Improving Performance of the Convolutional Neural Networks for Electricity Theft Detection by using Cheetah Optimization Algorithm

Majlesi Journal of Electrical Engineering

مقاله 8، دوره 16، شماره 4، اسفند 2022، صفحه 103-115   

نوع مقاله: Reseach Article

شناسه دیجیتال (DOI): 10.30486/mjee.2022.696521

نویسندگان

hassan ghaedi¹؛ Seyed Reza Kamel Tabbakh^{* 2}؛ reza ghaemi³

¹Department of Computer, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran

²Department of Computer, Mashhad Branch, Islamic Azad University, Mashhad, Iran

³Department of Computer, Quchan Branch, Islamic Azad University, Quchan, Iran

چکیده

Today, electricity theft is one of the main challenges for energy distribution and transmission companies around the world. Early detection of abnormal consumers can prevent security and financial losses. Extensive research studies have been done to detect electricity theft by analyzing customer consumption patterns. Today, one of the most widely used methods is convolutional neural networks (CNNs). These networks contain a large number of hyper-parameters.  The accuracy of these networks is low in most studies due to the lack of attention to the adjustment of these hyper-parameters.  Network accuracy and achieving a robust learning model are influenced by the optimal adjusting of these hyper-parameters, which requires exploring a complex and large search space. Meta-heuristic-based search methods are suitable for solving these problems. Therefore, the main contribution of this paper is to use the high ability of the cheetah optimization algorithm (CHOA) to optimally extract CNN hyper-parameters. In this paper, in order to balance the dataset, abnormal samples are created using artificial attacks and added to the dataset. Also, in order to increase the accuracy of the network, abnormal data are clustered using the CHOA algorithm. ISSDA dataset is used to test and evaluate the results. Based on the results obtained and comparing them with the other works, it was proved that the proposed framework with high accuracy identifies abnormal consumers.

کلیدواژه‌ها

Data mining؛ Classification؛ Electricity Theft Detection؛ Convolutional Neural Network (CNN)

