Skip to main content

Advertisement

Springer Nature Link
Log in

Non intrusive load monitoring using additive time series modeling via finite mixture models aggregation

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Energy disaggregation, or Non-Intrusive Load Monitoring (NILM), involves different methods aiming to distinguish the individual contribution of appliances, given the aggregated power signal. In this paper, the application of finite Generalized Gaussian and finite Gamma mixtures in energy disaggregation is proposed and investigated. The procedure includes approximation of the distribution of the sum of two Generalized Gaussian random variables (RVs) and the approximation of the distribution of the sum of two Gamma RVs using Method-of-Moments matching. By adopting this procedure, the probability distribution of each combination of appliances consumption is acquired to predict and disaggregate the specific device data from the aggregated data. Moreover, to make the models more practical we propose a deep version, that we call DNN-Mixture, as a cascade model, which is a combination of a deep neural network and each of the proposed mixture models. As part of our extensive evaluation process, we apply the proposed models on three different datasets, from different geographical locations, that had different sampling rates. The results indicate the superiority of proposed models as compared to the Gaussian mixture model and other widely used approaches. In order to investigate the applicability of our models in challenging unsupervised settings, we tested them on unseen houses with unlabeled data. The outcomes proved the extensibility and robustness of the proposed approach. Finally, the evaluation of the cascade model against the state of the art shows that by benefiting from the advantages of both neural networks and finite mixtures, cascade model can produce promising and competing results with RNN without suffering from its inherent disadvantages.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 12
Fig. 13

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

Data availability

Data could be made available on reasonable request.

Notes

  1. https://remodece.isr.uc.pt/

References

  • Abolhassani SS, Zandifar A, Ghourchian N, Amayri M, Bouguila N, Eicker U (2022) Improving residential building energy simulations through occupancy data derived from commercial off-the-shelf wi-fi sensing technology. Energy Build 272:112354

    Article  Google Scholar 

  • Ahmadi-Karvigh S, Becerik-Gerber B, Soibelman L (2016) A framework for allocating personalized appliance-level disaggregated electricity consumption to daily activities. Energy Build 111:337–350

    Article  Google Scholar 

  • Algumaei A, Azam M, Amayri M, Bouguila N (2023) ICA and IVA bounded multivariate generalized gaussian mixture based hidden markov models. Eng Appl Artif Intell 123(Part B):106345

    Article  Google Scholar 

  • Al-Osaimi FR, Bouguila N (2015) A finite gamma mixture model-based discriminative learning frameworks. In: 14th IEEE international conference on machine learning and applications. IEEE, pp 819–824

  • Amiri Z, Heidari A, Navimipour NJ, Unal M, Mousavi A (2023) Adventures in data analysis: a systematic review of deep learning techniques for pattern recognition in cyber-physical-social systems. Multimed Tools Appl 83:22909–22973

    Article  Google Scholar 

  • Arash H, Mohammad AJJ, Jafari NN, Shahin A (2023) A qos-aware technique for computation offloading in iot-edge platforms using a convolutional neural network and markov decision process. IT Prof 25(1):24–39

    Article  Google Scholar 

  • Azam M, Bouguila N (2015) Unsupervised keyword spotting using bounded generalized gaussian mixture model with ica. In: 2015 IEEE global conference on signal and information processing, pp 1150–1154

  • Bansal S, Schmidt M (2017) Energy disaggregation methods for commercial buildings using smart meter and operational data. In: The workshops of the the thirty-first AAAI conference on artificial intelligence. AAAI Press

  • Bonfigli R, Principi E, Fagiani M, Severini M, Squartini S, Piazza F (2017) Non-intrusive load monitoring by using active and reactive power in additive factorial hidden markov models. Appl Energy 208:1590–1607

    Article  Google Scholar 

  • Bouguila N, Fan W (2020) Mixture models and applications. Springer, Berlin

    Book  Google Scholar 

  • Bouguila N, Almakadmeh K, Boutemedjet S (2012) A finite mixture model for simultaneous high-dimensional clustering, localized feature selection and outlier rejection. Expert Syst Appl 39(7):6641–6656

    Article  Google Scholar 

  • Buddhahai B, Wongseree W, Rakkwamsuk P (2020) An energy prediction approach for a nonintrusive load monitoring in home appliances. IEEE Trans Consum Electron 66(1):96–105

    Article  Google Scholar 

  • Dempster Arthur P, Laird Nan M, Rubin Donald B (1977) Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc: Ser B (Methodol) 39(1):1–22

    Article  MathSciNet  Google Scholar 

  • Dey Dipak K, Lynn Kuo, Sahu Sujit K (1995) A bayesian predictive approach to determining the number of components in a mixture distribution. Stat Comput 5(4):297–305

    Article  Google Scholar 

  • Ding J, Wang Y (2020) A wifi-based smart home fall detection system using recurrent neural network. IEEE Trans Consum Electron 66(4):308–317

    Article  Google Scholar 

  • Dridi J, Amayri M, Bouguila N (2022) Transfer learning for estimating occupancy and recognizing activities in smart buildings. Build Environ 217:109057

    Article  Google Scholar 

  • Elguebaly T, Bouguila N (2010) Bayesian learning of generalized gaussian mixture models on biomedical images. In: Artificial neural networks in pattern recognition, 4th IAPR TC3 Workshop, ANNPR, volume 5998 of Lecture Notes in Computer Science. Springer, pp 207–218.

  • Elguebaly T, Bouguila N (2011) Bayesian learning of finite generalized gaussian mixture models on images. Sig Process 91(4):801–820

    Article  Google Scholar 

  • Elguebaly T, Bouguila N (2012) Generalized gaussian mixture models as a nonparametric bayesian approach for clustering using class-specific visual features. J Vis Commun Image Represent 23(8):1199–1212

    Article  Google Scholar 

  • Elguebaly T, Bouguila N (2013) Finite asymmetric generalized gaussian mixture models learning for infrared object detection. Comput Vis Image Understand 117(12):1659–1671

    Article  Google Scholar 

  • Elguebaly T, Bouguila N (2015) A hierarchical nonparametric bayesian approach for medical images and gene expressions classification. Soft Comput 19(1):189–204

    Article  Google Scholar 

  • Elnaz A, Mohammadpour SA, Mohhamd-Taghi H-B, Behnam M-I, Sadegh B (2020) Residential household non-intrusive load monitoring via smart event-based optimization. IEEE Trans Consum Electron 66(3):233–241

    Article  Google Scholar 

  • Faustine A, Mvungi NH, Kaijage S, Michael K (2017) A survey on non-intrusive load monitoring methods and techniques for energy disaggregation problem. arXiv preprint arXiv:1703.00785

  • Hart GW (1992) Nonintrusive appliance load monitoring. Proc IEEE 80(12):1870–1891

    Article  Google Scholar 

  • Heidari A, Navimipour NJ, Jamali MAJ, Akbarpour S (2023a) A green, secure, and deep intelligent method for dynamic iot-edge-cloud offloading scenarios. Sustain Comput Inform Syst 38:100859

    Google Scholar 

  • Heidari A, Navimipour NJ, Jamali MAJ, Akbarpour S (2023b) A hybrid approach for latency and battery lifetime optimization in iot devices through offloading and cnn learning. Sustain Comput Inform Syst 39:100899

    Google Scholar 

  • Jia R, Gao Y, Spanos CJ (2015) A fully unsupervised non-intrusive load monitoring framework. In: 2015 IEEE international conference on smart grid communications. IEEE, pp 872–878

  • Kelly D (2016) Disaggregation of domestic smart meter energy data.https://spiral.imperial.ac.uk/handle/10044/1/49452

  • Kelly J, Knottenbelt W (2015) 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

  • Kolter JZ, Johnson MJ (2011) Redd: a public data set for energy disaggregation research. In: Workshop on data mining applications in sustainability (SIGKDD), San Diego, CA, vol 25, pp 59–62

  • Kumar P, Jawher D, Manar A, Nizar B (2022) Explainable k-means clustering for occupancy estimation. Procedia Computer Science 203:326–333. 17th International Conference on Future Networks and Communications/19th International Conference on Mobile Systems and Pervasive Computing/12th International Conference on Sustainable Energy Information Technology (FNC/MobiSPC/SEIT 2022), August 9-11, 2022, Niagara Falls, Ontario, Canada

  • Makonin S, Popowich F, Bartram L, Gill B, Bajić IV (2013) Ampds: A public dataset for load disaggregation and eco-feedback research. In: 2013 IEEE electrical power & energy conference. IEEE, pp 1–6

