ANN-Based Electricity Theft Classification Technique for Limited Data Distribution Systems

Main Article Content

Monister Yaw Kwarteng
Francis Boafo Effah
Daniel Kwegyir
Emmanuel Asuming Frimpong


non-technical loss, electricity theft detection, artificial neural networks, synthetic minority oversampling


Electricity theft has been a challenge for distribution systems over the years. Theft presents a massive cost to the system operators and other issues such as transformer overloading, line loading, etc. It has become crucial for measures to be implemented to combat illegal electricity consumption. This work sought to develop an artificial neural network-based electricity theft classifier for distribution systems with limited data, i.e., systems that can only provide consumption data alone and no auxiliary data. First, a novel data pre-processing method was proposed for the systems with consumption data only. Again, synthetic minority oversampling is employed to deal with the unbalance problem in the theft detection dataset. Afterwards, an artificial neural network (ANN)-based classifier was proposed to classify customers as normal or fraudulent. The proposed method was tested on actual electricity theft data from the Electricity Company of Ghana (ECG) and its performance compared to random forest (RF) and logistic regression (LR) classifiers. The proposed ANN-based classifier performed exceptionally by producing the best results over RF and LR regarding precision, recall, F1-score, and accuracy of 99.49%, 100%, 99.75%, and 99.74%, respectively.


P. R. Babu and B. Sushma, “Operation and control of electrical distribution system with extra voltage to minimize the losses,” Proc. 2013 Int. Conf. Power, Energy Control. ICPEC 2013, pp. 165–169, 2013, doi: 10.1109/ICPEC.2013.6527643.

L. Marques, N. Silva, I. Miranda, E. Rodriges, and H. Leite, “Detection and localisation of nontechnical losses in low voltage distribution networks,” IET Conf. Publ., vol. 2016, no. CP711, 2016, doi: 10.1049/cp.2016.1079.

A. Hatem Tameem Alfarra, B. Amani Attia, and C. S. M. El Safty, “Nontechnical loss detection for metered customers in alexandria electricity distribution company using support vector machine,” Renew. Energy Power Qual. J., vol. 1, no. 16, pp. 468–474, 2018, doi: 10.24084/repqj16.353.

M. Hashatsi, C. Maulu, and M. Shuma-Iwisi, “Detection of electricity theft in low voltage networks using analytics and machine learning,” 2020 Int. SAUPEC/RobMech/PRASA Conf. SAUPEC/RobMech/PRASA 2020, 2020, doi: 10.1109/SAUPEC/RobMech/PRASA48453.2020.9041117.

M. Madrigal, J. J. Rico, and L. Uzcategui, “Estimation of Non-Technical Energy Losses in Electrical Distribution Systems,” IEEE Lat. Am. Trans., vol. 15, no. 8, pp. 1447–1452, 2017, doi: 10.1109/TLA.2017.7994791.

I. Bula, V. Hoxha, M. Shala, and E. Hajrizi, “Minimizing non-technical losses with point-to-point measurement of voltage drop between ‘SMART’ meters,” IFAC-PapersOnLine, vol. 49, no. 29, pp. 206–211, 2016, doi: 10.1016/j.ifacol.2016.11.103.

Z. Fang, Q. Cheng, L. Mou, H. Qin, H. Zhou, and J. Caol, “Abnormal electricity consumption detection based on ensemble learning,” 9th Int. Conf. Inf. Sci. Technol. ICIST 2019, pp. 175–182, 2019, doi: 10.1109/ICIST.2019.8836863.

M. M. Buzau, J. Tejedor-Aguilera, P. Cruz-Romero, and A. Gómez-Expósito, “Hybrid Deep Neural Networks for Detection of Non-Technical Losses in Electricity Smart Meters,” IEEE Trans. Power Syst., vol. 35, no. 2, pp. 1254–1263, 2020, doi: 10.1109/TPWRS.2019.2943115.

M. Nazmul Hasan, R. N. Toma, A. Al Nahid, M. M. Manjurul Islam, and J. M. Kim, “Electricity theft detection in smart grid systems: A CNN-LSTM based approach,” Energies, vol. 12, no. 17, pp. 1–18, 2019, doi: 10.3390/en12173310.

C. Tsai, K. Chiang, H. Hsieh, C. Yang, J. Lin, and Y. Chang, “Feature Extraction of Anomaly Electricity Usage Behavior in Residence Using Autoencoder,” 2022.

N. F. Avila, G. Figueroa, and C. C. Chu, “NTL Detection in electric distribution systems using the maximal overlap discrete wavelet-packet transform and random undersampling boosting,” IEEE Trans. Power Syst., vol. 33, no. 6, pp. 7171–7180, 2018, doi: 10.1109/TPWRS.2018.2853162.

“Power and Sample Size Determination.” (accessed May 31, 2022).

“Best First Search Algorithm in AI | Concept, Algorithm and Implementation.” (accessed Jan. 10, 2023).

E. Frank, M. A. Hall, and I. H. Witten, “The WEKA workbench,” Data Min., pp. 553–571, 2017, doi: 10.1016/b978-0-12-804291-5.00024-6.

“Over-sampling methods — Version 0.10.1.” https://imbalanced (accessed Jan. 10, 2023).

Most read articles by the same author(s)