Open Access

Electric Theft Detection Based on Multilayer Backpropagation Neural Network Optimized by Sine Chaotic Genetic Algorithm

   | Apr 20, 2024

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Al-Shetwi, A.Q., Hannan, M.A., Jern, K.P., Mansur, M., & Mahlia, T.M.I. (2020). Grid-connected renewable energy sources: Review of the recent integration requirements and control methods. Journal of Cleaner Production, 253, 119831. Search in Google Scholar

Fan, X., Liu, B., Liu, J., Ding, J., Han, X., Deng, Y., Lv, X., Chen, B., Hu, W., & Zhong, C. (2020). Battery technologies for grid-level large-scale electrical energy storage. Transactions of Tianjin University, 26, 92-103. Search in Google Scholar

Takiddin, A., Ismail, M., Zafar, U., & Serpedin, E. (2021). Robust electricity theft detection against data poisoning attacks in smart grids. IEEE Transactions on Smart Grid, 12(3), 2675-2684. Search in Google Scholar

Komolafe, O.M., & Udofia, K.M. (2020). A technique for electrical energy theft detection and location in low voltage power distribution systems. Engineering and applied sciences, 5(2), 41-49. Search in Google Scholar

Liu, S., Liang, Y., Wang, J., Jiang, T., Sun, W., & Rui, Y. (2020). Identification of stealing electricity based on big data analysis. Energy Reports, 6, 731-738. Search in Google Scholar

Liu, Y., Liu, T., Sun, H., Zhang, K., & Liu, P. (2020). Hidden electricity theft by exploiting multiple-pricing scheme in smart grids. IEEE Transactions on Information Forensics and Security, 15, 2453-2468. Search in Google Scholar

Ponnusamy, V.K., Kasinathan, P., Madurai, E.R., Ramanathan, V., Anandan, R.K., Subramaniam, U., Ghosh, A., & Hossain, E. (2021). A comprehensive review on sustainable aspects of big data analytics for the smart grid. Sustainability, 13, 13322. Search in Google Scholar

Javaid, N., Jan, N., & Javed, M.U. (2021). An adaptive synthesis to handle imbalanced big data with deep siamese network for electricity theft detection in smart grids. Journal of Parallel and Distributed Computing, 153, 44-52. Search in Google Scholar

Shrestha, A., & Mahmood, A. (2019). Review of deep learning algorithms and architectures. IEEE access, 7, 5304053065. Search in Google Scholar

Shen, Z., Shehzad, A., Chen, S., Sun, H., & Liu, J. (2020). Machine learning based approach on food recognition and nutrition estimation. Procedia Computer Science, 174, 448-453. Search in Google Scholar

Li, S., Han, Y., Yao, X., Song, Y., Wang, J., & Zhao, Q. (2019). Electricity theft detection in power grids with deep learning and random forests. Journal of Electrical and Computer Engineering, 2019, 1-12. Search in Google Scholar

Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M.P., Shyu, M., Chen, S., & Iyengar, S.S. (2018). A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys (CSUR), 51(5), 1-36. Search in Google Scholar

Alzubaidi, L., Zhang, J., Humaidi, A.J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaria, J., Mohammed, A., Fadhel, M.A., & Laith, F. (2021). Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 8, 1-74. Search in Google Scholar

Sun, R. (2019). Optimization for deep learning: theory and algorithms. arXiv preprint arXiv:1912.08957. Search in Google Scholar

Ahmad, T., Chen, H., Wang, J., & Guo, Y. (2018). Review of various modeling techniques for the detection of electricity theft in smart grid environment. Renewable and Sustainable Energy Reviews, 82, 2916-2933. Search in Google Scholar

Otuoze, A.O., Mustafa, M.W., & Larik, R.M. (2018). Smart grids security challenges: Classification by sources of threats. Journal of Electrical Systems and Information Technology, 5(3), 468-483. Search in Google Scholar

Singh, S.K., Bose, R., & Joshi, A. (2019). Energy theft detection for AMI using principal component analysis based reconstructed data. IET Cyber-Physical Systems: Theory and Applications, 4(2), 179-185. Search in Google Scholar

Zheng, K., Chen, Q., Wang, Y., Kang, C., & Xia, Q. (2019). A novel combined data-driven approach for electricity theft detection. IEEE Transactions on Industrial Informatics, 15(3), 1809-1819. Search in Google Scholar

Khan, Z.A., Adil, M., Javaid, N., Saqib, M.N., Shafiq, M., & Choi, J.G. (2020). Electricity theft detection using supervised learning techniques on smart meter data. Sustainability, 12, 8023. Search in Google Scholar

Zheng, Z., Yang, Y., Niu, X., Dai, H.N., & Zhou, Y. (2018). Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids. IEEE Transactions on Industrial Informatics, 14(4), 1606-1615. Search in Google Scholar

Gunturi, S.K., & Sarkar, D. (2021). Ensemble machine learning models for the detection of energy theft. Electric Electric systems Research, 192, 106904. Search in Google Scholar

Hussain, S., Mustafa, M.W., Jumani, T.A., Baloch, S.K., Alotaibi, H., Khan, L., & Khan, A. (2021). A novel feature engineeredCatBoost-based supervised machine learning framework for electricity theft detection. Energy Reports, 7, 4425-4436. Search in Google Scholar

Cervantes, J., Garcia-Lamont, F., Rodriguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189-215. Search in Google Scholar

Haq, E.U., Huang, J., Xu, H., Li, K., & Ahmad, F. (2021). A hybrid approach based on deep learning and support vector machine for the detection of electricity theft in power grids. Energy Reports, 7, 349-356. Search in Google Scholar

Banga, A., Ahuja, R., & Sharma, S.C. (2022). Accurate detection of electricity theft using classification algorithms and Internet of Things in smart grid. Arabian Journal for Science and Engineering, 47(8), 9583-9599. Search in Google Scholar

Ahmad, T., Chen, H., Wang, J., & Guo, Y. (2018). Review of various modeling techniques for the detection of electricity theft in smart grid environment. Renewable and Sustainable Energy Reviews, 82, 2916-2933. Search in Google Scholar

Ibrahim, N., Al-Janabi, S., & Al-Khateeb, B. (2021). Electricity-theft detection in smart grid based on deep learning. Bulletin of Electrical Engineering and Informatics, 10(4), 2285-2292. Search in Google Scholar

Lepolesa, L.J., Achari, S., & Cheng, L. (2022). Electricity theft detection in smart grids based on deep neural network. IEEE Access, 10, 39638-39655. Search in Google Scholar

Aslam, Z., Javaid, N., Ahmad, A., & Gulfam, S.M. (2020). A combined deep learning and ensemble learning methodology to avoid electricity theft in smart grids. Energies, 13, 5599. Search in Google Scholar

Zafar, M.H., Bukhari, S.M.S., Houran, M.A., Moosavi, S.K.R., Mansoor, M., Al-Tawalbeh, N., & Sanfilippo, F. (2023). Step towards secure and reliable smart grids in Industry 5.0: A federated learning assisted hybrid deep learning model for electricity theft detection using smart meters. Energy Reports, 10, 3001-3019. Search in Google Scholar

eISSN:
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Language:
English
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Volume Open
Journal Subjects:
Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics