Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Prologue: Artificial Intelligence for Energy Transition Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

5. A Review on Non-intrusive Load Monitoring Approaches Based on Machine Learning

verfasst von : Hajer Salem, Moamar Sayed-Mouchaweh, Moncef Tagina

Erschienen in: Artificial Intelligence Techniques for a Scalable Energy Transition

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Residential energy smart management (RESM) has received considerable momentum in the recent decade considering its strong impact on the total energy consumption and the elaboration of the smart grid. Non-Intrusive Load Monitoring (NILM) is the first brick of the smart grid. In this paper, the importance of NILM in the smart grid is highlighted and its impact on different smart grid issues is discussed. Challenges facing NILM are also explained and existing solutions are reviewed. Mainly, an overview of different machine learning approaches is presented and these methods’ limits are discussed giving rise to open problems in the state of the art.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel A Multi-Agent Approach to Energy Optimisation for Demand-Response Ready Buildings
Nächstes Kapitel Neural Networks and Statistical Decision Making for Fault Diagnosis in Energy Conversion Systems
Fußnoten
1
The SCP assumes that only one appliance ever changes state at any given point in time. This assumption holds if the sampling time is reasonably short.
 
Literatur
1.
M. Baranski, J. Voss, Nonintrusive appliance load monitoring based on an optical sensor, in 2003 IEEE Bologna Power Tech Conference Proceedings, vol. 4 (IEEE, Piscataway, 2003), p. 8
2.
K.S. Barsim, R. Streubel, B. Yang, An approach for unsupervised non-intrusive load monitoring of residential appliances, in Proceedings of the 2nd International Workshop on Non-Intrusive Load Monitoring (2014)
3.
M. Berges, E. Goldman, H.S. Matthews, L. Soibelman, K. Anderson, User-centered nonintrusive electricity load monitoring for residential buildings. J. Comput. Civ. Eng. 25(6), 471–480 (2011)CrossRef
4.
5.
R. Bonfigli, E. Principi, M. Fagiani, M. Severini, S. Squartini, F. Piazza, Non-intrusive load monitoring by using active and reactive power in additive factorial hidden Markov models. Appl. Energy 208, 1590–1607 (2017)CrossRef
6.
C. Dinesh, S. Makonin, I.V. Bajic, Incorporating time-of-day usage patterns into non-intrusive load monitoring, in 2017 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (IEEE, Piscataway, 2017), pp. 1110–1114
7.
D. Egarter, V.P. Bhuvana, W. Elmenreich, PALDi: Online load disaggregation via particle filtering. IEEE Trans. Instrum. Meas. 64(2), 467–477 (2015)CrossRef
8.
G.W. Hart, Nonintrusive appliance load monitoring. Proc. IEEE 80(12), 1870–1891 (1992)CrossRef
9.
S. Inagaki, T. Egami, T. Suzuki, H. Nakamura, K. Ito, Nonintrusive appliance load monitoring based on integer programming. Electr. Eng. Jpn. 174(2), 18–25 (2011)CrossRef
10.
R. Jia, Y. Gao, C.J. Spanos, A fully unsupervised non-intrusive load monitoring framework, in 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm) (IEEE, Piscataway, 2015), pp. 872–878CrossRef
11.
M.J. Johnson, A.S. Willsky, Bayesian nonparametric hidden semi-Markov models. J. Mach. Learn. Res. 14, 673–701 (2013)MathSciNetMATH
12.
J. Kelly, W. Knottenbelt, 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, New York, 2015), pp. 55–64
13.
H. Kim, M. Marwah, M. Arlitt, G. Lyon, J. Han, Unsupervised disaggregation of low frequency power measurements, in Proceedings of the 2011 SIAM International Conference on Data Mining (SIAM, 2011), pp. 747–758
14.
J.Z. Kolter, T. Jaakkola, Approximate inference in additive factorial HMMs with application to energy disaggregation, in Artificial Intelligence and Statistics (2012), pp. 1472–1482
15.
J.Z. Kolter, S. Batra, A.Y. Ng, Energy disaggregation via discriminative sparse coding, in Advances in Neural Information Processing Systems (2010), pp. 1153–1161
16.
H. Lange, M. Bergés, The neural energy decoder: energy disaggregation by combining binary subcomponents, in NILM2016 3rd International Workshop on Non-Intrusive Load Monitoring (2016). https://​www.​nilmworkshop.​org
17.
G.-Y. Lin, S.-C. Lee, J.Y.-J. Hsu, W.-R. Jih, Applying power meters for appliance recognition on the electric panel, in 2010 5th IEEE Conference on Industrial Electronics and Applications (IEEE, 2010), pp. 2254–2259
18.
S. Makonin, Approaches to non-intrusive load monitoring (NILM) in the home (2012)
19.
