Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
The Power of the “Pursuit” Learning Paradigm in the Partitioning of Data Read chapter Read first chapter

2019 | OriginalPaper | Chapter

A Benchmark Framework to Evaluate Energy Disaggregation Solutions

Authors : Nikolaos Symeonidis, Christoforos Nalmpantis, Dimitris Vrakas

Published in: Engineering Applications of Neural Networks

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Energy Disaggregation is the task of decomposing a single meter aggregate energy reading into its appliance level subcomponents. The recent growth of interest in this field has lead to development of many different techniques, among which Artificial Neural Networks have shown remarkable results. In this paper we propose a categorization of experiments that should serve as a benchmark, along with a baseline of results, to efficiently evaluate the most important aspects for this task. Furthermore, using this benchmark we investigate the application of Stacking on five popular ANNs. The models are compared on three metrics and show that Stacking can help improve or ensure performance in certain cases, especially on 2-state devices.

Dont have a licence yet? Then find out more about our products and how to get one now:

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!

inform now

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!

inform now

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!

inform now
previous chapter The Power of the “Pursuit” Learning Paradigm in the Partitioning of Data
next chapter Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting
Literature
1.
2.
3.
4.
Hart, G.W.: Nonintrusive appliance load monitoring. Proc. IEEE 80(12), 1870–1891 (1992)CrossRef
5.
6.
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)CrossRef
7.
8.
Kolter, J.Z., Johnson, M.J.: 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 (2011)
9.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
10.
Krystalakos, O., Nalmpantis, C., Vrakas, D.: Sliding window approach for online energy disaggregation using artificial neural networks. In: Proceedings of the 10th Hellenic Conference on Artificial Intelligence, SETN 2018, pp. 7:1–7:6. ACM, New York (2018). https://​doi.​org/​10.​1145/​3200947.​3201011
11.
Lange, H., Bergés, M.: The neural energy decoder: energy disaggregation by combining binary subcomponents (2016)
12.
Mauch, L., Yang, B.: 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), pp. 63–67. IEEE (2015)
13.
Nalmpantis, C., Vrakas, D.: Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation. Artif. Intell. Rev. 1–27 (2018)
14.
15.
Parson, O., Ghosh, S., Weal, M., Rogers, A.: Non-intrusive load monitoring using prior models of general appliance types. In: Twenty-Sixth AAAI Conference on Artificial Intelligence (2012)
16.
Todorovski, L., Džeroski, S.: Combining classifiers with meta decision trees. Mach. Learn. 50(3), 223–249 (2003)CrossRef
17.
Zeifman, M.: Disaggregation of home energy display data using probabilistic approach. IEEE Trans. Consum. Electron. 58(1), 23–31 (2012)MathSciNetCrossRef
18.
Zhang, C., Zhong, M., Wang, Z., Goddard, N., Sutton, C.: Sequence-to-point learning with neural networks for non-intrusive load monitoring. In: Thirty-Second AAAI Conference on Artificial Intelligence (2018)
19.
Zhong, M., Goddard, N., Sutton, C.: Signal aggregate constraints in additive factorial HMMs, with application to energy disaggregation. In: Advances in Neural Information Processing Systems, pp. 3590–3598 (2014)
Metadata
Title
A Benchmark Framework to Evaluate Energy Disaggregation Solutions
Authors
Nikolaos Symeonidis
Christoforos Nalmpantis
Dimitris Vrakas
Copyright Year
2019
Book
Engineering Applications of Neural Networks

Print ISBN: 978-3-030-20256-9

Electronic ISBN: 978-3-030-20257-6

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-20257-6_2

Premium Partner

    Image Credits