Aydin, ZaferGüngör, Vehbi Çağrı2025-09-252025-09-2520189781538642917https://doi.org/10.1109/ISGT-Asia.2018.8467810https://hdl.handle.net/20.500.12573/3121Non-technical electricity losses continue to jeopardize economic and social well-being of many countries. In this work, we develop machine learning classifiers that can identify anomalous electricity consumption in Turkey. Starting from weekly electricity usage data, we develop new features that capture statistical and frequency domain characteristics of the customers and their consumption patterns. We analyze the effect of reducing number of feature descriptors through dimensionality reduction and feature selection techniques. To overcome the class imbalance problem, we implement several ensemble methods and compare their prediction accuracy to those of the standard classifiers. The proposed features and combining strengths of different classifiers bring significant improvements on performance metrics, which is demonstrated through detailed simulations on shopping mall sector. We anticipate that advances in this field will contribute to the economies considerably. © 2018 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessAnomaly DetectionEnsemble ClassifiersFeature SelectionFraud DetectionMachine LearningNon-Technical Electricity Loss DetectionArtificial IntelligenceClassification (Of Information)Electric Power Transmission NetworksElectric Power UtilizationFrequency Domain AnalysisLearning SystemsSmart Power GridsAnomaly DetectionClass Imbalance ProblemsDimensionality ReductionElectricity LossElectricity-ConsumptionEnsemble ClassifiersFeature Selection TechniquesFraud DetectionFeature ExtractionConference Object10.1109/ISGT-Asia.2018.84678102-s2.0-85055548946