Skip to main content
SpringerLink
Log in

Empirical mode decomposition-based multi-scale spectral graph convolution network for abnormal electricity consumption detection

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Abnormal electricity consumption detection is of great significance for maintaining the safe and stable operation of the power grids, or for reducing the irregular electricity consumption and economic losses. However, not enough attention has been paid to comprehensively explore the multi-scale characteristics of the consumption series, and to discover the intra-series relationships among different timestamps. To tackle such problems, this paper proposes an empirical mode decomposition-based multi-scale spectral graph convolution network for detecting the abnormal electricity consumptions. Firstly, the electricity consumption data are decomposed into subcomponents by the empirical mode decomposition to generate the multi-scale time characteristics, and then, the entropy feature vectors are extracted from such subcomponents and the original data. Furthermore, the feature fusion for the entropy feature vectors and the original data is realized by the multi-scale convolutional layers. Then, the spectral graph convolution network (SGCN) is employed to detect the abnormal electricity consumption and to output specific electricity consumption case. In this SGCN, both the inter-series and the intra-series relationships can be tackled by the graph Fourier transform and the graph convolution operator. Finally, detailed experiments and comparisons are done. Experimental results demonstrate that our proposed method has satisfactory accuracy and adaptability, and has much better detection performance than some popular deep and shallow methods, e.g., the CNN, LSTM, ELM and SVM.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Huang S-C, Lo Y-L, Lu C-N (2013) Non-technical loss detection using state estimation and analysis of variance. IEEE Trans Power Syst 28(3):2959–2966

    Google Scholar 

  2. Navani J, Sharma N, Sapra S (2012) Technical and non-technical losses in power system and its economic consequence in Indian economy. Int J Electron Comput Sci Eng 1(2):757–761

    Google Scholar 

  3. Li B, Xu K, Cui X, Wang Y, Ai X, Wang Y (2018) Multi-scale densenet-based electricity theft detection. In: International conference on intelligent computing, pp 172–182. Springer

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

    Google Scholar 

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

    Google Scholar 

  6. Xiao Z, Xiao Y, Du DH-C (2013) Non-repudiation in neighborhood area networks for smart grid. IEEE Commun Mag 51(1):18–26

    Google Scholar 

  7. Cárdenas AA, Amin S, Schwartz G, Dong R, Sastry S (2012) A game theory model for electricity theft detection and privacy-aware control in ami systems. In: 2012 50th Annual allerton conference on communication, control, and computing (Allerton), pp 1830–1837. IEEE

  8. Angelos EWS, Saavedra OR, Cortés OAC, de Souza AN (2011) Detection and identification of abnormalities in customer consumptions in power distribution systems. IEEE Trans Power Delivery 26(4):2436–2442

    Google Scholar 

  9. Depuru SSSR, Wang L, Devabhaktuni V (2011) Support vector machine based data classification for detection of electricity theft. In: 2011 IEEE/PES power systems conference and exposition, pp 1–8. IEEE

  10. Depuru SSSR, Wang L, Devabhaktuni V, Green RC (2013) High performance computing for detection of electricity theft. Int J Electrical Power Energy Syst 47:21–30

    Google Scholar 

  11. Di Martino M, Decia F, Molinelli J, Fernández A (2012) Improving electric fraud detection using class imbalance strategies. In: ICPRAM (2), pp 135–141

  12. Jindal A, Dua A, Kaur K, Singh M, Kumar N, Mishra S (2016) Decision tree and svm-based data analytics for theft detection in smart grid. IEEE Trans Ind Inf 12(3):1005–1016

    Google Scholar 

  13. Sahoo S, Nikovski D, Muso T, Tsuru K (2015) Electricity theft detection using smart meter data. In: 2015 IEEE power and energy society innovative smart grid technologies conference (ISGT), pp 1–5. IEEE

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

    Google Scholar 

  15. Nagi J, Yap KS, Tiong SK, Ahmed SK, Mohamad M (2009) Nontechnical loss detection for metered customers in power utility using support vector machines. IEEE Trans Power Delivery 25(2):1162–1171

    Google Scholar 

  16. Kong X, Zhao X, Liu C, Li Q, Dong D, Li Y (2021) Electricity theft detection in low-voltage stations based on similarity measure and dt-ksvm. Int J Electrical Power Energy Syst 125:106544

    Google Scholar 

  17. Wang Y, Chen Q, Kang C, Xia Q (2016) Clustering of electricity consumption behavior dynamics toward big data applications. IEEE Trans Smart Grid 7(5):2437–2447

