Next Article in Journal
Peripheralization Risk Mitigation: A Decision Support Model to Evaluate Urban Regeneration Programs Effectiveness
Previous Article in Journal
Farmer Awareness and Implementation of Sustainable Agriculture Practices in Different Types of Farms in Poland
Previous Article in Special Issue
A Power Flow Control Strategy for Hybrid Control Architecture of DC Microgrid under Unreliable Grid Connection Considering Electricity Price Constraint
Article

Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data

1
Computer Information Science, Higher Colleges of Technology, Fujairah 4114, UAE
2
Department of Electrical and Computer Engineering, COMSATS University Islamabad, Islamabad 44000, Pakistan
3
Department of Cybersecurity, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia
4
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea
*
Authors to whom correspondence should be addressed.
Sustainability 2020, 12(19), 8023; https://doi.org/10.3390/su12198023
Received: 10 August 2020 / Revised: 19 September 2020 / Accepted: 23 September 2020 / Published: 28 September 2020
(This article belongs to the Special Issue Microgrids: The Path to Sustainability)
Due to the increase in the number of electricity thieves, the electric utilities are facing problems in providing electricity to their consumers in an efficient way. An accurate Electricity Theft Detection (ETD) is quite challenging due to the inaccurate classification on the imbalance electricity consumption data, the overfitting issues and the High False Positive Rate (FPR) of the existing techniques. Therefore, intensified research is needed to accurately detect the electricity thieves and to recover a huge revenue loss for utility companies. To address the above limitations, this paper presents a new model, which is based on the supervised machine learning techniques and real electricity consumption data. Initially, the electricity data are pre-processed using interpolation, three sigma rule and normalization methods. Since the distribution of labels in the electricity consumption data is imbalanced, an Adasyn algorithm is utilized to address this class imbalance problem. It is used to achieve two objectives. Firstly, it intelligently increases the minority class samples in the data. Secondly, it prevents the model from being biased towards the majority class samples. Afterwards, the balanced data are fed into a Visual Geometry Group (VGG-16) module to detect abnormal patterns in electricity consumption. Finally, a Firefly Algorithm based Extreme Gradient Boosting (FA-XGBoost) technique is exploited for classification. The simulations are conducted to show the performance of our proposed model. Moreover, the state-of-the-art methods are also implemented for comparative analysis, i.e., Support Vector Machine (SVM), Convolution Neural Network (CNN), and Logistic Regression (LR). For validation, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), Receiving Operating Characteristics Area Under Curve (ROC-AUC), and Precision Recall Area Under Curve (PR-AUC) metrics are used. Firstly, the simulation results show that the proposed Adasyn method has improved the performance of FA-XGboost classifier, which has achieved F1-score, precision, and recall of 93.7%, 92.6%, and 97%, respectively. Secondly, the VGG-16 module achieved a higher generalized performance by securing accuracy of 87.2% and 83.5% on training and testing data, respectively. Thirdly, the proposed FA-XGBoost has correctly identified actual electricity thieves, i.e., recall of 97%. Moreover, our model is superior to the other state-of-the-art models in terms of handling the large time series data and accurate classification. These models can be efficiently applied by the utility companies using the real electricity consumption data to identify the electricity thieves and overcome the major revenue losses in power sector. View Full-Text
Keywords: data pre-processing; electricity theft; imbalance data; parameter tuning; smart grid data pre-processing; electricity theft; imbalance data; parameter tuning; smart grid
Show Figures

Figure 1

MDPI and ACS Style

Khan, Z.A.; Adil, M.; Javaid, N.; Saqib, M.N.; Shafiq, M.; Choi, J.-G. Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data. Sustainability 2020, 12, 8023. https://doi.org/10.3390/su12198023

AMA Style

Khan ZA, Adil M, Javaid N, Saqib MN, Shafiq M, Choi J-G. Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data. Sustainability. 2020; 12(19):8023. https://doi.org/10.3390/su12198023

Chicago/Turabian Style

Khan, Zahoor A., Muhammad Adil, Nadeem Javaid, Malik N. Saqib, Muhammad Shafiq, and Jin-Ghoo Choi. 2020. "Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data" Sustainability 12, no. 19: 8023. https://doi.org/10.3390/su12198023

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop