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Predictive Model for the Detection of Electrical Energy Theft
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Predictive Model for the Detection of Electrical Energy Theft. (2024). Journal of Scientific and Technological Research Industrial, 5(1), 02-10.


Illegal access and use of electricity, known as electricity theft, represents a significant threat to the energy industry and society as a whole. This fraudulent phenomenon undermines the integrity of the electrical system, negatively affects service providers and has serious economic, social and environmental consequences. Furthermore, abnormal electricity consumption also poses significant challenges in terms of early detection of irregularities and optimization of energy consumption. In this research, the causes and motivations that drive electricity theft were analyzed. Likewise, the various methods used by offenders to manipulate energy meters and hide their fraudulent activities were examined, highlighting the need for innovative solutions to combat this problem. Of all the existing solutions, we focus on the analysis and prediction of consumption using Machine Learning techniques. The use of algorithmic machine learning models is explored as a key tool to detect and prevent electricity theft and anomalous consumption. Presenting a significant improvement in the detection of electrical energy theft.

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Amin, S., Schwartz, G., Cardenas, A., & Sastry, S. (2015). Gametheoretic models of electricity theft detection in smart utility networks: Providing new capabilities with advanced metering infrastructure. IEEE Control. Syst. Mag.

Avila, N., Figueroa, G., & Chu, C. (2018). NTL detection in electric distribution systems using the maximal overlap discrete wavelet-packet transform and random under sampling boosting. IEEE Trans. Power Syst.

Biswas, P., Cai, H., Zhou, B., Chen, B., Mashima, D., & Zheng, V. (2019). Electricity Theft Pinpointing through Correlation Analysis of Master and Individual Meter Readings. IEEE Trans.

Buzau, M., & Tejedor, J. (2018). Detection of non-technical losses using smart meter data and supervised learning. EEE Trans. Smart Grid.

Buzau, M., Tejedor, J., Cruz, P., & Gomez, A. (2019). Hybrid deep neural networks for detection of non-technical losses in electricity smart meters. IEEE Trans. Power Syst.

Ding, N., Ma, H., Gao, H., & Tan, G. (2019). Real-time anomaly detection based on long short-Term memory and Gaussian Mixture Model. Comput. Electr. Eng.

ENEL. (2021). Hurto de Energía - Obtenido de

Glauner, P., Valtchev, P., Glaeser, C., Dahringer, N., State, R., & Duarte, D. (2018). Non-Technical Losses in the 21st Century: Causes, Economic Effects, Detection and Perspectives. Obtenido de

Hammerschmitt, B. (2020). Non-Technical Losses Review and Possible Methodology Solutions. Proceedings - 2020 6th International Conference on Electric Power and Energy Conversion Systems, EPECS, 64–68. doi:10.1109/EPECS48981.2020.9304525

Hasan, M., Toma, R., Nahid, A., Islam, M., & Kim, J. (2019). Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach.

Jamil, A., Alghamdi, T., Khan, Z., Javaid, S., Haseeb, A., Wadud, Z., & Javaid, N. (2019). An Innovative Home Energy Management Model with Coordination among Appliances using Game Theory. Sustainability.

Jiménez, R., Serebrisky, T., & Mercado, J. (2014). Power Lost: Sizing Electricity Losses in Transmission and Distribution Systems in Latin America and the Caribbean. Inter-American Development Bank. doi:10.18235/0001046.

Leite, J., & Mantovani, J. (2016). Detecting and locating non-technical losses in modern distribution networks. IEEE Trans. Smart Grid.

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. Electr. Comput. Eng.

Lydia, M., Kumar, G., & Levron, Y. (2019). Detection of Electricity Theft based on Compressed Sensing. In Proceedings of the 2019 5th International Conference on Advanced Computing and Communication Systems (ICACCS) IEEE. Coimbatore, India.

McDaniel, P., & McLaughlin, S. (2009). Security and privacy challenges in the smart grid. IEEE Secur. Priv.

Ramos, C., Rodrigues, D., de Souza, A., & Papa, J. (2016). On the study of commercial losses in Brazil: a binary black hole algorithm for theft characterization. IEEE Trans. Smart Grid.

Razavi, r., & Fleury, m. (2019). Socio-economic predictors of electricity theft in developing countries: An Indian case study. Energy Sustain. Dev.

Razavi, R., Gharipour, A., Fleury, M., & Akpan, I. (2019). A practical feature-engineering framework for electricity theft detection in smart grids. Appl. Energy.

Saeed, M., Mustafa, M., Sheikh, U., Jumani, T., & Mirjat, N. (2019). Ensemble Bagged Tree Based Classification for Reducing Non-Technical Losses in Multan Electric Power Company of Pakistan.

Savian, F., Siluk, J., Garlet, T., Nascimento, F., Pinheiro, J., & Vale, Z. (2021). Non-technical losses: A systematic contemporary article review. Renewable and Sustainable Energy Reviews. doi:10.1016/J.RSER.2021.111205

Wang, S., & Chen, H. (2019). A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network. Appl. Energy.

Zahoor, A., Muhammad, A., Nadeem, J., Malik, S., Muhammad, S., & Jin-Ghoo, C. (2020). Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data. MDPI.

Zheng, K., Chen, Q., Wang, Y., Kang, C., & Xia, Q. (2019). A novel combined data-driven approach for electricity theft detection. IEEE Trans. Ind. Inform.

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