Abstract
In this paper, a problem of misclassification due to cross pairs across a decision boundary is investigated. A cross pair is a junction of the two opposite class samples. These cross pairs are identified using Tomek links technique. The majority class sample associated with cross pairs are removed to segregate the two opposite classes through an affine decision boundary. Due to non-availability of theft data, six theft cases are used to synthesize theft data to mimic real world scenario. These six theft cases are applied to benign class data, where benign samples are modified and malicious samples are synthesized. Furthermore, to tackle the class imbalance issue a K-means SMOTE is used for the provision of balance data. Moreover, the technical route is to train the model on a time-series data of both classes. Training model on imbalance data tends to misclassification of the samples, due to biasness towards a majority class, which results in a high FPR. The balanced data is provided as an input to a hybrid bi-directional GRU and bi-directional LSTM model. The two classes are efficiently classified with a high accuracy, high detection rate and low FPR.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Grigsby, L.L. (ed.): Electric Power Generation, Transmission, and Distribution. CRC Press, Boca Raton (2018)
Yu, X., Cecati, C., Dillon, T., Simoes, M.G.: The new frontier of smart grids. IEEE Ind. Electron. Mag. 5(3), 49–63 (2011)
Depuru, S.S.S.R., Wang, L., Devabhaktuni, V.: Electricity theft: overview, issues, prevention and a smart meter based approach to control theft. Energy Policy 39(2), 1007–1015 (2011)
Buzau, M., Tejedor-Aguilera, J., Cruz-Romero, P., Gómez-Expósito, A.: Hybrid deep neural networks for detection of non-technical losses in electricity smart meters. IEEE Trans. Power Syst. 35(2), 1254–1263 (2020). https://doi.org/10.1109/TPWRS.2019.2943115
World Bank: World development report 2004: making services work for poor people. The World Bank (2003)
Gaur, V., Gupta, E.: The determinants of electricity theft: an empirical analysis of Indian states. Energy Policy 93, 127–136 (2016)
Bhatti, S.S., et al.: Electric power transmission and distribution losses overview and minimization in Pakistan. Int. J. Sci. Eng. Res. 6(4), 1108–1112 (2015)
Buzau, M.M., Tejedor-Aguilera, J., Cruz-Romero, P., Gómez-Expósito, A.: Hybrid deep neural networks for detection of non-technical losses in electricity smart meters. IEEE Trans. Power Syst. 35(2), 1254–1263 (2019)
Smart Meters help reduce electricity theft increase safety. BC Hydro, Inc., vancouvers (2011)
Saeed, M.S., Mustafa, M.W., Sheikh, U.U., Jumani, T.A., Mirjat, N.H.: Ensemble bagged tree based classification for reducing non-technical losses in multan electric power company of Pakistan. Electronics 8(8), 860 (2019)
Punmiya, R., Choe, S.: Energy theft detection using gradient boosting theft detector with feature engineering-based preprocessing. IEEE Trans. Smart Grid 10(2), 2326–2329 (2019). https://doi.org/10.1109/TSG.2019.2892595
Buzau, M.M., Tejedor-Aguilera, J., Cruz-Romero, P., Gómez-Expósito, A.: Detection of non-technical losses using smart meter data and supervised learning. IEEE Trans. Smart Grid 10(3), 2661–2670 (2018)
Biswas, P.P., Cai, H., Zhou, B., Chen, B., Mashima, D., Zheng, V.W.: Electricity theft pinpointing through correlation analysis of master and individual meter readings. IEEE Trans. Smart Grid 11(4), 3031–3042 (2019)
Li, S., Han, Y., Yao, X., Yingchen, S., Wang, J., Zhao, Q.: Electricity theft detection in power grids with deep learning and random forests. J. Electr. Comput. Eng. (2019)
Hasan, M., Toma, R.N., Nahid, A.A., Islam, M.M., Kim, J.M.: Electricity theft detection in smart grid systems: a CNN-LSTM based approach. Energies 12(17), 3310 (2019)
