Abstract
Non-intrusive load monitoring (NILM) is the prevailing method used to monitor the energy profile of a domestic building and disaggregate the total power consumption into consumption signals by appliance. Whilst the most popular disaggregation algorithms are based on Hidden Markov Model solutions based on deep neural networks have attracted interest from researchers. The objective of this paper is to provide a comprehensive overview of the NILM method and present a comparative review of modern approaches. In this effort, many obstacles are identified. The plethora of metrics, the variety of datasets and the diversity of methodologies make an objective comparison almost impossible. An extensive analysis is made in order to scrutinize these problems. Possible solutions and improvements are suggested, while future research directions are discussed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Notes
The authors would like to thank Odyssefs Krystalakos for his contribution in developing the software and running the experiments for the appliance set complexity. The source code is available at: https://github.com/christofernal/power-disaggregation-complexity.
References
Aiad M, Lee PH (2016) Unsupervised approach for load disaggregation with devices interactions. Energy Build 116:96–103. https://doi.org/10.1016/j.enbuild.2015.12.043
Anderson K, Ocneanu AF, Benitez D, Carlson D, Rowe A, Bergés M (2012) BLUED? A fully labeled public dataset for event-based non-intrusive load monitoring research. In: Proceedings of the 2nd KDD workshop on data mining applications in sustainability (SustKDD), Oct 2011, pp 1–5
Armel CK, Gupta A, Shrimali G, Albert A (2013) Is disaggregation the holy grail of energy efficiency? The case of electricity. Energy Policy 52:213–234. https://doi.org/10.1016/j.enpol.2012.08.062
Baranski M, Voss J (2003) Non-intrusive appliance load monitoring based on an optical sensor. In: 2003 IEEE Bologna power tech conference proceedings, vol 4, pp 267–274. https://doi.org/10.1109/PTC.2003.1304732
Batra N, Kelly J, Parson O, Dutta H, Knottenbelt W, Rogers A, Srivastava M (2014) NILMTK? An open source toolkit for non-intrusive load monitoring categories and subject descriptors. In: International conference on future energy systems (ACM E-Energy), pp 1–4. https://doi.org/10.1145/2602044.2602051
Batra N, Parson O, Berges M, Singh A, Rogers A (2014) A comparison of non-intrusive load monitoring methods for commercial and residential buildings. Retrieved from arXiv:1408.6595 [Cs]
Beckel C, Kleiminger W, Staake T, Santini S (2014) The ECO data set and the performance of non-intrusive load monitoring algorithms. In: Proceedings of the 1st ACM conference on embedded systems for energy-efficient buildings, pp 80–89. https://doi.org/10.1145/2674061.2674064
Bonfigli R, Squartini S, Fagiani M, Piazza F (2015) Unsupervised algorithms for non-intrusive load monitoring: an up-to-date overview. In: 2015 IEEE 15th international conference on environment and electrical engineering, EEEIC 2015—conference proceedings, pp 1175–1180. https://doi.org/10.1109/EEEIC.2015.7165334
Burbano D (2015). Intrusive and non-intrusive load monitoring (a survey). Latin Am J Comput LAJC 2(1):45–53. Retrieved from https://mail-attachment.googleusercontent.com/attachment/u/0/?ui=2&ik=a8b4c7ac5b&view=att&th=14e3165cca971edb&attid=0.2&disp=inline&realattid=f_ibe10fe21&safe=1&zw&saddbat=ANGjdJ_AVSj69eWsgIDKUm0W9eHeDXeCzMksC_qs-I4333QRnHUGyg5RlEFzoxH62VE5QrorrWywBPR9t4B
Chang HH, Chien PC, Lin LS, Chen N (2011) Feature extraction of non-intrusive load-monitoring system using genetic algorithm in smart meters. In: Proceedings—2011 8th IEEE international conference on e-business engineering, ICEBE 2011, pp 299–304. https://doi.org/10.1109/ICEBE.2011.48
Department of Energy and Climate Change (2008) Climate change act, Tech rep, UK Retrieved from http://www.legislation.gov.uk/ukpga/2008/27/pdfs/ukpga_20080027_en.pdf
Darby S (2006) The effectiveness of feedback on energy consumption a review for defra of the literature on metering, billing and direct displays. Retrieved from http://www.eci.ox.ac.uk/research/energy/downloads/smart-metering-report.pdf
Dong R, Ratliff L, Ohlsson H, Sastry SS (2013) Fundamental limits of nonintrusive load monitoring. In: 3rd ACM international conference on high confidence networked systems (HiCoNS), pp 11–18. https://doi.org/10.1145/2566468.2576849
