A novel and effective method based deep learning model for detecting non-technical electricity losses

Israa Mohammed Ridha Baldawi, Timur İnan


This study focused on non-technical electricity loss detection. As mentioned, non-technical losses (NTLs) affect utilities and economies financially. Electricity theft, fraud, and metering issues can create NTLs. NTL generate most distribution losses in electrical power networks, costing utilities a lot. NTL detection approaches are data-focused, network-oriented, or hybrid. Data-oriented writing dominated this analysis. After data collection and cleaning and labeling the unlabeled dataset with a target, a methodology was supplied that used four machine learning techniques random forest, decision tree, KNN, and logistic regression and four neural network models-DNN, CNN, CNN-LSTM, and CNN-GRU. The CNN and DNN model have the best accuracy, stability, fast learning, and training time.


classification; deep learning; machine learning; non-technical electricity; prediction

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DOI: http://doi.org/10.11591/ijpeds.v14.i4.pp2464-2473


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