CNN with BI-LSTM Electricity Theft Detection based on Modified Cheetah Optimization Algorithm in Deep Learning

International Journal of Electrical and Electronics Engineering

© 2023 by SSRG - IJEEE Journal

Volume 10 Issue 2

Year of Publication : 2023

Authors : G. P. Dimf, P. Kumar, K. Paul Joshua

10.14445/23488379/IJEEE-V10I2P104

How to Cite?

G. P. Dimf, P. Kumar, K. Paul Joshua, "CNN with BI-LSTM Electricity Theft Detection based on Modified Cheetah Optimization Algorithm in Deep Learning," SSRG International Journal of Electrical and Electronics Engineering, vol. 10,  no. 2, pp. 35-43, 2023. Crossref, https://doi.org/10.14445/23488379/IJEEE-V10I2P104

Abstract:

The theft of electricity is a serious problem that all energy distribution businesses face, and it is only becoming worse. Thus, there has been an upsurge in recent years in research into techniques for identifying electricity theft. During production, incorrect and illegal energy metre calibrations could cause losses in addition to technical ones. In this paper, a biLSTM and convolutional neural network (CNN) are combined to propose a system for detecting electricity theft. To identify the actual daily electricity use statistics for the dataset, a Long Short-Term Memory (LSTM) based deep learning algorithm has been developed. Data classification, feature extraction, and pre-processing are a few of the techniques that have been developed. In the pre-processing stage, we prepare the data for the training model using a data pre-processing technique before removing unnecessary information. In order to enhance performance, synthetic data is also produced during the preprocessing stage. The Modified Cheetah Optimization Technique (MCHOA)-based new feature selection approach is used to choose the appropriate features for a base classifier during the feature extraction phase of the model's analysis of the voltage, current, and electric energy collected. In the classification stage, the extracted data are classified using the suggested CNN with Bi-LSTM after the feature extraction stage is completed. Whether a customer steals electricity or not, the results obtained when some techniques are combined with CNN and Bi-LSTM attain high-quality values comparable to those obtained by other methods.

Keywords:

CNN with Bi-LSTM, Cheetah optimization technique, Deep learning, Electricity theft detection.

References:

