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Review on Deep Neural Networks Applied to Low-Frequency NILM

Author

Listed:
  • Patrick Huber

    (iHomeLab, Engineering and Architecture, Lucerne University of Applied Sciences and Arts, 6048 Horw, Switzerland)

  • Alberto Calatroni

    (iHomeLab, Engineering and Architecture, Lucerne University of Applied Sciences and Arts, 6048 Horw, Switzerland)

  • Andreas Rumsch

    (iHomeLab, Engineering and Architecture, Lucerne University of Applied Sciences and Arts, 6048 Horw, Switzerland)

  • Andrew Paice

    (iHomeLab, Engineering and Architecture, Lucerne University of Applied Sciences and Arts, 6048 Horw, Switzerland)

Abstract

This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e., data with sampling rates lower than the AC base frequency. The overall purpose of this review is, firstly, to gain an overview on the state of the research up to November 2020, and secondly, to identify worthwhile open research topics. Accordingly, we first review the many degrees of freedom of these approaches, what has already been done in the literature, and compile the main characteristics of the reviewed publications in an extensive overview table. The second part of the paper discusses selected aspects of the literature and corresponding research gaps. In particular, we do a performance comparison with respect to reported mean absolute error (MAE) and F 1 -scores and observe different recurring elements in the best performing approaches, namely data sampling intervals below 10 s, a large field of view, the usage of generative adversarial network (GAN) losses, multi-task learning, and post-processing. Subsequently, multiple input features, multi-task learning, and related research gaps are discussed, the need for comparative studies is highlighted, and finally, missing elements for a successful deployment of NILM approaches based on deep neural networks are pointed out. We conclude the review with an outlook on possible future scenarios.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2390-:d:541765
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    References listed on IDEAS

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    1. Hasan Rafiq & Xiaohan Shi & Hengxu Zhang & Huimin Li & Manesh Kumar Ochani, 2020. "A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing," Energies, MDPI, vol. 13(9), pages 1-26, May.
    2. Chao Min & Guoquan Wen & Zhaozhong Yang & Xiaogang Li & Binrui Li, 2019. "Non-Intrusive Load Monitoring System Based on Convolution Neural Network and Adaptive Linear Programming Boosting," Energies, MDPI, vol. 12(15), pages 1-23, July.
    3. Ying Zhang & Bo Yin & Yanping Cong & Zehua Du, 2020. "Multi-State Household Appliance Identification Based on Convolutional Neural Networks and Clustering," Energies, MDPI, vol. 13(4), pages 1-12, February.
    4. İsmail Hakkı ÇAVDAR & Vahid FARYAD, 2019. "New Design of a Supervised Energy Disaggregation Model Based on the Deep Neural Network for a Smart Grid," Energies, MDPI, vol. 12(7), pages 1-18, March.
    5. Marco Fagiani & Roberto Bonfigli & Emanuele Principi & Stefano Squartini & Luigi Mandolini, 2019. "A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks," Energies, MDPI, vol. 12(7), pages 1-26, April.
    6. Changho Shin & Seungeun Rho & Hyoseop Lee & Wonjong Rhee, 2019. "Data Requirements for Applying Machine Learning to Energy Disaggregation," Energies, MDPI, vol. 12(9), pages 1-19, May.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano & Saad Dosse Bennani & Hakim El Fadili, 2022. "Non-Intrusive Load Monitoring of Household Devices Using a Hybrid Deep Learning Model through Convex Hull-Based Data Selection," Energies, MDPI, vol. 15(3), pages 1-22, February.
    2. Krzysztof Dowalla & Piotr Bilski & Robert Łukaszewski & Augustyn Wójcik & Ryszard Kowalik, 2022. "Application of the Time-Domain Signal Analysis for Electrical Appliances Identification in the Non-Intrusive Load Monitoring," Energies, MDPI, vol. 15(9), pages 1-20, May.
    3. Everton Luiz de Aguiar & André Eugenio Lazzaretti & Bruna Machado Mulinari & Daniel Rodrigues Pipa, 2021. "Scattering Transform for Classification in Non-Intrusive Load Monitoring," Energies, MDPI, vol. 14(20), pages 1-20, October.
    4. Li, Dandan & Li, Jiangfeng & Zeng, Xin & Stankovic, Vladimir & Stankovic, Lina & Xiao, Changjiang & Shi, Qingjiang, 2023. "Transfer learning for multi-objective non-intrusive load monitoring in smart building," Applied Energy, Elsevier, vol. 329(C).
    5. Andreas Reinhardt & Lucas Pereira, 2021. "Special Issue: “Energy Data Analytics for Smart Meter Data”," Energies, MDPI, vol. 14(17), pages 1-3, August.
    6. Todic, Tamara & Stankovic, Vladimir & Stankovic, Lina, 2023. "An active learning framework for the low-frequency Non-Intrusive Load Monitoring problem," Applied Energy, Elsevier, vol. 341(C).
    7. 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).
    8. 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.
    9. İsmail Hakkı Çavdar & Vahit Feryad, 2021. "Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid," Energies, MDPI, vol. 14(15), pages 1-21, July.
    10. Hao Ma & Juncheng Jia & Xinhao Yang & Weipeng Zhu & Hong Zhang, 2021. "MC-NILM: A Multi-Chain Disaggregation Method for NILM," Energies, MDPI, vol. 14(14), pages 1-14, July.
    11. Apostolos Vavouris & Benjamin Garside & Lina Stankovic & Vladimir Stankovic, 2022. "Low-Frequency Non-Intrusive Load Monitoring of Electric Vehicles in Houses with Solar Generation: Generalisability and Transferability," Energies, MDPI, vol. 15(6), pages 1-27, March.

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