Non Intrusive Load Monitoring (NILM): A State of the Art

  • Jorge Revuelta HerreroEmail author
  • Álvaro Lozano Murciego
  • Alberto López Barriuso
  • Daniel Hernández de la Iglesia
  • Gabriel Villarrubia González
  • Juan Manuel Corchado Rodríguez
  • Rita Carreira
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 619)


The recent increase in smart meters installations in households and small bussiness by electric companies has led to interest in monitoring load techniques in order to provide better quality service and get useful information about appliance usage and user consumption behavior. This works summarizes the current state of the art in Non Intrusive Load Monitoring from its beginning, describes the main process followed in the literature to perform this technique and shows current methods and techniques followed nowadays. The possible application of this techniques in the context of ambient intelligence, energy efficiency, occupancy detection are described. This work also points the current challenges in the field and the future lines of research in this broad topic.


NILM ILM Dissagregation Ambient intelligence Load monitoring HMM LSTM 



This work has been supported by the European Commission H2020 MSCA-RISE-2014: Marie Skłodowska-Curie project DREAM-GO Enabling Demand Response for short and real-time Efficient And Market Based Smart Grid Operation - An intelligent and real-time simulation approach ref 641794.


  1. 1.
    Abubakar, I., et al.: Application of load monitoring in appliances energy management a review. Renew. Sustain. Energy Rev. 67, 235–245 (2017). doi: 10.1016/j.rser.2016.09.064. ISSN: 18790690CrossRefGoogle Scholar
  2. 2.
    Bhotto, M.Z.A., Makonin, S., Bajic, I.: Load disaggregation based on aided linear integer programming. IEEE Trans. Circuits Syst. II Express Briefs 1 (2016). doi: 10.1109/TCSII.2016.2603479. ISSN: 1549-7747
  3. 3.
    Baranski, M., Voss, J.: Genetic algorithm for pattern detection in NIALM systems. In: IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583), pp. 3462–3468. IEEE (2004). doi: 10.1109/ICSMC.2004.1400878. ISBN: 0-7803-8567-5
  4. 4.
    Basu, K., Hably, A., Debusschere, V., Bacha, S., Driven, G.J., Ovalle, A.: A comparative study of low sampling non intrusive load dis-aggregation. In: IECON Proceedings (Industrial Electronics Conference), pp. 5137–5142 (2016). doi: 10.1109/IECON.2016.7793294
  5. 5.
    Darby, S.: The effectiveness of feedback on energy consumption a review for DEFRA of the literature on metering, billing and direct displays. In: Environmental Change Institute University of Oxford, 22 April, pp. 1–21 (2006). doi: 10.4236/ojee.2013.21002. ISSN: 2169-2637
  6. 6.
    Egarter, D., Sobe, A., Elmenreich, W.: Non-intrusive load monitoring, pp. 4–6. Springer, Heidelberg (2013). doi: 10.1109/5.192069. ISBN: 1424409462
  7. 7.
    Erhan, D., et al.: Why does unsupervised pre-training help deep learning? J. Mach. Learn. Res. 11, 625–660 (2010). ISSN: 1533-7928Google Scholar
  8. 8.
    Figueiredo, M.B., De Almeida, A., Ribeiro, B.: An experimental study on electrical signature identification of non-intrusive load monitoring (NILM) systems. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6594. PART 2, pp. 31–40. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-20267-4_4. ISBN: 9783642202667
  9. 9.
    Gers, F.A.: J Urgen Schmidhuber, and Fred Cummins. Learning to Forget: Continual Prediction with LSTM (1999)Google Scholar
  10. 10.
    Gers, F.A., Schraudolph, N.N., Schmidhuber, J.: Learning precise timing with LSTM recurrent networks. J. Mach. Learn. Res. 3, 115–143 (2002)MathSciNetzbMATHGoogle Scholar
  11. 11.
    Gonçalves, H., et al.: Unsupervised disaggregation of appliances using aggregated consumption data. In: Environmental Engineering (2011)Google Scholar
