A Novel Feature Design and Stacking Approach for Non-Technical Electricity Loss Detection - Aydin, Zafer; Güngör, Vehbi Çağrı (Institute of Electrical and Electronics Engineers Inc., 2018)
Non-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.
Keywords: Anomaly Detection; Ensemble Classifiers; Feature Selection; Fraud Detection; Machine Learning; Non-Technical Electricity Loss Detection; Artificial Intelligence; Classification (Of Information); Electric Power Transmission Networks; Electric Power Utilization; Frequency Domain Analysis; Learning Systems; Smart Power Grids; Anomaly Detection; Class Imbalance Problems; Dimensionality Reduction; Electricity Loss; Electricity-Consumption; Ensemble Classifiers; Feature Selection Techniques; Fraud Detection; Feature Extraction