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A Scoping Review of Energy Load Disaggregation

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Progress in Artificial Intelligence (EPIA 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14116))

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Abstract

Energy load disaggregation can contribute to balancing power grids by enhancing the effectiveness of demand-side management and promoting electricity-saving behavior through increased consumer awareness. However, the field currently lacks a comprehensive overview. To address this gap, this paper conducts a scoping review of load disaggregation domains, data types, and methods, by assessing 72 full-text journal articles. The findings reveal that domestic electricity consumption is the most researched area, while others, such as industrial load disaggregation, are rarely discussed. The majority of research uses relatively low-frequency data, sampled between 1 and 60 s. A wide variety of methods are used, and artificial neural networks are the most common, followed by optimization strategies, Hidden Markov Models, and Graph Signal Processing approaches.

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References

  1. Ma, Z., J⊘rgensen, B.N.: Energy flexibility of the commercial greenhouse growers: The potential and benefits of participating in the electricity market. In: 2018 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT) (2018)

    Google Scholar 

  2. Massidda, L., Marrocu, M.: A bayesian approach to unsupervised, non-intrusive load disaggregation. Sensors 22(12) (2022)

    Google Scholar 

  3. Værbak, M., et al.: Agent-based modelling of demand-side flexibility adoption in reservoir pumping. In: 2019 IEEE Sciences and Humanities International Research Conference (SHIRCON) (2019)

    Google Scholar 

  4. Xu, Y.Z., Milanovic, J.V.: Artificial-intelligence-based methodology for load disaggregation at bulk supply point. IEEE Trans. Power Syst. 30(2), 795–803 (2015)

    Article  Google Scholar 

  5. Hart, G.W.: Nonintrusive appliance load monitoring. Proc. IEEE 80(12), 1870–1891 (1992)

    Article  Google Scholar 

  6. Howard, D.A., Ma, Z., Jørgensen, B.N.: Evaluation of industrial energy flexibility potential: A scoping review (2021)

    Google Scholar 

  7. Ma, Z.G.: Scoping review in one week: A cookbook for beginners to conduct scoping reviews and write review papers with simple steps (2022). Amazon.com

    Google Scholar 

  8. Chalmers, C., et al.: Detecting activities of daily living and routine behaviours in dementia patients living alone using smart meter load disaggregation. IEEE Trans. Emerg. Top. Comput. 10(1), 157–169 (2022)

    Article  Google Scholar 

  9. Zhou, G., et al.: Sequence-to-sequence load disaggregation using multiscale residual neural network. IEEE Trans. Instrum. Meas. 70 (2021)

    Google Scholar 

  10. Zeinal-Kheiri, S., Shotorbani, A.M., Mohammadi-Ivatloo, B.: Residential load disaggregation considering state transitions. IEEE Trans. Industr. Inf. 16(2), 743–753 (2020)

    Article  Google Scholar 

  11. Massidda, L., Marrocu, M., Manca, S.: Non-intrusive load disaggregation by convolutional neural network and multilabel classification. Appl. Sci.-Basel 10(4) (2020)

    Google Scholar 

  12. Yuan, Q.B., et al.: A fusion load disaggregation method based on clustering algorithm and support vector regression optimization for low sampling data. Future Internet 11(2) (2019)

    Google Scholar 

  13. Xia, M., et al.: Dilated residual attention network for load disaggregation. Neural Comput. Appl. 31(12), 8931–8953 (2019)

    Article  Google Scholar 

  14. Xia, M., et al.: Non-intrusive load disaggregation based on deep dilated residual network. Electr. Power Syst. Res. 170, 277–285 (2019)

    Article  Google Scholar 

  15. Marceau, M.L., Zmeureanu, R.: Nonintrusive load disaggregation computer program to estimate the energy consumption of major end uses in residential buildings. Energy Convers. Manage. 41(13), 1389–1403 (2000)

    Article  Google Scholar 

  16. Green, C., Garimella, S.: Analysis of supervised graph signal processing-based load disaggregation for residential demand-side management. Electr. Power Syst. Res. 208 (2022)

    Google Scholar 

  17. Zou, M. Z., et al.: Heating and lighting load disaggregation using frequency components and convolutional bidirectional long short-term memory method. Energies 14(16) (2021)

    Google Scholar 

  18. Deb, C., et al.: Automated load disaggregation for residences with electrical resistance heating. Energy Build. 182, 61–74 (2019)

    Article  Google Scholar 

  19. Sun, J.X., et al.: Power load disaggregation of households with solar panels based on an improved long short-term memory network. J. Electr. Eng. & Technol. 15(5), 2401–2413 (2020)

    Article  Google Scholar 

  20. Liu, Y., et al.: A robust non-intrusive load disaggregation method with roof-top photovoltaics. Electr. Power Syst. Res. 208 (2022)

    Google Scholar 

  21. Wang, W., et al.: Bats: An appliance safety hazards factors detection algorithm with an improved nonintrusive load disaggregation method. Energies 14(12) (2021)

    Google Scholar 

  22. Doherty, B., Trenbath, K.: Device-level plug load disaggregation in a zero energy office building and opportunities for energy savings. Energy Build. 204 (2019)

    Google Scholar 

  23. Rafsanjani, H.N., et al.: A load-disaggregation framework to sense personalized energy-use information in commercial buildings. Energy Build. 207 (2020)

    Google Scholar 

  24. Jazizadeh, F., et al.: Spatiotemporal lighting load disaggregation using light intensity signal. Energy Build. 69, 572–583 (2014)

    Article  Google Scholar 

  25. Xiao, Z.W., et al.: Cooling load disaggregation using a NILM method based on random forest for smart buildings. Sustain. Cities Soc. 74 (2021)

    Google Scholar 

  26. Oskouei, M.Z., et al.: Optimal scheduling of demand response aggregators in industrial parks based on load disaggregation algorithm. IEEE Syst. J. 16(1), 945–953 (2022)

    Article  MathSciNet  Google Scholar 

  27. Tavakoli, S., Khalilpour, K.: A practical load disaggregation approach for monitoring industrial users demand with limited data availability. Energies 14(16) (2021)

    Google Scholar 

  28. Ebrahimi, M., Rastegar, M., Arefi, M.M.: Real-time estimation frameworks for feeder-level load disaggregation and PEVs’ charging behavior characteristics extraction. IEEE Trans. Industr. Inf. 18(7), 4715–4724 (2022)

    Article  Google Scholar 

  29. Liu, Q., et al.: A sensory similarities approach to load disaggregation of charging stations in internet of electric vehicles. IEEE Sens. J. 21(14), 15895–15903 (2021)

    Article  Google Scholar 

  30. Wang, J.Y., et al.: A data-driven pivot-point-based time-series feeder load disaggregation method. IEEE Trans. Smart Grid 11(6), 5396–5406 (2020)

    Article  Google Scholar 

  31. Wang, H.J., et al.: An optimal load disaggregation method based on power consumption pattern for low sampling data. Sustainability 11(1) (2019)

    Google Scholar 

  32. Wang, H.J., Yang, W.R.: An iterative load disaggregation approach based on appliance consumption pattern. Appl. Sci.-Basel 8(4) (2018)

    Google Scholar 

  33. Kong, W.C., et al.: An extensible approach for non-intrusive load disaggregation with smart meter data. IEEE Trans. Smart Grid 9(4), 3362–3372 (2018)

    Article  Google Scholar 

  34. Bhotto, M.Z.A., Makonin, S., Bajic, I.V.: Load disaggregation based on aided linear integer programming. IEEE Trans. Circuits Syst. Ii-Express Briefs 64(7), 792–796 (2017)

    Google Scholar 

  35. Pochacker, M., Egarter, D., Elmenreich, W.: Proficiency of power values for load disaggregation. IEEE Trans. Instrum. Meas. 65(1), 46–55 (2016)

    Article  Google Scholar 

  36. Wang, S.Y., et al.: Electric vehicle load disaggregation based on limited activation matching pursuits. Innov. Solut. Energy Transit. 158, 2611–2616 (2019)

