Skip to main content

Advertisement

SpringerLink
Log in

Detecting anomalous energy consumption using contextual analysis of smart meter data

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Energy consumption is dependent on temperature, humidity, occupancy, occupant type, building area etc. All these factors collectively define the context of an energy meter. Once the context is known, the meters within the same context can be grouped and their behaviour can be analyzed together. This paper presents four heuristics, including one novel heuristic, to identify abnormal energy consumption. Using these heuristics, data collected from fifty smart meters deployed inside hostels of IIIT-Delhi was investigated for abnormal energy consumption detection. The anomalies and possible causes were discussed with IIIT-Delhi campus administrator. Energy consumption per occupant for one of the meters was found four times when compared to rest of the meters. The results demonstrated that the proposed heuristics successfully found abnormal energy consumption behaviour.

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
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Araya, D., Grolinger, K., ElYamany, H., Capretz, M., & Bitsuamlak, G. (2016). Collective contextual anomaly detection framework for smart buildings. In Proceedings of the international joint conference on neural networks.

  2. Saad, A., & Sisworahardjo, N. (2017). Data analytics-based anomaly detection in smart distribution network. In Proceedings of the international conference on high voltage engineering and power system.

  3. Rossi, B., Chren, S., Buhnova, B., Pitner, T. (2016). Anomaly detection in smart grid data: An experience report. In Proceedings of the IEEE international conference on systems, man, and cybernetics.

  4. Ponocko, J., & Milanovic, J. (2017). Application of data analytics for advanced demand profiling of residential load using smart meter data. In Proceedings of the IEEE PowerTech conference.

  5. Chandola, V., Banerjee, A., & Kumar, V. (2012). Anomaly detection for discrete sequences: A survey. IEEE Transactions on Knowledge and Data Engineering, 24(5), 823–839.

    Article  Google Scholar 

  6. Chen, C., & Cook, D. (2011). Energy outlier detection in smart environments. In Artificial intelligence and smarter living 11.

  7. Seem, J. (2007). Using intelligent data analysis to detect abnormal energy consumption in buildings. Energy and Buildings, 39(1), 52–58.

    Article  Google Scholar 

  8. Bellala, G., Marwah, M., Arlitt, M., Lyon, G., & Bash, C. (2011). Towards an understanding of campus-scale power consumption. In Proceedings of the ACM workshop on embedded sensing systems for energy-efficiency in buildings.

  9. Bellala, G., Marwah, M., Arlitt, M., Lyon, G., & Bash, C. (2012). Following the electrons: Methods for power management in commercial buildings. In Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining.

  10. Fontugne, R., Ortiz, J., Tremblay, N., Borgnat, P., Flandrin, P., Fukuda, K., Culler, D., & Esaki, H. (2013). Strip, bind, and search: A method for identifying abnormal energy consumption in buildings. In Proceedings of the international conference on information processing in sensor networks.

  11. Ismail, A., Abdullah, A., Bak, K., Ngadi, Md., Dahlan, D., & Chimphlee, W. (2008). A novel method for unsupervised anomaly detection using unlabelled data. In Proceedings of the international conference on computational sciences and its applications.

  12. Qiu, H., Liu, Y., Subrahmanya, N., & Li, W. (2012). Granger causality for time-series anomaly detection. In Proceedings of the international conference on data mining.

  13. Yip, S., Tan, W., Tan, C., Gan, M., & Wong, K. (2018). An anomaly detection framework for identifying energy theft and defective meters in smart grids. International Journal of Electrical Power and Energy Systems, 101, 189–203.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. IIIT-Delhi, New Delhi, India

    Ankur Sial & Amarjeet Singh

  2. University of Auckland, Auckland, New Zealand

    Aniket Mahanti

Authors
  1. Ankur Sial
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Amarjeet Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aniket Mahanti
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aniket Mahanti.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sial, A., Singh, A. & Mahanti, A. Detecting anomalous energy consumption using contextual analysis of smart meter data. Wireless Netw 27, 4275–4292 (2021). https://doi.org/10.1007/s11276-019-02074-8

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-019-02074-8

Keywords

Search

Navigation