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Using machine learning ensemble method for detection of energy theft in smart meters

AbstractElectricity theft is a primary concern for utility providers, as it leads to substantial financial losses. To address the issue, a novel extreme gradient boosting (XGBoost)‐based model utilizing the consumers’ electricity consumption patterns for analysis is proposed for electricity theft detection (ETD). To remove the imbalance in the real‐world electricity consumption dataset and ensure an even distribution of theft and non‐theft data instances, six different artificially created theft attacks were used. Moreover, the utilization of the XGBoost algorithm for classification, especially to identify malicious instances of electricity theft, yielded commendable accuracy rates and a minimal occurrence of false positives. The proposed model identifies electricity theft specific to the regions, utilizing electricity consumption parameters, and other variables as input features. The authors’ model outperformed existing benchmarks like k‐neural networks, light gradient boost, random forest, support vector machine, decision tree, and AdaBoost. The simulation results using the false attacks for balancing the dataset have improved the proposed model's performance, achieving a precision, recall, and F1‐score of 96%, 95%, and 95%, respectively. The results of the detection rate and the false positive rate (FPR) of the proposed XGBoost‐based detection model have achieved 96% and 3%, respectively.
- Lovely Professional University India
- Khon Kaen University Thailand
- Lovely Professional University India
- Khon Kaen University Thailand
- University of Vassa Finland
TK1001-1841, Artificial intelligence, Support vector machine, False positive paradox, Fraud Detection, Handling Imbalanced Data in Classification Problems, fi=Tietotekniikka|en=Computer Science|, TK3001-3521, electricity supply industry, Boosting (machine learning), Production of electric energy or power. Powerplants. Central stations, Engineering, Electricity, Artificial Intelligence, Ensemble learning, Machine learning, FOS: Electrical engineering, electronic engineering, information engineering, Decision tree, Leak Detection, False positive rate, Electrical and Electronic Engineering, Civil and Structural Engineering, ta113, Distribution or transmission of electric power, ta213, Electricity Theft Detection in Smart Grids, Design and Management of Water Distribution Networks, AdaBoost, smart meters, 006, Computer science, Detection, Electrical engineering, Physical Sciences, Computer Science, Gradient boosting, Electricity Theft, Random forest
TK1001-1841, Artificial intelligence, Support vector machine, False positive paradox, Fraud Detection, Handling Imbalanced Data in Classification Problems, fi=Tietotekniikka|en=Computer Science|, TK3001-3521, electricity supply industry, Boosting (machine learning), Production of electric energy or power. Powerplants. Central stations, Engineering, Electricity, Artificial Intelligence, Ensemble learning, Machine learning, FOS: Electrical engineering, electronic engineering, information engineering, Decision tree, Leak Detection, False positive rate, Electrical and Electronic Engineering, Civil and Structural Engineering, ta113, Distribution or transmission of electric power, ta213, Electricity Theft Detection in Smart Grids, Design and Management of Water Distribution Networks, AdaBoost, smart meters, 006, Computer science, Detection, Electrical engineering, Physical Sciences, Computer Science, Gradient boosting, Electricity Theft, Random forest
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).30 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
