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2018 | OriginalPaper | Buchkapitel

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

verfasst von : Jorge Revuelta Herrero, Álvaro Lozano Murciego, Alberto López Barriuso, Daniel Hernández de la Iglesia, Gabriel Villarrubia González, Juan Manuel Corchado Rodríguez, Rita Carreira

Erschienen in: Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017

Verlag: Springer International Publishing

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Abstract

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.

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Vorheriges Kapitel Single Appliance Automatic Recognition: Comparison of Classifiers

Nächstes Kapitel Learning Frequent Behaviors Patterns in Intelligent Environments for Attentiveness Level

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: 18790690CrossRef

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

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

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

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. http://www.citeulike.org/user/cblock/article/4123593. ISSN: 2169-2637

Egarter, D., Sobe, A., Elmenreich, W.: Non-intrusive load monitoring, pp. 4–6. Springer, Heidelberg (2013). doi:10.1109/5.192069. ISBN: 1424409462

Erhan, D., et al.: Why does unsupervised pre-training help deep learning? J. Mach. Learn. Res. 11, 625–660 (2010). ISSN: 1533-7928

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

Gers, F.A.: J Urgen Schmidhuber, and Fred Cummins. Learning to Forget: Continual Prediction with LSTM (1999)

10.

Gers, F.A., Schraudolph, N.N., Schmidhuber, J.: Learning precise timing with LSTM recurrent networks. J. Mach. Learn. Res. 3, 115–143 (2002)MathSciNetMATH

11.

Gonçalves, H., et al.: Unsupervised disaggregation of appliances using aggregated consumption data. In: Environmental Engineering (2011)

12.

Hart, G.W.: Nonintrusive appliance load monitoring. Proc. IEEE 80(12), 1870–1891 (1992). ISSN: 15582256CrossRef

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: 0899-7667CrossRef

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: 00223751CrossRef

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.

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

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.

Kelly, J.D.: Disaggregation of domestic smart meter energy data, August 2016

19.

Kelly, J., Knottenbelt, W.: Does disaggregated electricity feedback reduce domestic electricity consumption? A systematic review of the literature (2016). arXiv:1605.00962

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 CrossRef

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.

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). http://people.csail.mit.edu/kolter/lib/exe/fetch.php?media=pubs:kolter-aistats12.pdf

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)

24.

Larochelle, H., et al.: Exploring strategies for training deep neural networks. J. Mach. Learn. Res. 10, 1–40 (2009). ISSN: 1533-7928MATH

25.

Laughman, C., et al.: Power signature analysis. IEEE Power Energy Mag. 1(2), 56–63 (2003). doi:10.1109/MPAE.2003.1192027. ISSN: 15407977CrossRef

26.

Lee, K.D.: Electric load information system based on non-intrusive power monitoring (2003)

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: 08858969CrossRef

28.

Makonin, S.: Real-time embedded low-frequency load disaggregation. pp. 1–121, August 2014

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)

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: 19493053

31.

Mehta, V.K.: Principles of Electronics, p. 792. Prentice Hall, Upper Saddle River (1980). ISBN: 0130344060

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: 2196-1115CrossRef

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: 9781577355687

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: 9783540748526

35.

Paulo, P.: Applications of deep learning techniques on NILM (2016)

36.

PG&E energy efficiency gains using behavior – Bidgely Blog. http://www.bidgely.com/blog/pge-pilot-yields-7-7-energy-savings/. Accessed 13 Feb 2017

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.

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)

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: 9781577356158

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: 01996231CrossRef

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: 0885-8977CrossRef

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.

Sultanem, F.: Using appliance signatures for monitoring residential loads at meter panel level. IEEE Trans. Power Deliv. 6(4), 1380–1385 (1991). ISSN: 19374208CrossRef

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.

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: 0018-9448CrossRefMATH

46.

Wichakool, W., et al.: Modeling and estimating current harmonics of variable electronic loads. IEEE Trans. Power Electron. 24(12), 2803–2811 (2009). ISSN: 08858993CrossRef

47.

Wiki NILM Databases. http://wiki.nilm.eu/index.php?title=NILM_dataset. Accessed 09 Feb 2017

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: 00983063MathSciNetCrossRef

49.

Zoha, A., et al.: Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey. Sensors (Switzerland) 12(12), 16838–16866 (2012)CrossRef

Titel: Non Intrusive Load Monitoring (NILM): A State of the Art
verfasst von: Jorge Revuelta Herrero
Álvaro Lozano Murciego
Alberto López Barriuso
Daniel Hernández de la Iglesia
Gabriel Villarrubia González
Juan Manuel Corchado Rodríguez
Rita Carreira
Verlag: Springer International Publishing
Buch: Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection - 15th International Conference, PAAMS 2017
Print ISBN: 978-3-319-61577-6

Electronic ISBN: 978-3-319-61578-3

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-61578-3_12

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