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

doi:10.11947/j.JGGS.2022.0204

测绘学报（英文版） ›› 2022, Vol. 5 ›› Issue (2): 29-37.doi: 10.11947/j.JGGS.2022.0204

收稿日期:2021-10-31 接受日期:2022-02-22 出版日期:2022-06-20 发布日期:2022-07-22

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

Yuyun LIANG(),Yao YAO(),Xiaoqin YAN,Qingfeng GUAN

School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China

Received:2021-10-31 Accepted:2022-02-22 Online:2022-06-20 Published:2022-07-22
Contact: Yao YAO E-mail:20161000196@cug.edu.cn;yaoy@cug.edu.cn
About author:Yuyun LIANG, E-mail: 20161000196@cug.edu.cn
Supported by:
National Natural Science Foundation of China(41801306);National Natural Science Foundation of China(42171466));The Scientific Research Program of the Department of Natural Resources of Hubei Province(ZRZY2021KJ02)

摘要/Abstract

Abstract:

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.

Key words: abnormal electricity user detection, spatial autocorrelation, abnormal electricity usage in urban areas, points of interest enrichment factor, Geodetector

. [J]. 测绘学报（英文版）, 2022, 5(2): 29-37.

Yuyun LIANG,Yao YAO,Xiaoqin YAN,Qingfeng GUAN. Estimating the Spatial Variation of Electricity Consumption Anomalies and the Influencing Factors[J]. Journal of Geodesy and Geoinformation Science, 2022, 5(2): 29-37.

图/表 5

参考文献 25

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Impact factor	q-value	Impact factor	q-value
Restaurants	0.263^***	Scientific research education	0.180^***
Accommodation	0.262^***	Shopping	0.173^***
Leisure and entertainment	0.259^***	Government offices	0.164^***
Residential neighborhoods	0.256^***	Mansions	0.163^***
Financial services	0.242^***	Medical services	0.111^***
Life services	0.208^***	Travel	0.005
Public facilities	0.182^***

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

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