Skip to main content
SpringerLink
Log in

Appliance Identification Through Nonintrusive Load Monitoring in Residences

  • Chapter
  • First Online:
Computational Intelligence and Optimization Methods for Control Engineering

Part of the book series: Springer Optimization and Its Applications ((SOIA,volume 150))

Abstract

Residential energy consumption forms a major part of the total energy expenditure. Consumers, power utilities, grid operators, electric appliance manufacturers, government agencies, and others are greatly interested in curbing the energy consumption, expecting in return financial and environmental rewards. Better understanding of how energy is consumed in residences will be crucial in developing trustworthy Demand Side Management (DSM) systems. This work presents state-of-the-art approaches for disaggregating power consumption in residences through nonintrusive load monitoring. Also, it contributes a new dataset of detailed power consumption data that was captured in a residence that was specially set up. The results show that by analyzing overlapping power patterns that electrical appliances generate, and a resident-level energy meter of adequate granularity, appliance identification becomes possible.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 79.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://ses.jrc.ec.europa.eu/smart-metering-deployment-european-union

  2. 2.

    http://nilmtk.github.io/

References

  1. Anderson, K., Ocneanu, A., Benitez, D., Carlson, D., Rowe, A., Berges, M.: BLUED: a fully labeled public dataset for event-based non-intrusive load monitoring research. In: 2nd KDD Workshop on Data Mining Applications in Sustainability (SustKDD), pp. 12–16 (2012)

    Google Scholar 

  2. Baranski, M., Voss, J.: Genetic algorithm for pattern detection in NIALM Systems. In: Proceedings of IEEE International Conference on Systems Man and Cybernetics vol. 4, pp. 3462–3468 (2004)

    Google Scholar 

  3. Barsim, K.S., Mauch, L., Yang, B.: Neural network ensembles to real-time identification of plug-level appliance measurements. In: Proceedings of the 2016 NILM Workshop (2016)

    Google Scholar 

  4. Batra, N., Gulati, M., Singh, A., Srivastava, M.B.: It’s different: insights into home energy consumption in India. In: Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings, pp. 1–8. ACM (2013)

    Google Scholar 

  5. Batra, N., Kelly, J., Parson, O., Dutta, H., Knottenbelt, W., Rogers, A., Singh, A., Srivastava, M.: NILMTK: An open source toolkit for non-intrusive load monitoring. In: Proceedings of the 5th International Conference on Future Energy Systems, pp. 265–276. ACM (2014)

    Google Scholar 

  6. Egarter, D., Bhuvana, V.P., Elmenreich, W.: PALDi: online load disaggregation via particle filtering. IEEE Trans. Instrum. Meas. 64(2), 467–477 (2015)

    Article  Google Scholar 

  7. Darby, S., et al.: The effectiveness of feedback on energy consumption. A Rev. DEFRA Lit. Metering, Billing Direct Disp. 486(2006), 26 (2006)

    Google Scholar 

  8. Hart, G.W.: Nonintrusive appliance load monitoring. Proc. IEEE 80(12), 1870–1891 (1992)

    Article  Google Scholar 

  9. He, K., Stankovic, L., Liao, J., Stankovic, V.: Non-intrusive load disaggregation using graph signal processing. IEEE Trans. Smart Grid (2016)

    Google Scholar 

  10. Holcomb, C., Pecan street inc.: A test-bed for NILM. In: International Workshop on Non-Intrusive Load Monitoring, Pittsburgh, PA, USA (2012)

    Google Scholar 

  11. Huss, A.: Hybrid model approach to appliance load disaggregation: expressive appliance modelling by combining convolutional neural networks and hidden semi markov models (2015)

    Google Scholar 

  12. Inagaki, S., Egami, T., Suzuki, T., Nakamura, H., Ito, K.: Nonintrusive appliance load monitoring based on integer programming. Electr. Eng. Jpn. 174(2), 18–25 (2011)

    Article  Google Scholar 

  13. Johnson, M.J., Willsky, A.S.: Bayesian nonparametric hidden semi-markov models. J. Mach. Learn. Res. 14(2), 673–701 (2013)

    Google Scholar 

  14. Kato, T., Cho, H.S., Lee, D., Toyomura, T., Yamazaki, T.: Appliance recognition from electric current signals for information-energy integrated network in home environments. In: International Conference on Smart Homes and Health Telematics, pp. 150–157. Springer, Berlin (2009)

    Google Scholar 

  15. Kelly, J., Knottenbelt, W.: Neural NILM: deep neural networks applied to energy disaggregation. In Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments, pp. 55–64. ACM (2015)

    Google Scholar 

  16. Kelly, J., Knottenbelt, W.: The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci. Data 2, 150007 (2015)

    Article  Google Scholar 

  17. Kelly, D.: Disaggregation of domestic smart meter energy data. Ph.D. thesis, Imperial College London, Technology and Medicine (2016)

    Google Scholar 

  18. Kolter, J.Z., Jaakkola, T.: Approximate inference in additive factorial HMMs with application to energy disaggregation. In: Artificial Intelligence and Statistics, pp. 1472–1482, (2012)

    Google Scholar 

  19. Kolter, J.Z., Johnson, M.J.: REDD: a public data set for energy disaggregation research. In: Workshop on Data Mining Applications in Sustainability (SIGKDD), vol. 25, pp. 59–62. San Diego, CA (2011)

    Google Scholar 

  20. Kong, W., Dong, Z.Y., Hill, D.J.: A hierarchical hidden markov model framework for home appliance modelling. IEEE Trans. Smart Grid (2016)

    Google Scholar 

  21. Kong, W., Dong, Z.Y., Hill, D.J., Luo, F., Xu, Y.: Improving nonintrusive load monitoring efficiency via a hybrid programing method. IEEE Trans. Ind. Inform. 12(6), 2148–2157 (2016)

    Article  Google Scholar 

  22. Kong, W., Dong, Z.Y., Ma, J., Hill, D.J., Zhao, J. and Luo, F.: An extensible approach for non-intrusive load disaggregation with smart meter data. IEEE Trans. Smart Grid (2016)

    Google Scholar 

  23. Li, Y., Peng, Z., Huang, J., Zhang, Z., Son, J.H.: Energy disaggregation via hierarchical factorial HMM. In: Proceedings of the 2nd International Workshop on Non-Intrusive Load Monitoring (NILM) (2014)

    Google Scholar 

  24. Makonin, S., Popowich, F., Bartram, L., Gill, B., Bajic, I.V.: AMPds: a public dataset for load disaggregation and eco-feedback research. In: 2013 IEEE, Electrical Power and Energy Conference (EPEC), pp. 1–6. IEEE (2013)

    Google Scholar 

  25. Makonin, S.W.: Real-time embedded low-frequency load disaggregation. Ph.D. thesis, Simon Fraser University (2014)

    Google Scholar 

  26. Marchiori, A., Hakkarinen, D., Han, Q., Earle, L.: Circuit-level load monitoring for household energy management. IEEE Pervasive Comput. 10(1), 40–48 (2011)

    Article  Google Scholar 

  27. Mueller, J.A., Kimball, J.W.: Accurate energy use estimation for nonintrusive load monitoring in systems of known devices. IEEE Trans. Smart Grid (2016)

    Google Scholar 

  28. Pereira, L., Quintal, F., Gonçalves, R., Nunes, N.J.: SustData: a public dataset for ICT4S electric energy research. In ICT for Sustainability 2014 (ICT4S-14). Atlantis Press (2014)

    Google Scholar 

  29. Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manag. 45(4), 427–437 (2009)

    Article  Google Scholar 

  30. Srinivasan, D., Ng, W.S., Liew, A.C.: Neural-network-based signature recognition for harmonic source identification. IEEE Trans. Power Deliv. 21(1), 398–405 (2006)

    Article  Google Scholar 

  31. Tsai, M.-S., Lin, Y.-H.: Modern development of an adaptive non-intrusive appliance load monitoring system in electricity energy conservation. Appl. Energy 96, 55–73 (2012)

    Article  Google Scholar 

  32. Zhao, B., Stankovic, L., Stankovic, V.: On a training-less solution for non-intrusive appliance load monitoring using graph signal processing. IEEE Access 4, 1784–1799 (2016)

    Article  Google Scholar 

  33. Zoha, A., Gluhak, A., Imran, M., Rajasegarar, S.: Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey. Sensors, 12(12), 16838–16866 (2012)

    Article  Google Scholar 

Download references

Acknowledgements

We would like to thank MEAZON (https://meazon.com/) for providing us with the sensors and the gateway that were used in our experiments and for the technical support when needed.

Author information

Authors and Affiliations

  1. Department of Informatics and Telecommunications, University of Ioannina, Kostakioi Campus, 47100, Arta, Greece

    Christos Gogos & George Georgiou

Authors
  1. Christos Gogos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. George Georgiou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Christos Gogos .

Editor information

Editors and Affiliations

  1. Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USA

    Maude Josée Blondin

  2. Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  3. Institute of Automation and Industrial Computer, Polytechnic University of Valencia, Valencia, Spain

    Javier Sanchis Sáez

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Gogos, C., Georgiou, G. (2019). Appliance Identification Through Nonintrusive Load Monitoring in Residences. In: Blondin, M., Pardalos, P., Sanchis Sáez, J. (eds) Computational Intelligence and Optimization Methods for Control Engineering. Springer Optimization and Its Applications, vol 150. Springer, Cham. https://doi.org/10.1007/978-3-030-25446-9_10

Download citation

Publish with us

Policies and ethics

Search

Navigation