[1] Rui Xia et al., "An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis," Energies, vol. 15, no. 9, p. 7423, 2022. Crossref, https://doi.org/10.3390/en15197423
[2] Hong-Xin Gao, Stefanie Kuenzel, and Xiao-Yu Zhang, "A Hybrid ConvLSTM-based Anomaly Detection Approach for Combating Energy Theft,” IEEE Transactions on Instrumentation and Measurement, vol. 71, pp. 1-10, 2022. Crossref, https://doi.org/10.1109/TIM.2022.3201569
[3] Ayush Jain et al., "Detection of Sarcasm through Tone Analysis on video and Audio files: A Comparative Study on Ai Models Performance," SSRG International Journal of Computer Science and Engineering, vol. 8, no. 12, pp. 1-5, 2021. Crossref, https://doi.org/10.14445/23488387/IJCSE-V8I12P101
[4] Rui Xia et al., "A Fast and Efficient Method Combined Data-Driven for Detecting Electricity Theft to Secure the Smart Grid with Stacking Structure," SSRN, 2022. Crossref, http://dx.doi.org/10.2139/ssrn.4019865
[5] Madhuri V. Joseph, "Sentiment Analysis of Amazon Review using Improvised Conditional Based Convolutional Neural Network and Word Embedding," International Journal of Engineering Trends and Technology, vol. 70, no. 12, pp. 194-209, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I12P220
[6] Ashraf Ullah et al., "A Hybrid Deep Neural Network for Electricity Theft Detection using Intelligent Antenna-Based Smart Meters," Wireless Communications and Mobile Computing, 2021. Crossref, https://doi.org/10.1155/2021/9933111
[7] Jeanne Pereira, and Filipe Saraiva, “Convolutional Neural Network Applied to Detect Electricity Theft: A Comparative Study on Unbalanced Data Handling Techniques,” International Journal of Electrical Power & Energy Systems. Crossref, https://doi.org/10.1016/j.ijepes.2021.107085
[8] Yuan Shen et al., “An Identification Method of Anti-Electricity Theft Load Based on Long and Short-Term Memory Network,” Procedia Computer Science, vol. 183, pp. 440-447, 2021. Crossref, https://doi.org/10.1016/j.procs.2021.02.082
[9] M. Preethi, C. Velayutham, and S. Arumugaperumal, "A Novel RGB Channel Assimilation for Hyperspectral Image Classification using 3D-Convolutional Neural Network with Bi-Long Short-Term Memory," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 201-211, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I3P223
[10] Lei Cui et al., “A Covert Electricity-Theft Cyber-Attack against Machine Learning-Based Detection Models,” IEEE Transactions on Industrial Informatics, vol. 18, no. 11, pp. 7824-7833, 2022. Crossref, https://doi.org/10.1109/TII.2021.3089976
[11] Guixue Cheng et al., "Energy Theft Detection in an Edge Data Center using Deep Learning," Mathematical Problems in Engineering, 2021. Crossref, https://doi.org/10.1155/2021/9938475
[12] Ms.S.Supraja, and Dr.P.Ranjith Kumar, "An Intelligent Traffic Signal Detection System Using Deep Learning," SSRG International Journal of VLSI & Signal Processing, vol. 8, no. 1, pp. 5-9, 2021. Crossref, https://doi.org/10.14445/23942584/IJVSP-V8I1P102
[13] Tianze Lan et al., “An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles Charging Demand,” Energies, vol. 14, no. 3, pp. 569, 2021. Crossref, https://doi.org/10.3390/en14030569
[14] Rutuja Umesh Madhure, Radha Raman, and Sandeep Kumar Singh, "CNN-LSTM based Electricity Theft Detector in Advanced Metering Infrastructure," 2020 11th International Conference on Computing, Communication and Networking Technologies, pp. 1-6, 2020. Crossref, https://doi.org/10.1109/ICCCNT49239.2020.9225572
[15] Dr.V.V.Narendra Kumar, and T.Satish Kumar, "Smarter Artificial Intelligence with Deep Learning," SSRG International Journal of Computer Science and Engineering, vol. 5, no. 6, pp. 10-16, 2018. Crossref, https://doi.org/10.14445/23488387/IJCSE-V5I6P102
[16] Jeanne Pereira, and Filipe Saraiva, “A Comparative Analysis of Unbalanced Data Handling Techniques for Machine Learning Algorithms to Electricity Theft Detection,” 2020 IEEE Congress on Evolutionary Computation, pp. 1-8, 2020. Crossref, https://doi.org/10.1109/CEC48606.2020.9185822
[17] Guoying Lin et al., "Electricity Theft Detection in Power Consumption Data Based on Adaptive Tuning Recurrent Neural Network," Frontiers in Energy Research, vol. 9, p. 773805, 2021. Crossref, https://doi.org/10.3389/fenrg.2021.773805
[18] M.Muruga Lakshmi, and Dr.S.Thayammal, "Ship Detection in Medium-Resolution SAR Images using Deep learning," SSRG International Journal of Electronics and Communication Engineering, vol. 8, no. 5, pp. 1-5, 2021. Crossref, https://doi.org/10.14445/23488549/IJECE-V8I5P101
[19] Muhammad Adil et al., “LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection,” Applied Sciences, vol. 10, no. 12, p. 4378, 2020. Crossref, https://doi.org/10.3390/app10124378
[20] D. J. Samatha Naidu, and R. Lokesh, "Missing Child Identification System using Deep Learning with VGG-FACE Recognition Technique," SSRG International Journal of Computer Science and Engineering, vol. 9, no. 9, pp. 1-11, 2022. Crossref, https://doi.org/10.14445/23488387/IJCSE-V9I9P101
[21] Murthy V. S. N. Tatavarthy, and V. Naga Lakshmi, "Pedagogical Content Knowledge Classification using CNN with Bi-LSTM," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 264-271, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I8P228
[22] Junhao Shi et al., "A Novel Approach to Detect Electricity Theft Based on Conv-Attentional Transformer," SSRN, 2022. Crossref, http://dx.doi.org/10.2139/ssrn.4069701
[23] Joyassree Sen et al., "Face Recognition using Deep Convolutional Network and One-shot Learning," SSRG International Journal of Computer Science and Engineering, vol. 7, no. 4, pp. 23-29, 2020. Crossref, https://doi.org/10.14445/23488387/IJCSE-V7I4P107
[24] Yanlin Peng et al., “Electricity Theft Detection in AMI Based on Clustering and Local Outlier Factor,” IEEE Access, vol. 9, pp. 107250-107259, 2021. Crossref, https://doi.org/10.1109/ACCESS.2021.3100980
[25] Shahriar Rahman Fahim et al., “A Deep Learning Based Intelligent Approach in Detection and Classification of Transmission Line Faults,” International Journal of Electrical Power & Energy Systems, vol. 133, p. 107102, 2021. Crossref, https://doi.org/10.1016/j.ijepes.2021.107102
[26] Zhuang Yuan, and Wu Chunrong, "Deep Learning-Based Listening Teaching Strategy in Junior Middle School," SSRG International Journal of Humanities and Social Science, vol. 9, no. 2, pp. 65-70, 2022. Crossref, https://doi.org/10.14445/23942703/IJHSS-V9I2P110
[27] Zahoor Ali Khan et al., “Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data,” Sustainability, vol. 12, no. 19, p. 8023, 2020. Crossref, https://doi.org/10.3390/su12198023
[28] Anupam Das, and Adian McFarlane, “Non-Linear Dynamics of Electric Power Losses, Electricity Consumption, and GDP in Jamaica,” Energy Economics, vol. 84, p. 104590, 2019. Crossref, https://doi.org/10.1016/j.eneco.2019.104530
[29] Zhengwei Qu et al., “Detection of Electricity Theft Behavior Based on Improved Synthetic Minority Oversampling Technique and Random Forest Classifier,” Energies, vol. 13, no. 8, p. 2039, 2020. Crossref, https://doi.org/10.3390/en13082039
[30] Shoaib Munawar et al., "Electricity Theft Detection in Smart Grids Using a Hybrid BiGRU–BiLSTM Model with Feature Engineering-Based Preprocessing," Sensors, vol. 22, no. 20, p. 7818, 2022. Crossref, https://doi.org/10.3390/s22207818