Advertisement

A new graph learning-based signal processing approach for non-intrusive load disaggregation with active power measurements

  • Ming-Yue ZhaiEmail author
ATCI 2019

Abstract

Recently, there is a potential technology called graph-based signal processing (GSP) that is being used in many applications. GSP has been used successfully in the domains such as signal and image filtering and processing. In the paper, GSP is used as an applicable method to non-intrusive appliance load monitoring (NILM). In NILM, all of power consumption is disaggregated down to every appliance’s consumption without hardware. Although there is over 30 years after NILM was proposed, there are still some problems faced by applications of NILM in real scenario if there is no training data. By combination of NILM with GSP concept, such a challenge is tackled with better performance over existing methods. As the first step, we propose a new graph learning algorithm to get a graph suitable for appliance load representation and for the disaggregation algorithm. In the following steps, graph-based signal processing method is used three times, from representation of the data sets of power measurements. Public datasets are used to demonstrate the proposed method’s performance and feasibility.

Keywords

Graph-based signal processing Non-intrusive appliance load monitoring Energy disaggregation Graph learning 

Notes

Compliance with ethical standards

Conflict of interest

The authors declared that they have no conflicts of interest to this work.

References

  1. 1.
    Zeifman M, Roth K (2011) Nonintrusive appliance load monitoring: review and outlook. IEEE Trans Consum Electron 57(1):76–84CrossRefGoogle Scholar
  2. 2.
    D. of Energy, C. C. UK (2013) Government response to the consultation on the second version of the smart metering equipment technical specifications: part 2Google Scholar
  3. 3.
    Armel KC, Gupta A, Shrimali A, Albert G (2013) Is disaggregation the holy grail of energy efficiency? The case of electricity. Energy Policy 52:213–234CrossRefGoogle Scholar
  4. 4.
    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.  https://doi.org/10.1109/ACCESS.2016.2557460 CrossRefGoogle Scholar
  5. 5.
    He K, Stankovic L, Liao J, Stankovic V (2017) Non-intrusive load disaggregation using graph signal processing. IEEE Trans Smart Grid.  https://doi.org/10.1109/tsg.2016.2598872 CrossRefGoogle Scholar
  6. 6.
    Giri S, Berges M (2015) An energy estimation framework for event-based methods in non-intrusive load monitoring. Energy Convers Manag 90:488–498CrossRefGoogle Scholar
  7. 7.
    Srinivasan D, Ng WS, Liew AC (2006) Neural-network-based signature recognition for harmonic source identification. IEEE Trans Power Deliv 21(1):398–405.  https://doi.org/10.1109/TPWRD.2005.852370 CrossRefGoogle Scholar
  8. 8.
    Kong W, Dong ZY, Hill DJ, Luo F, Xu Y (2016) Improving nonintrusive load monitoring efficiency via a hybrid programing method. IEEE Trans Ind Inform 12(6):2148–2157.  https://doi.org/10.1109/TII.2016.2590359 CrossRefGoogle Scholar
  9. 9.
    Makonin S, Popowich F, Bajic IV, Gill B, Bartram L (2016) Exploiting hmm sparsity to perform online real-time nonintrusive load monitoring. IEEE Trans Smart Grid 7(6):2575–2585.  https://doi.org/10.1109/TSG.2015.2494592 CrossRefGoogle Scholar
  10. 10.
    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 CrossRefGoogle Scholar
  11. 11.
    Shuman DI, Narang SK, Frossard P, Ortega A, Vandergheynst P (2013) The emerging field of signal processing on graphs: extending high-dimensional data analysis to networks and other irregular domains. IEEE Signal Process Mag 30(3):83–98.  https://doi.org/10.1109/MSP.2012.2235192 CrossRefGoogle Scholar
  12. 12.
    Romero D, Ma M, Giannakis GB (2017) Kernel-based reconstruction of graph signals. IEEE Trans Signal Process 65(3):764–778.  https://doi.org/10.1109/TSP.2016.2620116 MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Mei J, Moura JMF (2016) Signal processing on graphs: causal modeling of unstructured data. IEEE Trans Signal Process.  https://doi.org/10.1109/tsp.2016.2634543 CrossRefzbMATHGoogle Scholar
  14. 14.
    Perraudin N, Paratte J, Shuman D, Martin L, Kalofolias V, Vandergheynst P, Hammond DK (2014) GSPBOX: a toolbox for signal processing on graphs. arXiv eprints arXiv:1408.5781
  15. 15.
    Irion J, Saito N (2016) Efficient approximation and denoising of graph signals using the multiscale basis dictionaries. IEEE Trans Signal Inf Process Over Netw.  https://doi.org/10.1109/tsipn.2016.2632039 CrossRefGoogle Scholar