  12. 12.
    Hart, G.W.: Nonintrusive appliance load monitoring. Proc. IEEE 80(12), 1870–1891 (1992). ISSN: 15582256CrossRefGoogle Scholar
  13. 13.
    Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997). doi: 10.1162/neco.1997.9.8.1735. ISSN: 0899-7667CrossRefGoogle Scholar
  14. 14.
    Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160(1), 106–154 (1962). doi: 10.1113/jphysiol.1962.sp006837. ISSN: 00223751CrossRefGoogle Scholar
  15. 15.
    Jia, R., Gao, Y., Spanos, C.J.: A fully unsupervised nonintrusive load monitoring framework. In: 2015 IEEE International Conference on Smart Grid Communications, SmartGridComm 2015, pp. 872–878 (2016). doi: 10.1109/SmartGridComm.2015.7436411
  16. 16.
    Johnson, M.J., Willsky, A.S.: Bayesian nonparametric hidden semi-Markov models. J. Mach. Learn. Res. 14, 673–701 (2013)MathSciNetzbMATHGoogle Scholar
  17. 17.
    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: Mokhtari, M., Khalil, I., Bauchet, J., Zhang, D., Nugent, C. (eds.) Ambient Assistive Health and Wellness Management in the Heart of the City, ICOST 2009. LNCS, vol. 5597, pp. 150–157. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-02868-7_19
  18. 18.
    Kelly, J.D.: Disaggregation of domestic smart meter energy data, August 2016Google Scholar
  19. 19.
    Kelly, J., Knottenbelt, W.: Does disaggregated electricity feedback reduce domestic electricity consumption? A systematic review of the literature (2016). arXiv:1605.00962
  20. 20.
    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). doi: 10.1038/sdata.2015.7 CrossRefGoogle Scholar
  21. 21.
    Kim, H., Marwah, M., Arlitt, M., Lyon, G., Han, J.: Unsupervised disaggregation of low frequency power measurements. In: Proceedings of the 2011 SIAM International Conference on Data Mining, pp. 747–758 (2011). doi: 10.1137/1.9781611972818.64
  22. 22.
    Kolter, Z., Jaakkola, T., Kolter, J.Z.: Approximate inference in additive factorial HMMs with application to energy disaggregation. In: Proceedings of the International Conference on Artificial Intelligence and Statistics, pp. 1472–1482 (2012).
  23. 23.
    Kolter, J.Z., Johnson, M.J.: REDD : a public data set for energy disaggregation research. In: SustKDD Workshop, vol. 1, pp. 1–6 (2011)Google Scholar
  24. 24.
    Larochelle, H., et al.: Exploring strategies for training deep neural networks. J. Mach. Learn. Res. 10, 1–40 (2009). ISSN: 1533-7928zbMATHGoogle Scholar
  25. 25.
    Laughman, C., et al.: Power signature analysis. IEEE Power Energy Mag. 1(2), 56–63 (2003). doi: 10.1109/MPAE.2003.1192027. ISSN: 15407977CrossRefGoogle Scholar
  26. 26.
    Lee, K.D.: Electric load information system based on non-intrusive power monitoring (2003)Google Scholar
  27. 27.
    Lee, K.D., et al.: Estimation of variable-speed-drive power consumption from harmonic content. IEEE Trans. Energy Convers. 20(3), 566–574 (2005). ISSN: 08858969CrossRefGoogle Scholar
  28. 28.
    Makonin, S.: Real-time embedded low-frequency load disaggregation. pp. 1–121, August 2014Google Scholar
  29. 29.
    Makonin, S., et al.: Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014. Sci. Data 3(160037), 1–12 (2016)Google Scholar
  30. 30.
    Makonin, S., et al.: Exploiting HMM sparsity to perform online realtime nonintrusive load monitoring. IEEE Trans. Smart Grid PP(99), 2575–2585 (2015). doi: 10.1109/TSG.2015.2494592. ISSN: 19493053Google Scholar
  31. 31.
    Mehta, V.K.: Principles of Electronics, p. 792. Prentice Hall, Upper Saddle River (1980). ISBN: 0130344060Google Scholar
  32. 32.
    Najafabadi, M.M., et al.: Deep learning applications and challenges in big data analytics. J. Big Data 2(1), 1 (2015). doi: 10.1186/s40537-014-0007-7. ISSN: 2196-1115CrossRefGoogle Scholar
  33. 33.
