexity,randomness,and uncertainty of the users’electricity consumption behavior,this paper proposes an ensemble clustering method to analyze this behavior.First,principle component analysis(PCA)is used to reduce the dimensions of the data.Subsequently,the single clustering method is used,and the majority is selected for integrated clustering.As a result,the users’electricity consumption behavior is classified into different modes,and their characteristics are analyzed in detail.This paper examines the electricity power data of 19 real users in China for simulation purposes.This manuscript provides a thorough analysis along with suggestions for the users’weekly electricity consumption behavior.The results verify the effectiveness of the proposed method.

Estimating the Spatial Variation of Electricity Consumption Anomalies and the Influencing Factors

Effective detection of abnormal electricity users and analysis of the spatial distribution and influencing factors of abnormal electricity consumption in urban areas have positive effects on the quality of electricity consumption by customers,safe operation of power grids,and sustainable development of cities.However,current abnormal electricity consumption detection models do not consider the time dependence of time-series data and rely on a large number of training samples,and no study has analyzed the spatial distribution and influencing factors of abnormal electricity consumption in urban areas.In this study,we use the Seasonal-Trend decomposition procedure based on Loess(STL)based time series decomposition and outlier detection to detect abnormal electricity consumption in the central city of Pingxiang,and analyze the relationship between spatial variation and urban functions through Geodetector.The results show that the degree of abnormal electricity consumption in urban areas is related to geographic location and has spatial heterogeneity,and the abnormal electricity users are mainly located in areas with highly mixed residential,commercial and entertainment functions in the city.The results obtained from this study can provide a reference basis and a theoretical foundation for the detection of abnormal electricity consumption by users and the arming of electricity theft devices in the power grid.