Skip to main content

Towards Energy Efficient Smart Grids: Data Augmentation Through BiWGAN, Feature Extraction and Classification Using Hybrid 2DCNN and BiLSTM

  • 398 Accesses

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 279)

Abstract

In this paper, a novel hybrid deep learning approach is proposed to detect the nontechnical losses (NTLs) that occur in smart grids due to illegal use of electricity, faulty meters, meter malfunctioning, unpaid bills, etc. The proposed approach is based on data-driven methods due to the sufficient availability of smart meters’ data. Therefore, a bi-directional wasserstein generative adversarial network (Bi-WGAN) is utilized to generate the synthetic theft samples for solving the class imbalance problem. The Bi-WGAN efficiently synthesizes the minority class theft samples by leveraging the capabilities of an additional encoder module. Moreover, the curse of dimensionality degrades the model’s generalization ability. Therefore, the high dimensionality issue is solved using the two dimensional convolutional neural network (2D-CNN) and bidirectional long short-term memory network (Bi-LSTM). The 2D-CNN is applied on 2D weekly data to extract the most prominent features. In 2D-CNN, the convolutional and pooling layers extract only the potential features and discard the redundant features to reduce the curse of dimensionality. This process increases the convergence speed of the model as well as reduces the computational overhead. Meanwhile, a Bi-LSTM is also used to detect the non-malicious changes in consumers’ load profiles using its strong memorization capabilities. Finally, the outcomes of both models are concatenated into a single feature map and a sigmoid activation function is applied for final NTL detection. The simulation results demonstrate that the proposed model outperforms the existing scheme in terms of mathew correlation coefficient (MCC), precision-recall (PR) and area under the curve (AUC). It achieves 3%, 5% and 4% greater MCC, PR and AUC scores, respectively as compared to the existing model.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-79728-7_12
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   269.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-79728-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   349.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.

References

  1. McDaniel, P., McLaughlin, S.: Security and privacy challenges in the smart grid. IEEE Secur. Priv. 7(3), 75–77 (2009)

    CrossRef  Google Scholar 

  2. Chen, Q., Zheng, K., Kang, C., Huangfu, F.: Detection methods of abnormal electricity consumption behaviors: review and prospect. Autom. Electr. Power Syst. 42(17), 189–199 (2018)

    Google Scholar 

  3. Lo, C.-H., Ansari, N.: Consumer: a novel hybrid intrusion detection system for distribution networks in smart grid. IEEE Trans. Emerg. Top. Comput. 1(1), 33–44 (2013)

    CrossRef  Google Scholar 

  4. Amin, S., Schwartz, G.A., Tembine, H.: Incentives and security in electricity distribution networks. In: International Conference on Decision and Game Theory for Security, pp. 264–280. Springer (2012)

    Google Scholar 

  5. Zheng, Z., Yang, Y., Niu, X., Dai, H.-N., Zhou, Y.: Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids. IEEE Trans. Industr. Inf. 14(4), 1606–1615 (2017)

    CrossRef  Google Scholar 

  6. 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)

    CrossRef  Google Scholar 

  7. 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)

    CrossRef  Google Scholar 

  8. 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)

    CrossRef  Google Scholar 

  9. Razavi, R., Gharipour, A., Fleury, M., Akpan, I.J.: A practical feature-engineering framework for electricity theft detection in smart grids. Appl. Energy 238, 481–494 (2019)

    CrossRef  Google Scholar 

  10. Yao, D., Wen, M., Liang, X., Zipeng, F., Zhang, K., Yang, B.: Energy theft detection with energy privacy preservation in the smart grid. IEEE Internet Things J. 6(5), 7659–7669 (2019)

    CrossRef  Google Scholar 

  11. 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)

    CrossRef  Google Scholar 

  12. Huang, Y., Qifeng, X.: Electricity theft detection based on stacked sparse denoising autoencoder. Int. J. Electr. Power Energy Syst. 125, 106448 (2021)

    CrossRef  Google Scholar 

  13. Arif, A., Javaid, N., Aldegheishem, A., Alrajeh, N.: Big data analytics for identifying electricity theft using machine learning approaches in micro grids for smart communities

