Understanding the abnormal electricity usage behavior of buildings is essential to enhance the resilience,efficiency,and security of urban/building energy systems while safeguarding occupant comfort.However,data refle...Understanding the abnormal electricity usage behavior of buildings is essential to enhance the resilience,efficiency,and security of urban/building energy systems while safeguarding occupant comfort.However,data reflecting such behavior are often considered as outliers,and removed or smoothed during preprocessing,limiting insights into their potential impacts.This paper proposes an abnormal behavior analysis method that identifies outliers(considering data distribution)and anomalies(considering the physical context)based on the statistical principle and domain knowledge,assessing their effects on energy supply security.A 4-quadrant graph is proposed to quantify and categorize the impacts of buildings on urban energy systems.The method is illustrated by data from 1,451 buildings in a city.Results show that the proposed method can identify abnormal data effectively.Buildings in the primary industry have more outliers,while those in the tertiary industry have more anomalies.Seven buildings affecting both the security and economy of urban energy systems are identified.The outliers rise more frequently from 8:00 to 18:00,on weekdays and in the summer and winter months.However,the anomaly distribution has a weak connection with time.Moreover,the abnormal electricity usage behavior positively correlates with outdoor air temperatures.This method provides a new perspective for identifying potential risks,managing energy usage behavior,and enhancing flexibility of the urban energy systems.展开更多
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...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.展开更多
基金funded by the program Research and Application of Demand Response Potential Evaluation Technologies Based on Massive Electricity Data(No.B31532238944)supported by the State Grid Hubei Electric Power Research Institute.
文摘Understanding the abnormal electricity usage behavior of buildings is essential to enhance the resilience,efficiency,and security of urban/building energy systems while safeguarding occupant comfort.However,data reflecting such behavior are often considered as outliers,and removed or smoothed during preprocessing,limiting insights into their potential impacts.This paper proposes an abnormal behavior analysis method that identifies outliers(considering data distribution)and anomalies(considering the physical context)based on the statistical principle and domain knowledge,assessing their effects on energy supply security.A 4-quadrant graph is proposed to quantify and categorize the impacts of buildings on urban energy systems.The method is illustrated by data from 1,451 buildings in a city.Results show that the proposed method can identify abnormal data effectively.Buildings in the primary industry have more outliers,while those in the tertiary industry have more anomalies.Seven buildings affecting both the security and economy of urban energy systems are identified.The outliers rise more frequently from 8:00 to 18:00,on weekdays and in the summer and winter months.However,the anomaly distribution has a weak connection with time.Moreover,the abnormal electricity usage behavior positively correlates with outdoor air temperatures.This method provides a new perspective for identifying potential risks,managing energy usage behavior,and enhancing flexibility of the urban energy systems.
基金National Natural Science Foundation of China(Nos.4180130642171466)The Scientific Research Program of the Department of Natural Resources of Hubei Province(No.ZRZY2021KJ02)。
文摘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.
