Skip to main content

Steady-State Current Decomposition Based Appliance Identification

  • Chapter
  • First Online:
Non-intrusive Load Monitoring
  • 387 Accesses

Abstract

The steady state current decomposition based appliance identification methods are reviewed. The application of the classical load decomposition model, harmonic phasor based current decomposition model and the non-negative matrix factor based model are discussed in detail.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • 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. https://doi.org/10.1016/j.apenergy.2017.08.203

    Article  Google Scholar 

  • Bouhouras AS, Chatzisavvas KC, Panagiotou E, Poulakis N, Parisses C, Christoforidis GC (2017) Load signatures development via harmonic current vectors. In: International universities power engineering conference

    Google Scholar 

  • Bouhouras AS, Gkaidatzis PA, Panagiotou E, Poulakis N, Christoforidis GC (2019) A NILM algorithm with enhanced disaggregation scheme under harmonic current vectors. Energy Build 183:392–407. https://doi.org/10.1016/j.enbuild.2018.11.013

    Article  Google Scholar 

  • Bouhouras AS, Milioudis AN, Andreou GT, Labridis DP (2012) Load signatures improvement through the determination of a spectral distribution coefficient for load identification. In: 2012 9th international conference on the European energy market, 10–12 May 2012, pp 1–6. https://doi.org/10.1109/eem.2012.6254662

  • Chang HH, Lin LS, Chen N, Lee WJJIToIA (2013) Particle-swarm-optimization-based nonintrusive demand monitoring and load identification in smart meters. IEEE Trans Ind Appl 49(5):2229–2236

    Article  Google Scholar 

  • Coello CAC, Lechuga MS (2002) MOPSO: a proposal for multiple objective particle swarm optimization. In: Proceedings of the 2002 congress on evolutionary computation CEC ’02 (Cat. No.02TH8600), vol 1052, 12–17 May 2002, pp 1051–1056. https://doi.org/10.1109/cec.2002.1004388

  • Coello CAC, Pulido GT, Lechuga MS (2004) Handling multiple objectives with particle swarm optimization. IEEE Trans Evol Comput 8(3):256–279

    Article  Google Scholar 

  • Darby S (2006) The effectiveness of feedback on energy consumption. A Rev DEFRA Lit Metering, Billing Direct Disp 486(2006):26

    Google Scholar 

  • De Baets L, Develder C, Dhaene T, Deschrijver D (2019) Detection of unidentified appliances in non-intrusive load monitoring using siamese neural networks. Int J Electr Power Energy Syst 104:645–653. https://doi.org/10.1016/j.ijepes.2018.07.026

    Article  Google Scholar 

  • Deb K, Agrawal S, Pratap A, Meyarivan T (2000) A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In: International conference on parallel problem solving from nature. Springer, pp 849–858

    Google Scholar 

  • Fan YC, Liu X, Lee WC, Chen ALP (2012) Efficient time series disaggregation for non-intrusive appliance load monitoring. In: International conference on ubiquitous intelligence and computing and international conference on autonomic and trusted computing, pp 248–255

    Google Scholar 

  • García D, Díaz I, Pérez D, Cuadrado AA, Domínguez M, Morán A (2018) Interactive visualization for NILM in large buildings using non-negative matrix factorization. Energy Build 176:95–108. https://doi.org/10.1016/j.enbuild.2018.06.058

    Article  Google Scholar 

  • Gonzalez-Pardo A, Ser J, Camacho D (2015) On the applicability of ant colony optimization to non-intrusive load monitoring in smart grids. In: Conference of the Spanish association for artificial intelligence

    Google Scholar 

  • Hock D, Kappes M, Ghita B (2018) Non-intrusive appliance load monitoring using genetic algorithms. In: Conference series materials science and engineering, vol 366(1), p 012003

    Article  Google Scholar 

  • Jawlik AA (2016) r, Multiple R, r2, R2, R square, R2 adjusted. In: Statistics from A to Z. Wiley

    Google Scholar 

  • Jian L, Ng SKK, Kendall G, Cheng JWMJIToPD (2010) Load signature study—part II: disaggregation framework, simulation, and applications. IEEE Trans Power Deliv 25(2):561–569

