Call for Paper 25 September, 2023. Please submit your manuscript via online system or email at editor@ijew.io

ISSN E 2409-2770
ISSN P 2521-2419

A Systematic Review on Supervised Learning Techniques in Electricity Theft Detection


Sohrab Alam, Majid Ashraf, Salman Alam


Vol. 9, Issue 02, PP. 22-27, February 2022

DOI

Keywords: Data imbalance, overfitting, Non Technical losses, overfitting

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In smart grids the term non-technical losses impose challenges to perform classification, optimization, data analytics and regression analysis in almost all areas of real world research. The primary raw data suffers from an un-uniform distribution of one class over the other class in case of machine(ML) and Deep learning(DL). The data imbalanced, Overfiting, high False positive rate, handling of high dimensional data and generalization error impose chalanges for the industries and academia to detect the thieves of electricity efficiently. The aim of this article also to present comparative analysis of the approaches from the reference of data preprocessing, algorithms, models and hybrid paradigms for the coeval imbalance data analysis, overfitting, genersalization error, and high Fasle Possitive rate and the comparative study of different techniques and its application area.


  1. Sohrab Alam, sohrab.alamuet@gmail.com, Electrical Engineering Department, University of Engineering and Technology, Peshawar, Pakistan.
  2. Majid Ashraf, , Electrical Engineering Department, University of Engineering and Technology, Peshawar, Pakistan.
  3. Salman Alam, , Computer Science Department, COMSATS University, Abbottabad, Pakistan.

Sohrab Alam Majid Ashraf Salman Alam A Systematic Review on Supervised Learning Techniques in Electricity Theft Detection International Journal of Engineering Works Vol. 9 Issue 02 PP. 22-27 February 2022 https://doi.org/10.34259/ijew.22.9022227.


[1]     Jamil, A., Alghamdi, T. A., Khan, Z. A., Javaid, S., Haseeb, A., Wadud, Z., and Javaid, N. (2019). An Innovative Home Energy Management Model with Coordination among Appliances using Game Theory. Sustainability, pp. 1-23.

[2]     Kaur, H., Pannu, H. S., and Malhi, A. K. (2019). A systematic review on imbalanced data challenges in machine learning: Applications and solutions. ACM Computing Surveys (CSUR), pp. 1-36.

[3]      Sana Mujeeb and Nadeem Javaid, ESAENARX and DE-RELM: Novel Schemes for Big Data Predictive Analytics of Electricity Load and Price, Sustainable Cities and Society, pp. 2210-6707

[4]     Hussain, Z., Memon, S., Shah, R., Bhutto, Z.A. and Aljawarneh, M., 2016. Methods and Techniques of Electricity Thieving in Pakistan. Journal of Power and Energy Engineering, pp. 1-10.

[5]     Jokar, P., Arianpoo, N., and Leung, V. C. (2015). Electricity theft detection in AMI using customers consumption patterns. IEEE Transactions on Smart Grid, pp. 216- 226.

[6]     Leite, J. B., and Mantovani, J. R. S. (2016). Detecting and locating non-technical losses in modern distribution networks. IEEE Transactions on Smart Grid, pp. 1023- 1032.

[7]     Jamil, A., Alghamdi, T. A., Khan, Z. A., Javaid, S., Haseeb, A., Wadud, Z., and Javaid, N. (2019). An Innovative Home Energy Management Model with Coordination among Appliances using Game Theory. Sustainability, pp. 1-23.

[8]     Glauner, P., Meira, J. A., Valtchev, P., State, R and Bettinger, F. (2016). The challenge of non-technical loss detection using artificial intelligence: A survey. pp. 1-16.

[9]     Buzau, M.M, Tejedor-Aguilera, J, Cruz-Romero, P, Gomez Exposito, 2019. Hybrid deep neural networks for detection of non-technical losses in electricity smart meters. IEEE Trans. Power System, pp. 12541263

[10]  Zheng, Z., Yang, Y., Niu, X., Dai, H. N., and Zhou, Y. (2017). Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids. IEEE Transactions, pp. 16061615.

[11]  Adil, M., Javaid, N., Qasim, U., Ullah, I., Shafiq, M., and Choi, J. G. (2020). LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection. Applied Sciences. pp. 1-21.

[12]  Ramos, C. C., Rodrigues, D., de Souza, A. N., Papa, J. P. (2016). On the study of commercial losses in Brazil: a binary black hole algorithm for theft characterization. IEEE Transactions on Smart Grid, pp. 676-683

[13]  Buzau, M.M., Tejedor-Aguilera, J., Cruz-Romero, P. and Gomez Exposito, A., 2018. Detection of non-technical losses using smart meter data and supervised learning. IEEE Transactions on Smart Grid, 10(3), pp. 2661-2670.

[14]  Li, S.; Han, Y.; Yao, X.; Yingchen, S.; Wang, J.; Zhao, Q, 2019. Electricity Theft Detection in Power Grids with Deep Learning and Random Forests. J. Electr. Comput. Eng, pp. 1-12

[15]  Gul, H., Javaid, N., Ullah, I., Qamar, A. M., Afzal, M. K., and Joshi, G. P. (2020). Detection of Non-Technical Losses using SOSTLink and Bidirectional Gated Recurrent Unit to Secure Smart Meters. Applied Sciences, pp. 1-21.