    Google Scholar 

  37. Lemes, D.A.M., et al.: Load disaggregation based on time window for HEMS application. Ieee Access 9, 70746–70757 (2021)

    Article  Google Scholar 

  38. Qi, B., Liu, L.Y., Wu, X.: Low-rate nonintrusive load disaggregation for resident load based on graph signal processing. IEEJ Trans. Electr. Electron. Eng. 13(12), 1833–1834 (2018)

    Article  Google Scholar 

  39. Liu, Q., et al.: Improving wireless indoor non-intrusive load disaggregation using attention-based deep learning networks. Phys. Commun 51 (2022)

    Google Scholar 

  40. Aiad, M., Lee, P.H.: Non-intrusive load disaggregation with adaptive estimations of devices main power effects and two-way interactions. Energy Build. 130, 131–139 (2016)

    Article  Google Scholar 

  41. Wong, Y.F., Drummond, T., Sekercioglu, Y.A.: Real-time load disaggregation algorithm using particle-based distribution truncation with state occupancy model. Electron. Lett. 50(9), 697–698 (2014)

    Article  Google Scholar 

  42. Feng, R.H., et al.: Nonintrusive load disaggregation for residential users based on alternating optimization and downsampling. IEEE Trans. Instrum. Meas. 70 (2021)

    Google Scholar 

  43. Moradzadeh, A., et al.: Improving residential load disaggregation for sustainable development of energy via principal component analysis. Sustainability 12(8) (2020)

    Google Scholar 

  44. Yang, C.C., Soh, C.S., Yap, V.V.: A systematic approach in load disaggregation utilizing a multi-stage classification algorithm for consumer electrical appliances classification. Front. Energy 13(2), 386–398 (2019)

    Article  Google Scholar 

  45. Zhang, L.L., et al.: Assessment metrics for unsupervised non-intrusive load disaggregation learning algorithms. Pract. Appl. Intell. Syst., Iske 2014(279), 197–206 (2013)

    Google Scholar 

  46. Fang, Y.F., et al.: Non-intrusive load disaggregation based on a feature reused long short-term memory multiple output network. Buildings 12(7) (2022)

    Google Scholar 

  47. Abbas, M.Z., et al.: An adaptive-neuro fuzzy inference system based-hybrid technique for performing load disaggregation for residential customers. Sci. Rep. 12(1) (2022)

    Google Scholar 

  48. Alkhulaifi, A., Aljohani, A.J.: Investigation of deep learning-based techniques for load disaggregation, low-frequency approach. Int. J. Adv. Comput. Sci. Appl. 11(1), 701–706 (2020)

    Google Scholar 

  49. Yan, L., et al.: EFHMM: Event-based factorial hidden markov model for real-time load disaggregation. IEEE Trans. Smart Grid 13(5), 3844–3847 (2022)

    Article  MathSciNet  Google Scholar 

  50. Xia, D., Ba, S.S., Ahmadpour, A.: Non-intrusive load disaggregation of smart home appliances using the IPPO algorithm and FHM model. Sustain. Cities Soc. 67 (2021)

    Google Scholar 

  51. Weng, L.G., et al.: Non-intrusive load disaggregation based on a multi-scale attention residual network. Appl. Sci.-Basel 10(24) (2020)

    Google Scholar 

  52. Gois, J., Pereira, L., Nunes, N.: A data-centric analysis of the impact of non-electric data on the performance of load disaggregation algorithms. Sensors 22(18) (2022)

    Google Scholar 

  53. Eskander, M.M., Silva, C.A.: A complementary unsupervised load disaggregation method for residential loads at very low sampling rate data. Sustain. Energy Technol. Assess. 43 (2021)

    Google Scholar 

  54. Fan, W., et al.: Multi-objective non-intrusive load disaggregation based on appliances characteristics in smart homes. Energy Rep. 7, 4445–4459 (2021)

    Article  Google Scholar 

  55. Zhao, B.C., et al.: Non-intrusive load disaggregation solutions for very low-rate smart meter data. Appl. Energy 268 (2020)