مراجع

• Jokar, N. Arianpoo, and V. C. M. Leung, “Electricity theft detection in AMI using customers’ consumption patterns,” IEEE Trans. Smart Grid, Vol. 7, No. 1, pp. 216–226, 2016, doi: 10.1109/TSG.2015.2425222.
• Ghaedi, S. R. K. Tabbakh, and R. Ghaemi, “Improving Electricity Theft Detection using Combination of Improved Crow Search Algorithm and Support Vector Machine,” Majlesi J. Electr. Eng., vol. 15, no. 4 SE-Articles, Dec. 2021, doi: https://doi.org/10.52547/mjee.15.4.63.
• Askarzadeh, “A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm,” Comput. Struct., Vol. 169, pp. 1–12, 2016, doi: 10.1016/j.compstruc.2016.03.001.
• Razavi, A. Gharipour, M. Fleury, and I. J. Akpan, “A practical feature-engineering framework for electricity theft detection in smart grids,” Appl. Energy, Vol. 238, No. December 2018, pp. 481–494, 2019, doi: 10.1016/j.apenergy.2019.01.076.
• Feng, J. Huang, W. H. Tang, and M. Shahidehpour, “Data mining for abnormal power consumption pattern detection based on local matrix reconstruction,” Int. J. Electr. Power Energy Syst., Vol. 123, No. February, p. 106315, 2020, doi: 10.1016/j.ijepes.2020.106315.
• Ghaedi, S. R. Kamel Tabbakh Farizani, and R. Gaemi, “A Novel Meta-heuristic Framework for Solving Power Theft Detection Problem: Cheetah Optimization Algorithm,” Int. J. Ind. Electron. Control Optim., Vol. 5, No. 1, pp. 63–76, 2022, doi: 10.22111/ieco.2022.39528.1370.
• Ullah, N. Javaid, A. S. Yahaya, T. Sultana, F. A. Al-Zahrani, and F. Zaman, “A Hybrid Deep Neural Network for Electricity Theft Detection Using Intelligent Antenna-Based Smart Meters,” Wirel. Commun. Mob. Comput., Vol. 2021, p. 9933111, 2021, doi: 10.1155/2021/9933111.
• M. Ibrahim, S. T. F. Al-Janabi, and B. Al-Khateeb, “Electricity-theft detection in smart grids based on deep learning,” Bull. Electr. Eng. Informatics, Vol. 10, No. 4, pp. 2285–2292, 2021, doi: 10.11591/EEI.V10I4.2875.
• 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.
• Li, Y. Han, X. Yao, S. Yingchen, J. Wang, and Q. Zhao, “Electricity Theft Detection in Power Grids with Deep Learning and Random Forests,” J. Electr. Comput. Eng., Vol. 2019, 2019, doi: 10.1155/2019/4136874.
• Kocaman and V. Tümen, “Detection of electricity theft using data processing and LSTM method in distribution systems,” Sadhana - Acad. Proc. Eng. Sci., Vol. 45, No. 1, 2020, doi: 10.1007/s12046-020-01512-0.
• M. Rouzbahani, H. Karimipour, and L. Lei, “An Ensemble Deep Convolutional Neural Network Model for Electricity Theft Detection in Smart Grids,” Conf. Proc. - IEEE Int. Conf. Syst. Man Cybern., Vol. 2020-Octob, pp. 3637–3642, 2020, doi: 10.1109/SMC42975.2020.9282837.
• Zheng, Y. Yang, X. Niu, H. N. Dai, and Y. Zhou, “Wide and Deep Convolutional Neural Networks for Electricity-Theft Detection to Secure Smart Grids,” IEEE Trans. Ind. Informatics, Vol. 14, No. 4, pp. 1606–1615, 2018, doi: 10.1109/TII.2017.2785963.
• Chandel and T. Thakur, “Smart Meter Data Analysis for Electricity Theft Detection using Neural Networks,” Adv. Sci. Technol. Eng. Syst. J., Vol. 4, No. 4, pp. 161–168, 2019, doi: 10.25046/aj040420.
• Feng et al., “A novel electricity theft detection scheme based on text convolutional neural networks,” Energies, Vol. 13, No. 21, pp. 1–17, 2020, doi: 10.3390/en13215758.
• Maamar and K. Benahmed, “A Hybrid Model for Anomalies Detection in AMI System Combining K-means Clustering and Deep Neural Network,” Comput. Mater. Contin., Vol. 60, No. 1, pp. 15–39, 2019, doi: 10.32604/cmc.2019.06497.
• Fouad, M. Alfonse, M. Roushdy, and A. B. M. Salem, “Hyper-parameter optimization of convolutional neural network based on particle swarm optimization algorithm,” Bull. Electr. Eng. Informatics, Vol. 10, No. 6, pp. 3377–3384, 2021, doi: 10.11591/eei.v10i6.3257.