    Google Scholar 

  18. Lin G, Feng X, Guo W, Cui X, Liu S, Jin W, Lin Z, Ding Y (2021) Electricity theft detection based on stacked autoencoder and the undersampling and resampling based random forest algorithm. IEEE Access 9:124044–124058

    Google Scholar 

  19. Ullah A, Javaid N, Yahaya AS, Sultana T, Al-Zahrani FA, Zaman F (2021) A hybrid deep neural network for electricity theft detection using intelligent antenna-based smart meters. Wireless Commun Mobile Comput 2021

  20. Ismail M, Shaaban MF, Naidu M, Serpedin E (2020) Deep learning detection of electricity theft cyber-attacks in renewable distributed generation. IEEE Trans Smart Grid 11(4):3428–3437

    Google Scholar 

  21. Huang Y, Xu Q (2021) Electricity theft detection based on stacked sparse denoising autoencoder. Int J Electrical Power Energy Syst 125:106448

    Google Scholar 

  22. Qiu X, Ren Y, Suganthan PN, Amaratunga GA (2017) Empirical mode decomposition based ensemble deep learning for load demand time series forecasting. Appl Soft Comput 54:246–255

    Google Scholar 

  23. Liu H, Zhang J, Cheng Y, Lu C (2016) Fault diagnosis of gearbox using empirical mode decomposition and multi-fractal detrended cross-correlation analysis. J Sound Vib 385:350–371

    Google Scholar 

  24. Chen T, Ju S, Yuan X, Elhoseny M, Ren F, Fan M, Chen Z (2018) Emotion recognition using empirical mode decomposition and approximation entropy. Comput Electrical Eng 72:383–392

    Google Scholar 

  25. Shang C, Liu Q, Tong Q, Sun J, Song M, Bi J (2021) Multi-view spectral graph convolution with consistent edge attention for molecular modeling. Neurocomputing 445:12–25

    Google Scholar 

  26. Li J, Xie X, Cao Y, Pan Q, Zhao Z, Shi G (2021) Knowledge embedded gcn for skeleton-based two-person interaction recognition. Neurocomputing 444:338–348

    Google Scholar 

  27. Wang Y, Fang S, Zhang C, Xiang S, Pan C (2022) Tvgcn: Time-variant graph convolutional network for traffic forecasting. Neurocomputing 471:118–129

    Google Scholar 

  28. Ryu S, Kwon Y, Kim WY (2019) A bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification. Chem Sci 10(36):8438–8446

    Google Scholar 

  29. Ren Y, Shi Y, Zhang K, Chen Z, Yan Z (2020) Medical treatment migration prediction based on gcn via medical insurance data. IEEE J Biomed Health Informatics 24(9):2516–2522

    Google Scholar 

  30. Rilling G, Flandrin P, Goncalves P, et al. (2003) On empirical mode decomposition and its algorithms. In: IEEE-EURASIP workshop on nonlinear signal and image processing, 3:8–11

  31. Flandrin P, Rilling G, Goncalves P (2004) Empirical mode decomposition as a filter bank. IEEE Signal Process Lett 11(2):112–114

    Google Scholar 

  32. Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2008) The graph neural network model. IEEE Trans Neural Netw 20(1):61–80

    Google Scholar 

  33. Wu Z, Pan S, Chen F, Long G, Zhang C, Philip SY (2020) A comprehensive survey on graph neural networks. IEEE Trans Neural Netw Learn Syst 32(1):4–24

    MathSciNet  Google Scholar 

  34. Zhang S, Tong H, Xu J, Maciejewski R (2019) Graph convolutional networks: a comprehensive review. Comput Social Netw 6(1):1–23

    Google Scholar 

  35. Białynicki-Birula I, Mycielski J (1975) Uncertainty relations for information entropy in wave mechanics. Commun Math Phys 44(2):129–132

    MathSciNet  Google Scholar 

  36. Pincus S (1995) Approximate entropy (apen) as a complexity measure. Chaos Interdiscip J Nonlinear Sci 5(1):110–117

  37. Yin J, Xiao P, Li J, Liu Y, Yan C, Zhang Y (2020) Parameters analysis of sample entropy, permutation entropy and permutation ratio entropy for rr interval time series. Inf Process Manage 57(5):102283

    Google Scholar 

  38. Lee H-M, Chen C-M, Chen J-M, Jou Y-L (2001) An efficient fuzzy classifier with feature selection based on fuzzy entropy. IEEE Trans Syst Man Cybernet Part B (Cybernetics) 31(3):426–432