[16]  Fenza, G.; Gallo, M.; Loia, V. Drift-aware methodology for anomaly detection in smart grid. IEEE Access 2019, pp. 96459657

[17]  Qin, H., Zhou, H., and Cao, J. (2020). Imbalanced Learning Algorithm based Intelligent Abnormal Electricity Consumption Detection. Neuro computing, pp. 112-132.

[18]  Avila, N.F., Figueroa, Chu, (2018). NTL detection in electric distribution systems using the maximal overlap discrete wavelet-packet transform and random under sampling boosting. IEEE Transactions on Power Systems, pp. 7171-7180.

[19]  Buzau, M. M., Tejedor-Aguilera, J., Cruz-Romero, P., and Gomez-Exp osito, A. (2018). Detection of non-technical losses using smart meter data and supervised learning. IEEE Transactions on Smart Grid, 10(3), pp: 2661-2670.

[20]  Wang, S., and Chen, H. (2019). A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network. Applied energy, pp. 1126-1140.

[21]  Fenza, G.; Gallo, M.; Loia, V. Drift-aware methodology for anomaly detection in smart grid. IEEE Access 2019, pp. 96459657

[22]  Buzau, M.M., Tejedor-Aguilera, J., Cruz-Romero, P. and Gomez Exposito, A., 2018. Detection of non-technical losses using smart meter data and supervised learning. IEEE Transactions on Smart Grid, 10(3), pp. 2661-2670.

[23]  Avila, N.F., Figueroa, Chu, (2018). NTL detection in electric distribution systems using the maximal overlap discrete wavelet-packet transform and random under sampling boosting. IEEE Transactions on Power Systems, pp. 7171-7180.

[24]  Ding, N., Ma, H., Gao, H., Ma, Y., and Tan, G. (2019). Real-time anomaly detection based on long short-Term memory and Gaussian Mixture Model. Computers and Electrical Engineering, pp. 1-11.

[25]  Li, W., Logenthiran, T, Phan, V.T, Woo, W.L, 2019. A novel smart energy theft system (SETS) for IoT-based smart home. IEEE Int. Things J, pp. 55315539.

[26]  Hasan, M., Toma, R. N., Nahid, A. A., Islam, M. M., Kim, J. M. (2019). Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach. Energies. pp. 1-18.

[27]  Li, S.; Han, Y.; Yao, X.; Yingchen, S.; Wang, J.; Zhao, Q, 2019. Electricity Theft Detection in Power Grids with Deep Learning and Random Forests. J. Electr. Comput. Eng, pp. 1-12.

[28]  Toma, R. N., Hasan, M. N., Nahid, A. A., and Li, B. (2019, May). Electricity theft detection to reduce non-technical loss using support vector machine in smart grid. In 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) pp. 1-6.

[29]  Punmiya, R., and Choe, S. (2019). Energy theft detection using gradient boosting theft detector with feature engineering, pp. 23262329.

[30]  Alam S, Ashraf M, Alam S,Khan M. A hybrid SMOTE-ENN and EHO based CNN for electricity theft detection. Applied Sciences.

[31]  Ding, L., Li, H., Hu, C., Zhang, W., and Wang, S. (2018). Alexnet Feature Extraction And Multi-Kernel Learning for Object-Oriented Classification. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, pp. 277-281.

[32]  Birajdar, U., Gadhave, S., Chikodikar, S., Dadhich, S., and Chiwhane, S. (2020). Detection and Classification of Diabetic Retinopathy Using AlexNet Architecture of Convolutional Neural Networks. In Proceeding of International Conference on Computational Science and Applications, pp. 245-253.

[33]  Pedamonti, D. (2018). Comparison of non-linear activation functions for deep neural networks on MNIST classification task. pp. 1-5.

[34]  Mussab Alaa, A.A. Zaidan,*, B.B. Zaidan, Mohammed Talal, M.L.M. Kiah A review of smart home applications based on Internet of ThingsJournal of Network and Computer Applications ELSEVIER- 2017.

[35]  G. Wang, S. Deb and L. Coelho, Elephant Herding Optimization, 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI), 2015.

[36]  Saman M. Almufti,Renas Rajab Asaad,Baraa W Salim Review on Elephant Herding Optimization Algorithm Performance in Solving Optimization ProblemsOctober 2018 International Journal of Engineering and Technology 7(4):6109-6114

[37]  Wenye Wang, Zhuo Lu Cyber security in the Smart Grid Survey and challenges Computer Networks ELSEVIER-2013.

[38]  Y.LeCun,B.Boser,J.S.Denker,D.Henderson,R.E.Howard,W.Hubbard,L.D.Jackel Backpropagation Applied to Handwritten Zip Code Recognition AT & T Bell Laboratories Holmdel,NJ 07733 US