Lu, X., Zhou, Y., Wang, Z., Yi, Y., Feng, L., Wang, F.: Knowledge embedded semi-supervised deep learning for detecting non-technical losses in the smart grid. Energies 12(18), 3452 (2019)
Zheng, Z., Yang, Y., Niu, X., Dai, H., Zhou, Y.: Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids. IEEE Trans. Industr. Inf. 14(4), 1606–1615 (2018). https://doi.org/10.1109/TII.2017.2785963
Yan, Z., Wen, H.: Electricity theft detection base on extreme gradient boosting in AMI. IEEE Trans. Instrum. Meas. 70, 1–9 (2021)
Buzau, M.M., Tejedor-Aguilera, J., Cruz-Romero, P., Gómez-Expósito, A.: Detection of non-technical losses using smart meter data and supervised learning. IEEE Trans. Smart Grid 10(3), 2661–2670 (2019). https://doi.org/10.1109/TSG.2018.2807925
Ismail, M., Shaaban, M.F., Naidu, M., Serpedin, E.: Deep learning detection of electricity theft cyber-attacks in renewable distributed generation. IEEE Trans. Smart Grid 11(4), 3428–3437 (2020)
Jokar, P., Arianpoo, N., Leung, V.C.M.: Electricity theft detection in AMI using customers’ consumption patterns. IEEE Trans. Smart Grid 7(1), 216–226 (2016). https://doi.org/10.1109/TSG.2015.2425222
Liu, Y., Liu, T., Sun, H., Zhang, K., Liu, P.: Hidden electricity theft by exploiting multiple-pricing scheme in smart grids. IEEE Trans. Inf. Forensics Secur. 15, 2453–2468 (2020)
Zheng, K., Chen, Q., Wang, Y., Kang, C., Xia, Q.: A novel combined data-driven approach for electricity theft detection. IEEE Trans. Ind. Inf. 15(3), 1809–1819 (2018)
Kong, X., Zhao, X., Liu, C., Li, Q., Dong, D., Li, Y.: Electricity theft detection in low-voltage stations based on similarity measure and DT-KSVM. Int. J. Electr. Power Energy Syst. 125, 106544 (2021)
Fenza, G., Gallo, M., Loia, V.: Drift-aware methodology for anomaly detection in smart grid. IEEE Access 7, 9645–9657 (2019)
Huang, Y., Xu, Q.: Electricity theft detection based on stacked sparse denoising autoencoder. Int. J. Electr. Power Energy Syst. 125, 106448 (2021)
Yip, S.C., Wong, K., Hew, W.P., Gan, M.T., Phan, R.C.W., Tan, S.W.: Detection of energy theft and defective smart meters in smart grids using linear regression. Int. J. Electr. Power Energy Syst. 91, 230–240 (2017)
Park, C.H., Kim, T.: Energy theft detection in advanced metering infrastructure based on anomaly pattern detection. Energies 13(15), 3832 (2020)
Hu, J., Li, S., Hu, J., Guanci, Y.: A hierarchical feature extraction model for multi-label mechanical patent classification. Sustainability 10, 219 (2018). https://doi.org/10.3390/su10010219
Hasan, M., Toma, R.N., Nahid, A.A., Islam, M.M., Kim, J.M.: Electricity theft detection in smart grid systems: a CNN-LSTM based approach. Energies 12(17), 3310 (2019)
Khalid, R., Javaid, N., Al-Zahrani, F.A., Aurangzeb, K., Qazi, E.U.H., Ashfaq, T.: Electricity load and price forecasting using Jaya-Long Short Term Memory (JLSTM) in smart grids. Entropy 22(1), 10 (2020)
Mujeeb, S., Javaid, N., Akbar, M., Khalid, R., Nazeer, O., Khan, M.: Big data analytics for price and load forecasting in smart grids. In: International Conference on Broadband and Wireless Computing, Communication and Applications, pp. 77–87. Springer, Cham (2018)
Adil, M., Javaid, N., Qasim, U., Ullah, I., Shafiq, M., Choi, J.G.: LSTM and bat-based RUSBoost approach for electricity theft detection. Appl. Sci. 10(12), 4378 (2020)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Munawar, S., Asif, M., Kabir, B., Pamir, Ullah, A., Javaid, N. (2021). Electricity Theft Detection in Smart Meters Using a Hybrid Bi-directional GRU Bi-directional LSTM Model. In: Barolli, L., Yim, K., Enokido, T. (eds) Complex, Intelligent and Software Intensive Systems. CISIS 2021. Lecture Notes in Networks and Systems, vol 278. Springer, Cham. https://doi.org/10.1007/978-3-030-79725-6_29
Download citation
DOI: https://doi.org/10.1007/978-3-030-79725-6_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-79724-9
Online ISBN: 978-3-030-79725-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)