  16. 16.
    Lorenzo PD, Barbarossa S, Banelli P, Sardellitti S (2016) Adaptive least mean squares estimation of graph signals. IEEE Trans Signal Inf Process Over Netw 2(4):555–568.  https://doi.org/10.1109/TSIPN.2016.2613687 MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Sarand HG, Karimi B (2019) Adaptive consensus tracking of non-square MIMO nonlinear systems with input saturation and input gain matrix under directed graph. Neural Comput Appl 31(7):2171–2182CrossRefGoogle Scholar
  18. 18.
    Behjat H, Richter U, Ville DVD, Sornmo L (2016) Signal-adapted tight frames on graphs. IEEE Trans Signal Process 64(22):6017–6029.  https://doi.org/10.1109/TSP.2016.2591513.25 MathSciNetCrossRefzbMATHGoogle Scholar
  19. 19.
    Huang W, Goldsberry L, Wymbs NF, Grafton ST, Bassett DS, Ribeiro A (2016) Graph frequency analysis of brain signals. IEEE J Sel Topics Signal Process 10(7):1189–1203.  https://doi.org/10.1109/JSTSP.2016.2600859 CrossRefGoogle Scholar
  20. 20.
    Zheng X, Tang YY, Pan J, Zhou J (2016) Adaptive multiscale decomposition of graph signals. IEEE Signal Process Lett 23(10):1389–1393.  https://doi.org/10.1109/LSP.2016.2598750 CrossRefGoogle Scholar
  21. 21.
    Segarra S, Marques AG, Leus G, Ribeiro A (2016) Reconstruction of graph signals through percolation from seeding nodes. IEEE Trans Signal Process 64(16):4363–4378.  https://doi.org/10.1109/TSP.2016 MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Tremblay N, Borgnat P (2016) Subgraph-based filterbanks for graph signals. IEEE Trans Signal Process 64(15):3827–3840.  https://doi.org/10.1109/TSP.2016.2544747 MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Zhang D, Liang J (2017) Graph-based transform for 2-d piecewise smooth signals with random discontinuity locations. IEEE Trans Image Process.  https://doi.org/10.1109/tip.2017.2661399 MathSciNetCrossRefzbMATHGoogle Scholar
  24. 24.
    Fracastoro G, Magli E (2017) Steerable discrete fourier transform. IEEE Signal Process Lett.  https://doi.org/10.1109/lsp.2017.2657889 CrossRefzbMATHGoogle Scholar
  25. 25.
    Sandryhaila A, Moura JMF (2014) Discrete signal processing on graphs: frequency analysis. IEEE Trans Signal Process 62(12):3042–3054.  https://doi.org/10.1109/TSP.2014.2321121 MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Xu X, He L, Lu H, Shimada A, Taniguchi RI (2016) Non-linear matrix completion for social image tagging. IEEE Access.  https://doi.org/10.1109/access.2016.2624267 CrossRefGoogle Scholar
  27. 27.
    Bera D, Chakrabarti I, Pathak S, Karagiannidis G (2016) Another look in the analysis of cooperative spectrum sensing over nakagami-m fading channels. IEEE Trans Wirel Commun.  https://doi.org/10.1109/twc.2016.2633259 CrossRefGoogle Scholar
  28. 28.
    Komodakis N, Pesquet JC (2015) Playing with duality: an overview of recent primal dual approaches for solving large-scale optimization problems. IEEE Signal Process Mag 32(6):31–54.  https://doi.org/10.1109/MSP.2014 CrossRefGoogle Scholar
  29. 29.
    Stankovic V, Liao J, Stankovic L (2014) A graph-based signal processing approach for low-rate energy disaggregation. In: 2014 IEEE symposium on computational intelligence for engineering solutions (CIES), pp 81–87.  https://doi.org/10.1109/cies.2014.7011835
  30. 30.
    Sandryhaila A, Moura JMF (2013) Classification via regularization on graphs. In: 2013 IEEE global conference on signal and information processing, pp 495–498.  https://doi.org/10.1109/globalsip.2013.6736923
  31. 31.
    Zhao B, Stankovic L, Stankovic V (2015) Blind non-intrusive appliance load monitoring using graph-based signal processing. In: 2015 IEEE global conference on signal and information processing (GlobalSIP), pp 68–72.  https://doi.org/10.1109/globalsip.2015.7418158
  32. 32.
    Welikala S, Dinesh C, Godaliyadda V, Ekanayake MPB, Ekanayake J (2016) Robust non-intrusive load monitoring (nilm) with unknown loads. In: 2016 IEEE international conference on information and automation for sustainability (ICIAfS), pp 1–6.  https://doi.org/10.1109/iciafs
  33. 33.
    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 energy conference, pp 1–6.  https://doi.org/10.1109/epec.2013.6802949
  34. 34.
    Kolter J, Jaakkola T (2012) Approximate interence in additive factorial hmms with application to energy disaggregation. J Mach Learn Res 22(1):1472–1482Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Electronic Information EngineeringGuangdong University of Petrochemical TechnologyMaomingChina

Personalised recommendations