Energy Theft Detection with Determine Date Theft Period for State Grid Corporation of China Dataset


  • Mali H. Alameady Computer Science Department, Faculty of Computer Science and Mathematics,University of Kufa, Najaf, Iraq
  • Loay E. George University of Information Technology and Communication, Baghdad, Iraq
  • Salah Albermany Computer Science Department,Faculty of Computer Science and Mathematics,University of Kufa, Najaf, Iraq


Electricity Theft Detection, SGCC dataset, Statistical Features, Deep Learning, DCNN, Date of Theft


Electricity theft is a major concern for electric power distribution companies. The data set from the State Grid Corporation of China (SGCC) is preprocessing; first, order dataset by date, second, remove the empty record from the dataset, third, missing values by linear interpolation, and finally, imbalanced data handling technique. Then find feature extraction including monthly average, slope, moment and standard deviation, Variance, Peak to Peak, Energy Entropy, Skewness, Crest Factor, Total harmonic distortion, Log Energy, and Kurtosis for all months with gather. After finding features of the dataset, Deep Convolution Neural Network (DCNN) applied DCNN with A map is classified using a convolution layer to extract features, followed by a softmax layer. DCNN is used for data classification in energy theft or non-theft. Finally, the calculated accuracy achieved 100%, which is quite promising in comparison to other reported categorization schemes. The number and date of the theft were then calculated for each of the records in which the theft occurred.


Download data is not yet available.


R. R. Bhat, R. D. Trevizan, R. Sengupta, X. Li, and A. Bretas; "Identifying non-technical power loss via spatial and temporal deep learning", in Proceedings of the 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), December 2016.

Bohani F. A., Suliman, A., Saripuddin, M., Sameon, S. S., Md Salleh, N. S., & Nazeri, S.; "A comprehensive analysis of supervised learning techniques for electricity theft detection", Journal of Electrical and Computer Engineering, 2021.‏ (2021).‏

Ibrahim Noor Sufyan Al-Janabi, and Belal Al-Khateeb. "Electricity-Theft Detection in Smart Grid Based on Deep Learning." Bulletin of Electrical Engineering and Informatics 10.4 (2021): 2285-2292.‏

Yao, D., Wen, M., Liang, X., Fu, Z., Zhang, K., & Yang, B. (2019). Energy theft detection with energy privacy preservation in the smart grid. IEEE Internet of Things Journal, 6(5), 7659-7669.

Hasan, M. N., Toma, R. N., Nahid, A. A., Islam, M. M., & Kim, J. M. (2019).lectricity theft detection in smart grid systems: A CNN-LSTM based approach. Energies, 12(17), 3310.‏

Cheng, G., Zhang, Z., Li, Q., Li, Y., & Jin, W. (2021). Energy theft detection in an edge data center using deep learning. Mathematical Problems in Engineering, 2021.‏

Abdulaal, M. J., Ibrahem, M. I., Mahmoud, M. M., Khalid, J., Aljohani, A. J., Milyani, A. H., & Abusorrah, A. M.,” Real-Time Detection of False Readings in Smart Grid AMI Using Deep and Ensemble Learning”., IEEE Access, 10, 47541-47556., 2022.

Khan, I. U., Javaid, N., Taylor, C. J., & Ma, X. (2022). Data Driven Analysis for Electricity Theft Attack-Resilient Power Grid. IEEE Transactions on Power Systems.‏

Ullah, A., Javaid, N., Asif, M., Javed, M. U., & Yahaya, A. S. (2022). AlexNet, AdaBoost and Artificial Bee Colony Based Hybrid Model for Electricity Theft Detection in Smart Grids. IEEE Access, 10, 18681-18694.‏

Alameady, M. H., Albermany, S., & George, L. E. (2022). Energy Theft Detection and Preventive Measures for IoT Using Machine Learning. Mathematical Statistician and Engineering Applications, 155-168.‏

Zheng, Z., Yang, Y., Niu, X., Dai, H. N., & Zhou, Y. (2017). Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids. IEEE Transactions on Industrial Informatics, 14(4), 1606-1615.‏

Shuan Li , Yinghua Han , Xu Yao, Song Yingchen, Jinkuan Wang, and Qiang Zhao, Electricity Theft Detection in Power Grids with Deep Learning and Random Forests, Journal of Electrical and Computer Engineering Volume 2019, Article ID 4136874, 12 pages.

Hussain F., Hussain R. Hassan, S. A. & Hossain E.," Machine learning in IoT security: Current solutions and future challenges". IEEE Communications Surveys & Tutorials, 22(3), 1686-1721, 2020.‏

Hu W., Yang, Y. Wang, J. Huang, X. & Cheng Z.," Understanding electricity-theft behavior via multi-source data". In Proceedings of The Web Conference 2020 (pp. 2264-2274), 2020, April.

Finardi P., Campiotti I., Plensack G., de Souza R. D., Nogueira R., Pinheiro, G. & Lotufo R.," Electricity theft detection with self-attention",. arXiv preprint arXiv:2002.06219, 2020.

Ullah, A.; Javaid, N.; Samuel, O.; Imran, M.; Shoaib, M. CNN and GRU based deep neural network for electricity theft detection to secure smart grid. In Proceedings of the 2020 International Wireless Communications and Mobile Computing (IWCMC), Limassol, Cyprus, 15–19 June 2020; pp. 1598–1602.

Asif, M., Nazeer, O., Javaid, N., Alkhammash, E. H., & Hadjouni, M. (2022). Data Augmentation Using BiWGAN, Feature Extraction and Classification by Hybrid 2DCNN and BiLSTM to Detect Non-Technical Losses in Smart Grids. IEEE Access, 10, 27467-27483.‏

Rouzbahani, H. M., Karimipour, H., & Lei, L. (2020, October). An ensemble deep convolutional neural network model for electricity theft detection in smart grids. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 3637-3642). IEEE.‏

Javaid, N., Qasim, U., Yahaya, A. S., Alkhammash, E. H., & Hadjouni, M. (2022). Non-technical losses detection using autoencoder and bidirectional gated recurrent unit to secure smart grids. IEEE Access.‏

Badawi, S. A., Guessoum, D., Elbadawi, I., & Albadawi, A. (2022). A Novel Time-Series Transformation and Machine-Learning-Based Method for NTL Fraud Detection in Utility Companies. Mathematics, 10(11), 1878.‏

monthly electric power consumption pattern




How to Cite

M. H. . Alameady, L. E. . George, and S. . Albermany, “Energy Theft Detection with Determine Date Theft Period for State Grid Corporation of China Dataset”, Int J Intell Syst Appl Eng, vol. 11, no. 1s, pp. 01–13, Jan. 2023.