S. Makonin, Investigating the switch continuity principle assumed in non-intrusive load monitoring (NILM), in 2016 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE) (IEEE, Piscataway, 2016), pp. 1–4
20.
S. Makonin, F. Popowich, Nonintrusive load monitoring (NILM) performance evaluation. Energy Effic. 8(4), 809–814 (2015)CrossRef
21.
S. Makonin, I.V. Bajic, F. Popowich, Efficient sparse matrix processing for nonintrusive load monitoring (NILM), in 2nd International Workshop on Non-Intrusive Load Monitoring (2014)
22.
S. Makonin, F. Popowich, I.V. Bajić, B. Gill, L. Bartram, Exploiting HMM sparsity to perform online real-time nonintrusive load monitoring. IEEE Trans. Smart Grid 7(6), 2575–2585 (2016)CrossRef
23.
A. Marchiori, D. Hakkarinen, Q. Han, L. Earle, Circuit-level load monitoring for household energy management. IEEE Pervasive Comput. 10(1), 40–48 (2011)CrossRef
24.
L. Mauch, B. Yang, A new approach for supervised power disaggregation by using a deep recurrent LSTM network, in 2015 IEEE Global Conference on Signal and Information Processing (GlobalSIP) (IEEE, Piscataway, 2015), pp. 63–67CrossRef
25.
C. Nalmpantis, D. Vrakas, Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation. Artif. Intell. Rev. 52(1), 217–243 (2018)CrossRef
26.
T.A. Nguyen, M. Aiello, Energy intelligent buildings based on user activity: a survey. Energy Build. 56, 244–257 (2013)CrossRef
27.
L.K. Norford, S.B. Leeb, Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms. Energy Build. 24(1), 51–64 (1996)CrossRef
28.
J. Page, D. Robinson, N. Morel, J.-L. Scartezzini, A generalised stochastic model for the simulation of occupant presence. Energy Build. 40(2), 83–98 (2008)CrossRef
29.
F. Paradiso, F. Paganelli, D. Giuli, S. Capobianco, Context-based energy disaggregation in smart homes. Future Internet 8(1), 4 (2016)
30.
O. Parson, S. Ghosh, M.J. Weal, A. Rogers, Non-intrusive load monitoring using prior models of general appliance types, in Proceedings of the Twenty-Sixth AAAI Conference on Artificial Intelligence (2012)
31.
O. Parson, S. Ghosh, M. Weal, A. Rogers, An unsupervised training method for non-intrusive appliance load monitoring. Artif. Intell. 217, 1–19 (2014)CrossRef
32.
33.
A.G. Ruzzelli, C. Nicolas, A. Schoofs, G.M.P. O’Hare, Real-time recognition and profiling of appliances through a single electricity sensor, in 2010 7th Annual IEEE Communications Society Conference on Sensor Mesh and Ad Hoc Communications and Networks (SECON) (IEEE, Piscataway, 2010), pp. 1–9
34.
H. Salem, M. Sayed-Mouchaweh, A.B. Hassine, A review on machine learning and data mining techniques for residential energy smart management, in 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) (IEEE, Piscataway, 2016), pp. 1073–1076
35.
36.
I. Valera, F. Ruiz, L. Svensson, F. Perez-Cruz, Infinite factorial dynamical model, in Advances in Neural Information Processing Systems (2015), pp. 1666–1674
37.
I. Valera, F.J.R. Ruiz, F. Perez-Cruz, Infinite factorial unbounded-state hidden Markov model. IEEE Trans. Pattern Anal. Mach. Intell. 38(9), 1816–1828 (2016)CrossRef
38.
M. Weiss, A. Helfenstein, F. Mattern, T. Staake, Leveraging smart meter data to recognize home appliances, in 2012 IEEE International Conference on Pervasive Computing and Communications (PerCom) (IEEE, Piscataway, 2012), pp. 190–197CrossRef
39.
M. Zeifman, Disaggregation of home energy display data using probabilistic approach. IEEE Trans. Consum. Electron. 58(1), 23–31 (2012)MathSciNetCrossRef
40.
M. Zeifman, K. Roth, Nonintrusive appliance load monitoring: review and outlook. IEEE Trans. Consum. Electron. 57(1), 76–84 (2011)CrossRef
41.
C. Zhang, M. Zhong, Z. Wang, N. Goddard, C. Sutton, Sequence-to-point learning with neural networks for non-intrusive load monitoring, in Thirty-Second AAAI Conference on Artificial Intelligence (2018)
42.
B. Zhao, K. He, L. Stankovic, V. Stankovic, Improving event-based non-intrusive load monitoring using graph signal processing. IEEE Access 6, 53944–53959 (2018)CrossRef
43.
A. Zoha, A. Gluhak, M.A. Imran, S. Rajasegarar, Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey. Sensors 12(12), 16838–16866 (2012)CrossRef
Metadaten
Titel
A Review on Non-intrusive Load Monitoring Approaches Based on Machine Learning
verfasst von
Hajer Salem
Moamar Sayed-Mouchaweh
Moncef Tagina
Copyright-Jahr
2020
Buch
Artificial Intelligence Techniques for a Scalable Energy Transition

Print ISBN: 978-3-030-42725-2

Electronic ISBN: 978-3-030-42726-9

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-42726-9_5

Neuer Inhalt

    Bildnachweise