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Non-Intrusive Load Monitoring in industrial settings: A systematic review

Author

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  • Tanoni, Giulia
  • Principi, Emanuele
  • Squartini, Stefano

Abstract

Addressing climate change and promoting sustainable energy practices is a pressing issue in our era and requires a comprehensive transformation of our energy production, transportation, and consumption methods. This is particularly relevant for industries and residential buildings, which together represent 70% of the total electricity demand. The Smart Grid, with its real-time data collection and renewable energy management capabilities, promotes energy awareness and intelligent energy transactions. Recent studies have highlighted the untapped potential for energy reduction in industrial settings. However, the effective deployment of advanced methodologies to enhance energy efficiency in these settings necessitates a detailed understanding of consumption patterns Non-Intrusive Load Monitoring (NILM), a technique that disaggregates consumption at the device level, has been identified as a key strategy in this context. Although NILM has been widely applied in residential settings, the need for its application in industrial settings is increasingly acknowledged, given the complexities associated with installing electrical sensors in such environments. Notably, the last review dedicated to NILM for industrial settings was published in 2015. In light of the growing body of research, this work aims, for the first time, to systematically collect, organize, and analyze the literature published to date. This is crucial for the ongoing research in this field and to highlight open challenges and limitations.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:rensus:v:202:y:2024:i:c:s1364032124004295
    DOI: 10.1016/j.rser.2024.114703
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    References listed on IDEAS

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    1. 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).
    2. Carrie Armel, K. & Gupta, Abhay & Shrimali, Gireesh & Albert, Adrian, 2013. "Is disaggregation the holy grail of energy efficiency? The case of electricity," Energy Policy, Elsevier, vol. 52(C), pages 213-234.
    3. Abubakar, I. & Khalid, S.N. & Mustafa, M.W. & Shareef, Hussain & Mustapha, M., 2017. "Application of load monitoring in appliances’ energy management – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 235-245.
    4. Rudberg, Martin & Waldemarsson, Martin & Lidestam, Helene, 2013. "Strategic perspectives on energy management: A case study in the process industry," Applied Energy, Elsevier, vol. 104(C), pages 487-496.
    5. Hosseini, Sayed Saeed & Agbossou, Kodjo & Kelouwani, Sousso & Cardenas, Alben, 2017. "Non-intrusive load monitoring through home energy management systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1266-1274.
    6. Vito Introna & Annalisa Santolamazza & Vittorio Cesarotti, 2024. "Integrating Industry 4.0 and 5.0 Innovations for Enhanced Energy Management Systems," Energies, MDPI, vol. 17(5), pages 1-16, March.
    7. Anthony Faustine & Lucas Pereira, 2020. "Improved Appliance Classification in Non-Intrusive Load Monitoring Using Weighted Recurrence Graph and Convolutional Neural Networks," Energies, MDPI, vol. 13(13), pages 1-15, July.
    8. Patrick Huber & Alberto Calatroni & Andreas Rumsch & Andrew Paice, 2021. "Review on Deep Neural Networks Applied to Low-Frequency NILM," Energies, MDPI, vol. 14(9), pages 1-34, April.
    9. Tang, Yugui & Yang, Kuo & Zhang, Shujing & Zhang, Zhen, 2022. "Photovoltaic power forecasting: A hybrid deep learning model incorporating transfer learning strategy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    10. Yongtao Shi & Xiaodong Zhao & Fan Zhang & Yaguang Kong, 2022. "Non-Intrusive Load Monitoring Based on Swin-Transformer with Adaptive Scaling Recurrence Plot," Energies, MDPI, vol. 15(20), pages 1-18, October.
    11. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Man, Yi, 2022. "Industrial artificial intelligence based energy management system: Integrated framework for electricity load forecasting and fault prediction," Energy, Elsevier, vol. 244(PB).
    12. Tuballa, Maria Lorena & Abundo, Michael Lochinvar, 2016. "A review of the development of Smart Grid technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 710-725.
    13. Hafsa Bousbiat & Yassine Himeur & Iraklis Varlamis & Faycal Bensaali & Abbes Amira, 2023. "Neural Load Disaggregation: Meta-Analysis, Federated Learning and Beyond," Energies, MDPI, vol. 16(2), pages 1-22, January.
    14. Wang, Zhongrui & Xu, Yonghai & He, Sheng & Yuan, Jindou & Yang, Heng & Pan, Mingming, 2023. "A non-intrusive method of industrial load disaggregation based on load operating states and improved grey wolf algorithm," Applied Energy, Elsevier, vol. 351(C).
    15. Sara Tavakoli & Kaveh Khalilpour, 2021. "A Practical Load Disaggregation Approach for Monitoring Industrial Users Demand with Limited Data Availability," Energies, MDPI, vol. 14(16), pages 1-27, August.
    16. Patrik Thollander & Jenny Palm, 2015. "Industrial Energy Management Decision Making for Improved Energy Efficiency—Strategic System Perspectives and Situated Action in Combination," Energies, MDPI, vol. 8(6), pages 1-10, June.
    17. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    18. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Effective non-intrusive load monitoring of buildings based on a novel multi-descriptor fusion with dimensionality reduction," Applied Energy, Elsevier, vol. 279(C).
    19. Veronica Piccialli & Antonio M. Sudoso, 2021. "Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network," Energies, MDPI, vol. 14(4), pages 1-16, February.
    20. A S M Monjurul Hasan & Andrea Trianni, 2020. "A Review of Energy Management Assessment Models for Industrial Energy Efficiency," Energies, MDPI, vol. 13(21), pages 1-21, November.
    21. Jiangang Lu & Ruifeng Zhao & Bo Liu & Zhiwen Yu & Jinjiang Zhang & Zhanqiang Xu, 2023. "An Overview of Non-Intrusive Load Monitoring Based on V-I Trajectory Signature," Energies, MDPI, vol. 16(2), pages 1-15, January.
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