  • Marwah M, Arlitt M, Lyon G, Lyons M, Hickman C (2010) Unsupervised disaggregation of low frequency power measurements. Tech. Rep, Technical report, HP Labs

    Google Scholar 

  • Moon Todd K (1996) The expectation-maximization algorithm. IEEE Signal Process Mag 13(6):47–60

    Article  Google Scholar 

  • Rastogi T (2019) Load identification from aggregated data using generative modeling. http://www.diva-portal.org/smash/get/diva2:1304677/FULLTEXT01.pdf

  • Ridi A, Christophe G, Jean H (2016) Aggregation procedure of gaussian mixture models for additive features. In: 2016 23rd international conference on pattern recognition. IEEE, pp 2544–2549

  • Saïd AM, Djemel Z, Nizar B, Sabri B (2010) Image and video segmentation by combining unsupervised generalized gaussian mixture modeling and feature selection. IEEE Trans Circuits Syst Video Technol 20(10):1373–1377

    Article  Google Scholar 

  • Salerno VM, Rabbeni G (2018) An extreme learning machine approach to effective energy disaggregation. Electronics 7(10):235

    Article  Google Scholar 

  • Soury H, Alouini M-S (2015) New results on the sum of two generalized gaussian random variables. In: 2015 IEEE global conference on signal and information processing. IEEE, pp 1017–1021

  • Stewart T, Strijbosch LWG, Moors H, van Batenburg P (2007) A simple approximation to the convolution of gamma distributions. https://doi.org/10.2139/ssrn.900109

  • Webb Andrew R (2000) Gamma mixture models for target recognition. Pattern Recogn 33(12):2045–2054

    Article  Google Scholar 

  • Yang D, Gao X, Kong L, Pang Y, Zhou B (2020) An event-driven convolutional neural architecture for non-intrusive load monitoring of residential appliance. IEEE Trans Consum Electron 66(2):173–182

    Article  Google Scholar 

  • Yang L, Fan W, Bouguila N (2022) Clustering analysis via deep generative models with mixture models. IEEE Trans Neural Netw Learn Syst 33(1):340–350

    Article  MathSciNet  Google Scholar 

  • Zamzami N, Amayri M, Bouguila N, Ploix S (2019) Online clustering for estimating occupancy in an office setting. In: 2019 IEEE 28th international symposium on industrial electronics (ISIE), pp 2195–2200

  • Zeinal-Kheiri S, Shotorbani AM, Mohammadi-Ivatloo B (2020) Residential load disaggregation considering state transitions. IEEE Trans Ind Inform 16(2):743–753

    Article  Google Scholar 

  • Zhao Q, Li H, Shen Y (2004) On the sum of generalized gaussian random signals. In: Proceedings 7th international conference on signal processing. IEEE, vol 1, pp 50–53

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

    Article  Google Scholar 

  • Zhou X, Feng J, Li Y (2021) Non-intrusive load decomposition based on cnn-lstm hybrid deep learning model. Energy Rep 7:5762–5771

    Article  Google Scholar 

  • Ziou D, Bouguila N (2004) Unsupervised learning of a finite gamma mixture using MML: application to SAR image analysis. In: 17th international conference on pattern recognition. IEEE Computer Society, pp 68–71

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

    Article  Google Scholar 

Download references

Acknowledgements

The completion of this research was made possible thanks to the Natural Sciences and Engineering Research Council of Canada (NSERC) and a start-up grant form Concordia University. The authors would like to tank the reviewers for their helpful comments.

Author information

Authors and Affiliations

  1. Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montreal, QC, H3G 1T7, Canada

    Soudabeh Tabarsaii, Manar Amayri & Nizar Bouguila

  2. Department of Building, Civil and Environmental Engineering (BCEE), Concordia University, Montreal, QC, H3G 1T7, Canada

    Ursula Eicker

Authors
  1. Soudabeh Tabarsaii
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Manar Amayri
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Nizar Bouguila
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Ursula Eicker
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Nizar Bouguila.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tabarsaii, S., Amayri, M., Bouguila, N. et al. Non intrusive load monitoring using additive time series modeling via finite mixture models aggregation. J Ambient Intell Human Comput 15, 3359–3378 (2024). https://doi.org/10.1007/s12652-024-04814-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-024-04814-x

Keywords