    Google Scholar 

  56. Kolter, J.: And M, p. 25. A Public Data Set for Energy Disaggregation Research. Artif. Intell, Johnson, REDD (2011)

    Google Scholar 

  57. Kong, S., et al.: Home appliance load disaggregation using cepstrum-smoothing-based method. IEEE Trans. Consum. Electron. 61(1), 24–30 (2015)

    Article  MathSciNet  Google Scholar 

  58. de Souza, W.A., et al.: Load disaggregation using microscopic power features and pattern recognition. Energies 12(14) (2019)

    Google Scholar 

  59. Quek, Y.T., Woo, W.L., Logenthiran, T.: Load disaggregation using one-directional convolutional stacked long short-term memory recurrent neural network. IEEE Syst. J. 14(1), 1395–1404 (2020)

    Article  Google Scholar 

  60. Song, J., Lee, Y., Hwang, E.: Time-frequency mask estimation based on deep neural network for flexible load disaggregation in buildings. IEEE Trans. Smart Grid 12(4), 3242–3251 (2021)

    Article  Google Scholar 

  61. Kahl, M., et al.: WHITED—A Worldwide Household and Industry Transient Energy Data Set (2016)

    Google Scholar 

  62. Liu, H., et al.: An improved non-intrusive load disaggregation algorithm and its application. Sustain. Cities Soc. 53 (2020)

    Google Scholar 

  63. Chui, K.T., et al.: Handling data heterogeneity in electricity load disaggregation via optimized complete ensemble empirical mode decomposition and wavelet packet transform. Sensors 21(9) (2021)

    Google Scholar 

  64. Qureshi, M., Ghiaus, C., Ahmad, N.: A blind event-based learning algorithm for non-intrusive load disaggregation. Int. J. Electr. Power & Energy Syst. 129 (2021)

    Google Scholar 

  65. Xia, M., et al.: Non-intrusive load disaggregation based on composite deep long short-term memory network. Expert. Syst. Appl. 160 (2020)

    Google Scholar 

  66. Qi, B., Liu, L.Y., Wu, X.: Low-rate non-intrusive load disaggregation with graph shift quadratic form constraint. Appl. Sci.-Basel 8(4) (2018)

    Google Scholar 

  67. Dash, S., Sodhi, R., Sodhi, B.: An appliance load disaggregation scheme using automatic state detection enabled enhanced integer programming. IEEE Trans. Industr. Inf. 17(2), 1176–1185 (2021)

    Article  Google Scholar 

  68. Lima, D.A., M.Z.C. Oliveira, Zuluaga, E.O.: Non-intrusive load disaggregation model for residential consumers with Fourier series and optimization method applied to White tariff modality in Brazil. Electr. Power Syst. Res. 184 (2020)

    Google Scholar 

  69. Guo, Y., et al.: Research on non-intrusive load disaggregation method based on multi-model combination. Electr. Power Syst. Res.. 200 (2021)

    Google Scholar 

  70. Zhai, M.-Y.: A new graph learning-based signal processing approach for non-intrusive load disaggregation with active power measurements. Neural Comput. Appl. 32(10), 5495–5504 (2019). https://doi.org/10.1007/s00521-019-04623-w

    Article  Google Scholar 

  71. He, K.H., Stankovic, V., Stankovic, L.: Building a graph signal processing model using dynamic time warping for load disaggregation. Sensors 20(22) (2020)

    Google Scholar 

  72. He, K.H., et al.: Non-intrusive load disaggregation using graph signal processing. IEEE Trans. Smart Grid 9(3), 1739–1747 (2018)

    Article  MathSciNet  Google Scholar 

  73. Yu, J.Y., et al.: Non-intrusive load disaggregation by linear classifier group considering multi-feature integration. Appl. Sci.-Basel 9(17) (2019)

    Google Scholar 

  74. Sahrane, S., Adnane, M., Haddadi, M.: Multi-label load disaggregation in presence of non-targeted loads. Electr. Power Syst. Res. 199 (2021)

    Google Scholar 

  75. Vanting, N.B., Ma, Z., Jørgensen, B.N.: A scoping review of deep neural networks for electric load forecasting. Energy Inform. 4(2), 49 (2021)