    Parson, O., et al.: Non-intrusive load monitoring using prior models of general appliance types. In: Proceedings of the 26th AAAI Conference on Artificial Intelligence, Toronto, pp. 356–362 (2012). ISBN: 9781577355687Google Scholar
  34. 34.
    Patel, S.N., et al.: At the flick of a switch: detecting and classifying unique electrical events on the residential power line (Nominated for the Best Paper Award). In: LNCS, vol. 4717, pp. 271–288. Springer, Heidelberg (2007). ISBN: 9783540748526Google Scholar
  35. 35.
    Paulo, P.: Applications of deep learning techniques on NILM (2016)Google Scholar
  36. 36.
    PG&E energy efficiency gains using behavior – Bidgely Blog. Accessed 13 Feb 2017
  37. 37.
    Ridi, A., Gisler, C., Hennebert, J.: A survey on intrusive load monitoring for appliance recognition. In: Proceedings - International Conference on Pattern Recognition, pp. 3702–3707 (2014). doi: 10.1109/ICPR.2014.636. ISSN: 10514651
  38. 38.
    Roos, J.G., et al.: Using neural networks for non-intrusive monitoring of industrial electrical loads. In: Instrumentation and Measurement Technology Conference (IMTC 1994). Conference Proceedings. 10th Anniversary. Advanced Technologies in I & M, pp. 1115–1118. IEEE (1994)Google Scholar
  39. 39.
    Shao, H., Tech, V., Marwah, M.: A temporal motif mining approach to unsupervised energy disaggregation. In: 1st International Workshop on Non-Intrusive Load Monitoring, pp. 1–2 (2012). ISBN: 9781577356158Google Scholar
  40. 40.
    Shaw, S.R., et al.: Instrumentation for high performance nonintrusive electrical load monitoring. J. Solar Energy Eng. 120(3), 224 (1998). doi: 10.1115/1.2888073. ISSN: 01996231CrossRefGoogle Scholar
  41. 41.
    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). doi: 10.1109/TPWRD.2005.852370. ISSN: 0885-8977CrossRefGoogle Scholar
  42. 42.
    Srinivasarengan, K., et al.: A framework for non intrusive load monitoring using Bayesian inference. In: 2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, July 2013, pp. 427–432. IEEE (2013). doi: 10.1109/IMIS.2013.78. ISBN: 978-0-7695-4974-3
  43. 43.
    Sultanem, F.: Using appliance signatures for monitoring residential loads at meter panel level. IEEE Trans. Power Deliv. 6(4), 1380–1385 (1991). ISSN: 19374208CrossRefGoogle Scholar
  44. 44.
    Vincent, P., Larochelle, H., Bengio, Y., Manzagol, P.-A.: Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th International Conference on Machine Learning - ICML 2008, pp. 1096–1103 (2008). doi: 10.1145/1390156.1390294. ISBN: 9781605582054, ISSN: 1605582050
  45. 45.
    Viterbi, A.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. Theor. 13(2), 260–269 (1967). doi: 10.1109/TIT.1967.1054010. ISSN: 0018-9448CrossRefzbMATHGoogle Scholar
  46. 46.
    Wichakool, W., et al.: Modeling and estimating current harmonics of variable electronic loads. IEEE Trans. Power Electron. 24(12), 2803–2811 (2009). ISSN: 08858993CrossRefGoogle Scholar
  47. 47.
    Wiki NILM Databases. Accessed 09 Feb 2017
  48. 48.
    Zeifman, M.: Disaggregation of home energy display data using probabilistic approach. IEEE Trans. Consum. Electron. 58(1), 23–31 (2012). doi: 10.1109/TCE.2012.6170051. ISSN: 00983063MathSciNetCrossRefGoogle Scholar
  49. 49.
    Zoha, A., et al.: Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey. Sensors (Switzerland) 12(12), 16838–16866 (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Jorge Revuelta Herrero
    • 1
    Email author
  • Álvaro Lozano Murciego
    • 1
  • Alberto López Barriuso
    • 1
  • Daniel Hernández de la Iglesia
    • 1
  • Gabriel Villarrubia González
    • 1
  • Juan Manuel Corchado Rodríguez
    • 1
  • Rita Carreira
    • 2
  1. 1.University of SalamancaSalamancaSpain
  2. 2.VPS - Virtual Power SolutionsCoimbraPortugal

Personalised recommendations