• Bacanin, T. Bezdan, E. Tuba, I. Strumberger, and M. Tuba, “Optimizing convolutional neural network hyperparameters by enhanced swarm intelligence metaheuristics,” Algorithms, Vol. 13, No. 3, 2020, doi: 10.3390/a13030067.
• Brodzicki, M. Piekarski, and J. Jaworek-Korjakowska, “The whale optimization algorithm approach for deep neural networks,” Sensors, Vol. 21, No. 23, 2021, doi: 10.3390/s21238003.
• Han, A. Tan, and J. Zhong, “Application of Particle Swarm Optimization Combined with Long and Short-term Memory Networks for Short-term Load Forecasting,” J. Phys. Conf. Ser., Vol. 2203, No. 1, 2022, doi: 10.1088/1742-6596/2203/1/012047.
• H. Kim, Z. W. Geem, and G. T. Han, “Hyperparameter optimization method based on harmony search algorithm to improve performance of 1D CNN human respiration pattern recognition system,” Sensors (Switzerland), Vol. 20, No. 13, pp. 1–20, 2020, doi: 10.3390/s20133697.
• Serizawa and H. Fujita, “Optimization of Convolutional Neural Network Using the Linearly Decreasing Weight Particle Swarm Optimization,” ArXiv, Vol. abs/2001.0, 2020, [Online]. Available: http://arxiv.org/abs/2001.05670.
• Li et al., “Genetic Algorithm based hyper-parameters optimization for transfer Convolutional Neural Network,” arXiv Prepr. arXiv2103.03875, Vol. abs/2103.0, 2021.
• -C. Yeh, Y.-P. Lin, Y.-C. Liang, C.-M. Lai, and X.-Z. Gao, “Simplified Swarm Optimisation for the Hyperparameters of a Convolutional Neural Network,” pp. 1–26.
• Zhou, “Heuristic Hyperparameter Optimization for Convolutional Neural Networks using Genetic Algorithm,” pp. 1–8, 2021, [Online]. Available: http://arxiv.org/abs/2112.07087.
• Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–14, 2015.
• [X. Kan et al., “A novel PSO-based optimized lightweight convolution neural network for movements recognizing from multichannel surface electromyogram,” Complexity, vol. 2020, 2020, doi: 10.1155/2020/6642463.
• Johnson, A. Valderrama, C. Valle, B. Crawford, R. Soto, and R. Nanculef, “Automating Configuration of Convolutional Neural Network Hyperparameters Using Genetic Algorithm,” IEEE Access, Vol. 8, pp. 156139–156152, 2020, doi: 10.1109/ACCESS.2020.3019245.
• M. Aszemi and P. D. D. Dominic, “Hyperparameter optimization in convolutional neural network using genetic algorithms,” Int. J. Adv. Comput. Sci. Appl., Vol. 10, No. 6, pp. 269–278, 2019, doi: 10.14569/ijacsa.2019.0100638.
• Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural networks: an overview and application in radiology,” Insights Imaging, Vol. 9, No. 4, pp. 611–629, 2018, doi: 10.1007/s13244-018-0639-9.
• Khan, H. Rahmani, S. A. A. Shah, M. Bennamoun, G. Medioni, and S. Dickinson, A Guide to Convolutional Neural Networks for Computer Vision. Morgan & Claypool, 2018.
• Raitoharju, “Chapter 3 - Convolutional neural networks,” A. Iosifidis and A. B. T.-D. L. for R. P. and C. Tefas, Eds. Academic Press, 2022, pp. 35–69.
• H. Lopez Pinaya, S. Vieira, R. Garcia-Dias, and A. Mechelli, “Chapter 10 - Convolutional neural networks,” A. Mechelli and S. B. T.-M. L. Vieira, Eds. Academic Press, 2020, pp. 173–191.
• Tian, “Artificial Intelligence Image Recognition Method Based on Convolutional Neural Network Algorithm,” IEEE Access, vol. 8, pp. 125731–125744, 2020, doi: 10.1109/ACCESS.2020.3006097.
• Kotu and B. Deshpande, “Chapter 10 - Deep Learning,” V. Kotu and B. B. T.-D. S. (Second E. Deshpande, Eds. Morgan Kaufmann, 2019, pp. 307–342.
• “Irish Social Science Data Archive,” 2012. https://www.ucd.ie/issda/data/commissionforenergyregulationcer/.
• Das, A. Abraham, and A. Konar, “Automatic clustering using an improved differential evolution algorithm,” IEEE Trans. Syst. Man, Cybern. Part ASystems Humans, Vol. 38, No. 1, pp. 218–237, 2008, doi: 10.1109/TSMCA.2007.909595.
• L. Davies and D. W. Bouldin, “A Cluster Separation Measure,” IEEE Trans. Pattern Anal. Mach. Intell., vol. PAMI-1, No. 2, pp. 224–227, 1979, doi: 10.1109/TPAMI.1979.4766909.