Egarter D, Bhuvana VP, Elmenreich W (2015) PALDi: online load disaggregation via particle filtering. IEEE Trans Instrum Meas 64(2):467–477. https://doi.org/10.1109/TIM.2014.2344373
Egarter D, Pöchacker M, Elmenreich W (2015) Complexity of power draws for load disaggregation 1–26. https://arxiv.org/abs/1501.02954
Eia (2017) Annual Energy Outlook 2017 with projections to 2050. Retrieved from https://www.eia.gov/outlooks/aeo/pdf/0383(2017).pdf
Froehlich J, Larson E, Gupta S, Cohn G, Reynolds M, Patel S (2011) Disaggregated end-use energy sensing for the smart grid. IEEE Perv Comput 10(1):28–39. https://doi.org/10.1109/MPRV.2010.74
Greveler U, Justus B, Loehr D (2012) Forensic content detection through power consumption. In: IEEE international conference on communications, pp 6759–6763. https://doi.org/10.1109/ICC.2012.6364822
Hart GW (1992) Nonintrusive appliance load monitoring. Proc IEEE 80(12):1870–1891
Inagaki S, Egami T, Suzuki T, Nakamura H, Ito K (2011) Nonintrusive appliance load monitoring based on integer programming. Electr Eng Jpn (English Translation of Denki Gakkai Ronbunshi) 174(2):1386–1392. https://doi.org/10.1002/eej.21040
Johnson MJ, Willsky AS (2013) Bayesian nonparametric hidden semi-Markov models. 14:673–701. arXiv Preprint Retrieved fromarxiv:1203.1365
Kamat PV (2007) Meeting the clean energy demand: nanostructure architectures for solar energy conversion. https://doi.org/10.1021/jp066952u
Kato T, Cho HS, Lee D, Toyomura T, Yamazaki T (2009) Appliance recognition from electric current signals for information-energy integrated network in home environments. In: Lecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol 5597, LNCS, pp 150–157. https://doi.org/10.1007/978-3-642-02868-7_19
Katos V, Vrakas D, Katsaros P (2011) A framework for access control with inference constraints. In: Proceedings—international computer software and applications conference, pp 289–297. https://doi.org/10.1109/COMPSAC.2011.45
Kelly J, Knottenbelt W (2015a) 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. ACM, pp 55–64
Kelly J, Knottenbelt W (2015b) The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci Data 2:150007. https://doi.org/10.1038/sdata.2015.7
Kim H, Marwah M, Arlitt MF, Lyon G, Han J (2011) Unsupervised disaggregation of low frequency power measurements. In: Proceedings of the 11th SIAM international conference on data mining, pp 747–758. https://doi.org/10.1137/1.9781611972818.64
Kolter JZ, Johnson MJ (2011) REDD? A public data set for energy disaggregation research. SustKDD Workshop, no (1), pp 1–6. Retrieved from http://users.cis.fiu.edu/~lzhen001/activities/KDD2011Program/workshops/WKS10/doc/SustKDD3.pdf
Kolter Z, Jaakkola T (2012) Approximate inference in additive factorial HMMs with application to energy disaggregation. In: Proceedings of the international conference on artificial intelligence and statistics, vol XX, pp 1472–1482. Retrieved from http://people.csail.mit.edu/kolter/lib/exe/fetch.php?media=pubs:kolter-aistats12.pdf
Krizhevsky A, Sutskever I, Geoffrey EH (2012) ImageNet classification with deep convolutional neural networks. In: Advances in neural information processing systems 25 (NIPS2012), pp 1–9. https://doi.org/10.1109/5.726791
Lai YX, Lai CF, Huang YM, Chao HC (2013) Multi-appliance recognition system with hybrid SVM/GMM classifier in ubiquitous smart home. Inf Sci 230:39–55. https://doi.org/10.1016/j.ins.2012.10.002
Lange H, Bergés M (2016) The neural energy decoder: energy disaggregation by combining binary subcomponents. In: NILM2016 3rd international workshop on non-intrusive load monitoring. Retrieved from nilmworkshop.org
Laughman C, Lee K, Cox R, Shaw S, Leeb S, Norford L, Armstrong P (2003) Power signature analysis. IEEE Power Energy Mag 1(2):56–63. https://doi.org/10.1109/MPAE.2003.1192027
Liang J, Ng SKK, Kendall G, Cheng JWM (2010) Load signature studypart I: basic concept, structure, and methodology. IEEE Trans Power Deliv 25(2):551–560. https://doi.org/10.1109/TPWRD.2009.2033799
Lin GY, Lee SC, Hsu JYJ, Jih WR (2010) Applying power meters for appliance recognition on the electric panel. In: Proceedings of the 2010 5th IEEE conference on industrial electronics and applications, ICIEA 2010, pp 2254–2259. https://doi.org/10.1109/ICIEA.2010.5515385
Makonin S, Popowich F (2015) Nonintrusive load monitoring (NILM) performance evaluation: a unified approach for accuracy reporting. Energy Effic 8(4):809–814. https://doi.org/10.1007/s12053-014-9306-2