    Article  Google Scholar 

  76. Piccialli, V., Sudoso, A.M.: Improving non-intrusive load disaggregation through an attention-based deep neural network. Energies 14(4) (2021)

    Google Scholar 

  77. Kwak, Y., Hwang, J., Lee, T.: Load disaggregation via pattern recognition: A feasibility study of a novel method in residential building. Energies 11(4) (2018)

    Google Scholar 

  78. Aiad, M., Lee, P.H.: Unsupervised approach for load disaggregation with devices interactions. Energy Build. 116, 96–103 (2016)

    Article  Google Scholar 

  79. Egarter, D., Bhuvana, V.P., Elmenreich, W.: PALDi: Online load disaggregation via particle filtering. IEEE Trans. Instrum. Meas. 64(2), 467–477 (2015)

    Article  Google Scholar 

  80. Chahine, K., et al.: Electric load disaggregation in smart metering using a novel feature extraction method and supervised classification. Impact Integr. Clean Energy Futur. Mediterr. Environ. 6, 627–632 (2011)

    Google Scholar 

  81. Ma, Z., Jørgensen, B.N.: A discussion of building automation and stakeholder engagement for the readiness of energy flexible buildings. Energy Inform. 1(1), 54 (2018)

    Article  Google Scholar 

  82. Ma, Z., et al.: An overview of digitalization for the building-to-grid ecosystem. Energy Inform. 4(2), 36 (2021)

    Article  Google Scholar 

  83. Ma, Z.: Survey data on university students’ experience of energy control, indoor comfort, and energy flexibility in campus buildings. Energy Inform. 5(4), 50 (2022)

    Article  Google Scholar 

  84. Howard, D.A., et al.: Greenhouse industry 4.0—digital twin technology for commercial greenhouses. Energy Inform. 4(S2) (2021)

    Google Scholar 

  85. Howard, D.A., Ma, Z., Jørgensen, B.N.: Digital Twin Framework for Energy Efficient Greenhouse Industry 4.0. in Ambient Intelligence—Software and Applications. Springer International Publishing, Cham (2021)

    Google Scholar 

  86. Ma, Z., Christensen, K., Jorgensen, B.N.: Business ecosystem architecture development: A case study of electric vehicle home charging. Energy Inform. 4, 37 (2021)

    Article  Google Scholar 

  87. Ma, Z., et al.: Ecosystem-driven business opportunity identification method and web-based tool with a case study of the electric vehicle home charging energy ecosystem in Denmark. Energy Inform. 5(4), 54 (2022)

    Article  Google Scholar 

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Acknowledgements

This paper is part of the project “Data-dreven smarte bygninger: data sandkasse og konkurrence” (Journalnummer: 64021-6025) by EUDP (Energy Technology Development and Demonstration Program), Denmark.

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Authors and Affiliations

  1. SDU Center for Energy Informatics, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, 5230, Odense, Denmark

    Balázs András Tolnai, Zheng Ma & Bo Nørregaard Jørgensen

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  1. Balázs András Tolnai
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  2. Zheng Ma
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  3. Bo Nørregaard Jørgensen
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Correspondence to Balázs András Tolnai .

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Editors and Affiliations

  1. Lucy Family Institute for Data and Society, Notre Dame, IN, USA

    Nuno Moniz

  2. GECAD, Polytechnic of Porto, Porto, Portugal

    Zita Vale

  3. GRIA—LIACC, University of Azores, Ponta Delgada, Portugal

    José Cascalho

  4. CISUC, University of Coimbra, Coimbra, Portugal

    Catarina Silva

  5. IEETA, University of Aveiro, Aveiro, Portugal

    Raquel Sebastião

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Tolnai, B.A., Ma, Z., Jørgensen, B.N. (2023). A Scoping Review of Energy Load Disaggregation. In: Moniz, N., Vale, Z., Cascalho, J., Silva, C., Sebastião, R. (eds) Progress in Artificial Intelligence. EPIA 2023. Lecture Notes in Computer Science(), vol 14116. Springer, Cham. https://doi.org/10.1007/978-3-031-49011-8_17

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  • DOI: https://doi.org/10.1007/978-3-031-49011-8_17

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