    Google Scholar 

  • Kahl M, Haq AU, Kriechbaumer T, Jacobsen HA (2016) WHITED—a worldwide household and industry transient energy data set. In: International workshop on non-intrusive load monitoring, pp 1–5

    Google Scholar 

  • Kennedy J (1995) Particle swarm optimization. In: Sammut C, Webb GI (eds) Encyclopedia of machine learning. Springer US, Boston, MA, pp 760–766. https://doi.org/10.1007/978-0-387-30164-8_630

    Chapter  Google Scholar 

  • Lee DD, Seung HSJ (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401(6755):788

    Article  Google Scholar 

  • Lin Y-H, Tsai M-S (2015) The integration of a genetic programming-based feature optimizer with fisher criterion and pattern recognition techniques to non-intrusive load monitoring for load identification. Int J Green Energy 12(3):279–290

    Article  Google Scholar 

  • Liu H, Yu C, Wu H, Chen C, Wang Z (2019) An improved non-intrusive load disaggregation algorithm and its application. Sustain Cities Soc: 101918. https://doi.org/10.1016/j.scs.2019.101918

    Article  Google Scholar 

  • Liu Y, Wang X, Zhao L, Liu YJE, Buildings (2018a) Admittance-based load signature construction for non-intrusive appliance load monitoring. Energy Build 171:209–219

    Article  Google Scholar 

  • Liu Y, Xue W, Lin Z, Liu YJE, Buildings (2018b) Admittance-based load signature construction for non-intrusive appliance load monitoring. Energy Buil 171:209–219

    Article  Google Scholar 

  • Makonin S, Popowich F, Bajić IV, Gill B, Bartram LJIToSG (2016) Exploiting HMM sparsity to perform online real-time nonintrusive load monitoring (NILM). IEEE Trans Smart Grid 7(6):2575–2585

    Article  Google Scholar 

  • Miyasawa A, Fujimoto Y, Hayashi Y (2019) Energy disaggregation based on smart metering data via semi-binary nonnegative matrix factorization. Energy Build 183:547–558. https://doi.org/10.1016/j.enbuild.2018.10.030

    Article  Google Scholar 

  • Piga D, Cominola A, Giuliani M, Castelletti A, Rizzoli AEJIToCST (2016) Sparse optimization for automated energy end use disaggregation. IEEE Trans Control Syst Technol 24(3):1044–1051

    Article  Google Scholar 

  • Rahimpour A, Qi H, Fugate D, Kuruganti TJIToPS (2017) Non-intrusive energy disaggregation using non-negative matrix factorization with sum-to-k constraint. IEEE Trans Power Syst (99):1

    Google Scholar 

  • Saleh A, Held P, Benyoucef D, Abdeslam DO (2018) A novel procedure for virtual measurements generation suitable for training and testing in the context of non intrusive load monitoring. In: Signal 2018 editors, p 36

    Google Scholar 

  • Tabatabaei SM, Dick S, Xu WJIToSG (2017) Towards non-intrusive load monitoring via multi-label classification. IEEE Trans Smart Grid (99):1

    Google Scholar 

  • Tang G, Wu K, Lei J, Tang J (2014) A simple model-driven approach to energy disaggregation. In: IEEE international conference on smart grid communications, pp 566–571

    Google Scholar 

  • Willmott CJ, Matsuura KJ (2005) Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Res 30(1):79–82

    Article  Google Scholar 

  • Yang J, Zhou J, Liu L, Li YJC, Mw Applications (2009) A novel strategy of pareto-optimal solution searching in multi-objective particle swarm optimization (MOPSO). Comput Math Appl 57(11):1995–2000

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hui Liu .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Science Press and Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Liu, H. (2020). Steady-State Current Decomposition Based Appliance Identification. In: Non-intrusive Load Monitoring. Springer, Singapore. https://doi.org/10.1007/978-981-15-1860-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-981-15-1860-7_5

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1859-1

  • Online ISBN: 978-981-15-1860-7

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics