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Advances in non-intrusive load monitoring for the industrial domain: Challenges, insights, and path forward

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  • Yaniv, Arbel
  • Beck, Yuval

Abstract

Non-intrusive load monitoring has emerged as an important tool for disaggregating the energy consumption of individual appliances within a facility, relying solely on data from a centralized smart meter. Smart meters have become increasingly predominant in industrial facilities for many reasons, such as regulatory mandates, economic incentives, environmental considerations, and operational advantages. However, the implementation and research of non-intrusive load monitoring specifically for the industrial environment, which is notably complex, are still in their early stages.

Suggested Citation

  • Yaniv, Arbel & Beck, Yuval, 2025. "Advances in non-intrusive load monitoring for the industrial domain: Challenges, insights, and path forward," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).
    • Handle: RePEc:eee:rensus:v:210:y:2025:i:c:s1364032124008621
    DOI: 10.1016/j.rser.2024.115136
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    References listed on IDEAS

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    1. Tanoni, Giulia & Principi, Emanuele & Squartini, Stefano, 2024. "Non-Intrusive Load Monitoring in industrial settings: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
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    5. Oladayo S. Ajani & Abhishek Kumar & Rammohan Mallipeddi & Swagatam Das & Ponnuthurai Nagaratnam Suganthan, 2022. "Benchmarking Optimization-Based Energy Disaggregation Algorithms," Energies, MDPI, vol. 15(5), pages 1-19, February.
    6. Li, Chuyi & Zheng, Kedi & Guo, Hongye & Chen, Qixin, 2023. "A mixed-integer programming approach for industrial non-intrusive load monitoring," Applied Energy, Elsevier, vol. 330(PA).
    7. Petros Papageorgiou & Dimitra Mylona & Konstantinos Stergiou & Aggelos S. Bouhouras, 2023. "A Time-Driven Deep Learning NILM Framework Based on Novel Current Harmonic Distortion Images," Sustainability, MDPI, vol. 15(17), pages 1-14, August.
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