Makonin S, Popowich F, Bajic IV, Gill B, Bartram L (2015) Exploiting HMM sparsity to perform online real-time nonintrusive load monitoring. IEEE Trans Smart Grid. https://doi.org/10.1109/TSG.2015.2494592
Makonin S, Popowich F, Bartram L, Gill B, Bajic IV (2013) AMPds: a public dataset for load disaggregation and eco-feedback research. In: 2013 IEEE electrical power and energy conference, EPEC 2013. https://doi.org/10.1109/EPEC.2013.6802949
Marchiori A, Hakkarinen D, Han Q, Earle L (2011) Circuit-level load monitoring for household energy management. IEEE Perv Comput 10(1):40–48. https://doi.org/10.1109/MPRV.2010.72
Mauch L, Yang B (2015) A new approach for supervised power disaggregation by using a deep recurrent LSTM network. In proceedings of the 3 rd IEEE global conference on signal and information processing (GlobalSIP), pp 63–67
Paradiso F, Paganelli F, Giuli D, Capobianco S (2016) Context-based energy disaggregation in smart homes. Future Internet 8(1):4. Retrieved from http://www.mdpi.com/1999-5903/8/1/4
Parson O, Ghosh S, Weal M, Rogers A (2011) Using hidden Markov models for iterative non-intrusive appliance monitoring. Electronics and Computer Science, University of Southampton, Hampshire, UK, pp 1–4. Retrieved from http://eprints.soton.ac.uk/272990/
Parson O, Ghosh S, Weal M, Rogers A (2012) Non-intrusive load monitoring using prior models of general appliance types. In: Proceedings of the 26th AAAI conference on artificial intelligence, pp 356–362
Parson O, Ghosh S, Weal M, Rogers A (2014) An unsupervised training method for non-intrusive appliance load monitoring. Artif Intell 217:1–19. https://doi.org/10.1016/j.artint.2014.07.010
Polat H, Du W, Renckes S, Oysal Y, Polat H, Renckes S, Du W (2010) Private predictions on hidden Markov models. Artif Intell Rev 34:53–72. https://doi.org/10.1007/s10462-010-9161-2
Ruzzelli AG, Nicolas C, Schoofs A, O’Hare GMP (2010) Real-time recognition and profiling of appliances through a single electricity sensor. In: SECON 2010—2010 7th Annual IEEE communications society conference on sensor, mesh and ad hoc communications and networks. https://doi.org/10.1109/SECON.2010.5508244
Sankar L, Raj Rajagopalan S, Mohajer S, Vincent Poor H (2013) Smart meter privacy: a theoretical framework. IEEE Trans Smart Grid 4(2):837–846. https://doi.org/10.1109/TSG.2012.2211046
Srinivasan D, Ng WS, Liew AC (2006) Neural-network-based signature recognition for harmonic source identification. IEEE Trans Power Deliv 21(1):398–405
Wytock M, Kolter JZ (2013) Contextually supervised source separation with application to energy disaggregation. In: Twenty-eighth AAAI conference on artificial intelligence, pp 1–10. Retrieved from arXiv:1312.5023
Zachary CL (2015) A critical review of recurrent neural networks for sequence learning. arXiv Preprint, pp 1–35. https://doi.org/10.1145/2647868.2654889
Zeifman M (2012) Disaggregation of home energy display data using probabilistic approach. IEEE Trans Consum Electron 58(1):23–31. https://doi.org/10.1109/TCE.2012.6170051
Zeifman M, Roth K (2011) Nonintrusive appliance load monitoring: review and outlook. IEEE Trans Consum Electron 57(1):76–84. https://doi.org/10.1109/TCE.2011.5735484
Zhang C, Zhong M, Wang Z, Goddard N, Sutton C (2017) Sequence-to-point learning with neural networks for non-intrusive load monitoring. Retrieved from https://pdfs.semanticscholar.org/b519/ffec16d6256b872c6c108023a64d02646293.pdf
Zhong M, Goddard N, Sutton C (2015) Latent Bayesian melding for integrating individual and population models. In: Advances in neural information processing systems, pp 3618–3626. http://papers.nips.cc/paper/5756-latent-bayesianmelding-for-integrating-individual-and-population-models.pdf
Zia T, Bruckner D, Zaidi A (2011) A hidden Markov model based procedure for identifying household electric loads. In: IECON proceedings (industrial electronics conference), pp 3218–3223. https://doi.org/10.1109/IECON.2011.6119826
Zoha A, Gluhak A, Imran MA, Rajasegarar S (2012) Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey. Sensors (Switzerland) 12(12):16838–16866. https://doi.org/10.3390/s121216838
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nalmpantis, C., Vrakas, D. Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation. Artif Intell Rev 52, 217–243 (2019). https://doi.org/10.1007/s10462-018-9613-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-018-9613-7