  39. Pincus SM (1991) Approximate entropy as a measure of system complexity. Proc Nat Acad Sci 88(6):2297–2301

    MathSciNet  MATH  Google Scholar 

  40. Yentes JM, Hunt N, Schmid KK, Kaipust JP, McGrath D, Stergiou N (2013) The appropriate use of approximate entropy and sample entropy with short data sets. Ann Biomed Eng 41(2):349–365

    Google Scholar 

  41. Parkash O, Sharma P, Mahajan R (2008) New measures of weighted fuzzy entropy and their applications for the study of maximum weighted fuzzy entropy principle. Inf Sci 178(11):2389–2395

    MathSciNet  MATH  Google Scholar 

  42. Sandryhaila A, Moura JM (2013) Discrete signal processing on graphs: Graph fourier transform. In: 2013 IEEE international conference on acoustics, speech and signal processing, pp 6167–6170. IEEE

  43. Jokar P, Arianpoo N, Leung VC (2015) Electricity theft detection in ami using customers‘ consumption patterns. IEEE Trans Smart Grid 7(1):216–226

    Google Scholar 

  44. Zanetti M, Jamhour E, Pellenz M, Penna M, Zambenedetti V, Chueiri I (2017) A tunable fraud detection system for advanced metering infrastructure using short-lived patterns. IEEE Trans Smart grid 10(1):830–840

    Google Scholar 

  45. Makonin S (2018) Hue: The hourly usage of energy dataset for buildings in British Columbia. Technical report

  46. Wei P, He F, Li L, Li J (2020) Research on sound classification based on svm. Neural Comput Appl 32(6):1593–1607

    Google Scholar 

  47. Koda S, Zeggada A, Melgani F, Nishii R (2018) Spatial and structured svm for multilabel image classification. IEEE Trans Geosci Remote Sens 56(10):5948–5960

    Google Scholar 

  48. Liu L, Martín-Barragán B, Prieto FJ (2021) A projection multi-objective svm method for multi-class classification. Comput Ind Eng 158:107425

    Google Scholar 

  49. Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70(1–3):489–501

    Google Scholar 

  50. Wan Y, Song S, Huang G, Li S (2017) Twin extreme learning machines for pattern classification. Neurocomputing 260:235–244

    Google Scholar 

  51. Yahia S, Said S, Zaied M (2022) Wavelet extreme learning machine and deep learning for data classification. Neurocomputing 470:280–289

    Google Scholar 

  52. Wang Q, Dou Y, Liu X, Lv Q, Li S (2016) Multi-view clustering with extreme learning machine. Neurocomputing 214:483–494

    Google Scholar 

  53. Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780

    Google Scholar 

  54. Nguyen H, Tran KP, Thomassey S, Hamad M (2021) Forecasting and anomaly detection approaches using lstm and lstm autoencoder techniques with the applications in supply chain management. Int J Inf Manage 57:102282

    Google Scholar 

  55. Zhang Q, Wang X, Wu YN, Zhou H, Zhu S-C (2020) Interpretable cnns for object classification. IEEE Trans Pattern Anal Mach Intell 43(10):3416–3431

    Google Scholar 

  56. Singhal S, Passricha V, Sharma P, Aggarwal RK (2019) Multi-level region-of-interest cnns for end to end speech recognition. J Ambient Intell Human Comput 10(11):4615–4624

    Google Scholar 

  57. Quan Y, Chen Y, Shao Y, Teng H, Xu Y, Ji H (2021) Image denoising using complex-valued deep cnn. Pattern Recogn 111:107639

    Google Scholar 

Download references

Acknowledgements

This study is partly supported by the National Natural Science Foundation of China (Nos. 62076150, 62133008, 61903226), the Taishan Scholar Project of Shandong Province (No. TSQN201812092), the Key Research and Development Program of Shandong Province (No. 2021CXGC011205, 2021TSGC1053), and the Youth Innovation Technology Project of Higher School in Shandong Province (No. 2019KJN005).

Author information

Authors and Affiliations

  1. School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan, 250101, China

    Songping Meng, Chengdong Li, Wei Peng & Chenlu Tian

Authors
  1. Songping Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chengdong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chenlu Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chengdong Li.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Meng, S., Li, C., Peng, W. et al. Empirical mode decomposition-based multi-scale spectral graph convolution network for abnormal electricity consumption detection. Neural Comput & Applic 35, 9865–9881 (2023). https://doi.org/10.1007/s00521-023-08222-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08222-8

Keywords

Search

Navigation