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Supervised and Unsupervised Neural Networks: Experimental Study for Anomaly Detection in Electrical Consumption

  • Joel García
  • Erik Zamora
  • Humberto Sossa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11288)

Abstract

Households are responsible for more than 40% of the global electricity consumption [7]. The analysis of this consumption to find unexpected behaviours could have a great impact on saving electricity. This research presents an experimental study of supervised and unsupervised neural networks for anomaly detection in electrical consumption. Multilayer perceptrons and autoencoders are used for each approach, respectively. In order to select the most suitable neural model in each case, there is a comparison of various architectures. The proposed methods are evaluated using real-world data from an individual home electric power usage dataset. The performance is compared with a traditional statistical procedure. Experiments show that the supervised approach has a significant improvement in anomaly detection rate. We evaluate different possible feature sets. The results demonstrate that temporal data and measures of consumption patterns such as mean, standard deviation and percentiles are necessary to achieve higher accuracy.

Keywords

Anomaly detection Neural networks Supervised Unsupervised Statistic 

Notes

Acknowledgments

E. Zamora and H. Sossa would like to acknowledge CIC-IPN for the support to carry out this research. This work was economically supported by SIP-IPN (grant numbers 20180180 and 20180730) and CONACYT (grant number 65) as well as the Red Temática de CONACYT de Inteligencia Computacional Aplicada. J. García acknowledges CONACYT for the scholarship granted towards pursuing his MSc studies.

References

  1. 1.
    A beginner’s guide to multilayer perceptrons. deeplearning4j.org/multilayerperceptron. Accessed 25 May 2018
  2. 2.
    Araya, D.B., Grolinger, K., ElYamany, H.F., Capretz, M.A., Bitsuamlak, G.: Collective contextual anomaly detection framework for smart buildings, pp. 511–518. IEEE (2016)Google Scholar
  3. 3.
    Ashton, K.: That “Internet of Things" thing. RFiD J. 22, 97–114 (2009)Google Scholar
  4. 4.
    Bengio, Y., et al.: Learning deep architectures for AI. Found. Trends\(\textregistered \) Mach. Learn. 2(1), 1–127 (2009)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Chandola, V., Banerjee, A., Kumar, V.: Anomaly detection: a survey. ACM Comput. Surv. (CSUR) 41(3), 15 (2009)CrossRefGoogle Scholar
  6. 6.
    Chou, J.-S., Telaga, A.S.: Real-time detection of anomalous power consumption. Renew. Sustain. Energy Rev. 33, 400–411 (2014)CrossRefGoogle Scholar
  7. 7.
    Costa, A., Keane, M.M., Raftery, P., O’Donnell, J.: Key factors methodology: a novel support to the decision making process of the building energy manager in defining optimal operation strategies. Energy Build. 49, 158–163 (2012)CrossRefGoogle Scholar
  8. 8.
    Dheeru, D., Taniskidou, E.K.: UCI machine learning repository (2017)Google Scholar
  9. 9.
    Gómez Chacón, I.M., et al.: Educación matemática y ciudadanía (2010)Google Scholar
  10. 10.
    Hajian-Tilaki, K.: Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Casp. J. Intern. Med. 4(2), 627 (2013)Google Scholar
  11. 11.
    IEA: World energy outlook 2011 executive summary (2011)Google Scholar
  12. 12.
    Tasfi, N.L., Higashino, W.A., Grolinger, K., Capretz, M.A.: Deep neural networks with confidence sampling for electrical anomaly detection, June 2017Google Scholar
  13. 13.
    Lyu, L., Jin, J., Rajasegarar, S., He, X., Palaniswami, M.: Fog-empowered anomaly detection in iot using hyperellipsoidal clustering. IEEE Internet Things J. 4(5), 1174–1184 (2017)CrossRefGoogle Scholar
  14. 14.
    Ouyang, Z., Sun, X., Chen, J., Yue, D., Zhang, T.: Multi-view stacking ensemble for power consumption anomaly detection in the context of industrial internet of things. IEEE Access 6, 9623–9631 (2018)CrossRefGoogle Scholar
  15. 15.
    Ouyang, Z., Sun, X., Yue, D.: Hierarchical time series feature extraction for power consumption anomaly detection. In: Li, K., Xue, Y., Cui, S., Niu, Q., Yang, Z., Luk, P. (eds.) LSMS/ICSEE-2017. CCIS, vol. 763, pp. 267–275. Springer, Singapore (2017).  https://doi.org/10.1007/978-981-10-6364-0_27CrossRefGoogle Scholar
  16. 16.
    Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Stover, C.: Unit circleGoogle Scholar
  18. 18.
    Yijia, T., Hang, G.: Anomaly detection of power consumption based on waveform feature recognition, pp. 587–591. IEEE (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Instituto Politécnico Nacional - Centro de Investigación en ComputaciónMexico CityMexico
  2. 2.Tecnológico de Monterrey, Campus GuadalajaraZapopanMexico

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