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Employing Feature Extraction, Feature Selection, and Machine Learning to Classify Electricity Consumption as Normal or Electricity Theft

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Abstract

One of the main causes of revenue loss in the energy sector across the globe has been non-technical losses. Electricity theft is a non-technical loss that affects the power supply’s quality. Detecting electricity theft is crucial for conserving energy and making use of it effectively. This research proposes a method through which higher accuracy in electricity theft detection can be obtained using fewer features. Different feature extraction and feature selection techniques are examined to find the best method for selecting the features that are more relevant in electricity theft detection. Various experiments are carried out using feature selection and feature extraction methods, such as mutual information, low variance filtering, and Principal Component Analysis. Various machine learning-based classifiers are used that include Random Forest, Support Vector Machine, K-Nearest Neighbours, Naive Bayes, and Decision Tree. Results of the experiments are tabulated based on standard performance measures, namely accuracy, precision, recall, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC) score. According to experimental findings, the Random Forest classifier with 30 components for PCA outperformed other methods by producing the best accuracy of 95.82%, recall of 0.938, and AUC-ROC-score of 0.989.

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Funding

This manuscript’s preparation is not supported financially in any way.

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Author notes

  1. Rajesh Nayak and C. D. Jaidhar have equally contributed to this work.

Authors and Affiliations

  1. Department of Information Technology, National Institute of Technology Karnataka, Surathkal, Mangalore, Karnataka, 575025, India

    Rajesh Nayak & C. D. Jaidhar

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  1. Rajesh Nayak
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  2. C. D. Jaidhar
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Contributions

Rajesh Nayak: Conceptualization of this study, Data collection, Methodology, Original draft preparation. Jaidhar C.D.: Conceptualization of this study, Methodology, Review and Editing, Supervision.

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Correspondence to Rajesh Nayak.

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This article is part of the topical collection “Research Trends in Computational Intelligence” guest edited by Anshul Verma, Pradeepika Verma, Vivek Kumar Singh and S. Karthikeyan.

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Nayak, R., Jaidhar, C.D. Employing Feature Extraction, Feature Selection, and Machine Learning to Classify Electricity Consumption as Normal or Electricity Theft. SN COMPUT. SCI. 4, 483 (2023). https://doi.org/10.1007/s42979-023-01911-0

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