    Google Scholar 

  14. Aldegheishem, A., Anwar, M., Javaid, N., Alrajeh, N., Shafiq, M., Ahmed, H.: Towards sustainable energy efficiency with intelligent electricity theft detection in smart grids emphasising enhanced neural networks. IEEE Access 9, 25036–25061 (2021)

    CrossRef  Google Scholar 

  15. Xiaoquan, L., Zhou, Yu., 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)

    CrossRef  Google Scholar 

  16. Ramos, C.C.O., Rodrigues, D., de Souza, A.N., Papa, J.P.: On the study of commercial losses in Brazil: a binary black hole algorithm for theft characterization. IEEE Trans. Smart Grid 9(2), 676–683 (2016)

    CrossRef  Google Scholar 

  17. Kocaman, B., Tümen, V.: Detection of electricity theft using data processing and LSTM method in distribution systems. Sādhanā 45(1), 1–10 (2020)

    CrossRef  Google Scholar 

  18. Hu, T., Guo, Q., Sun, H., Huang, T.-E., Lan, J.: Nontechnical losses detection through coordinated BiWGAN and SVDD. IEEE Trans. Neural Netw. Learn. Syst. 32, 1866–1880 (2020)

    CrossRef  Google Scholar 

  19. 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)

    CrossRef  Google Scholar 

  20. Gong, X., Tang, B., Zhu, R., Liao, W., Song, L.: Data augmentation for electricity theft detection using conditional variational auto-encoder. Energies 13(17), 4291 (2020)

    CrossRef  Google Scholar 

  21. Aslam, Z., Ahmed, F., Almogren, A., Shafiq, M., Zuair, M., Javaid, N.: An attention guided semi-supervised learning mechanism to detect electricity frauds in the distribution systems. IEEE Access 8, 221767–221782 (2020)

    CrossRef  Google Scholar 

  22. Li, S., Han, Y., Xu, Y., Yingchen, S., Wang, J., Zhao, Q.: Electricity theft detection in power grids with deep learning and random forests. J. Electr. Comput. Eng. 2019 (2019)

    Google Scholar 

  23. Avila, N.F., Figueroa, G., Chu, C.-C.: NTL detection in electric distribution systems using the maximal overlap discrete wavelet-packet transform and random undersampling boosting. IEEE Trans. Power Syst. 33(6), 7171–7180 (2018)

    CrossRef  Google Scholar 

  24. 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 (2015)

    CrossRef  Google Scholar 

  25. Zheng, K., Chen, Q., Wang, Y., Kang, C., Xia, Q.: A novel combined data-driven approach for electricity theft detection. IEEE Trans. Industr. Inf. 15(3), 1809–1819 (2018)

    CrossRef  Google Scholar 

  26. Gunturi, S.K., Sarkar, D.: Ensemble machine learning models for the detection of energy theft. Electr. Power Syst. Res. 192, 106904 (2021)

    CrossRef  Google Scholar 

  27. Hasan, Md., Toma, R.N., Nahid, A.-A., Islam, M.M., Kim, J.-M., et al.: Electricity theft detection in smart grid systems: a CNN-LSTM based approach. Energies 12(17), 3310 (2019)

    CrossRef  Google Scholar 

  28. Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 1–58 (2009)

    CrossRef  Google Scholar 

  29. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214–223. PMLR (2017)

    Google Scholar 

  30. Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.: Improved training of Wasserstein GANs. arXiv preprint arXiv:1704.00028 (2017)

  31. Zhao, J., Mao, X., Chen, L.: Speech emotion recognition using deep 1D & 2D CNN LSTM networks. Biomed. Signal Process. Control 47, 312–323 (2019)

    CrossRef  Google Scholar 

  32. Cui, Z., Ke, R., Ziyuan, P., Wang, Y.: Stacked bidirectional and unidirectional LSTM recurrent neural network for forecasting network-wide traffic state with missing values. Transp. Res. Part C Emerg. Technol. 118, 102674 (2020)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Asif, M., Kabir, B., Pamir, Ullah, A., Munawar, S., Javaid, N. (2022). Towards Energy Efficient Smart Grids: Data Augmentation Through BiWGAN, Feature Extraction and Classification Using Hybrid 2DCNN and BiLSTM. In: Barolli, L., Yim, K., Chen, HC. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing. IMIS 2021. Lecture Notes in Networks and Systems, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-79728-7_12

Download citation