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Neural Load Disaggregation: Meta-Analysis, Federated Learning and Beyond

Author

Listed:
  • Hafsa Bousbiat

    (Department of Informatics, University of Klagenfut, 9020 Klagentfurt, Austria
    These authors contributed equally to this work.)

  • Yassine Himeur

    (College of Engineering and Information Technology, University of Dubai, Dubai P.O. Box 14143, United Arab Emirates
    These authors contributed equally to this work.)

  • Iraklis Varlamis

    (Department of Informatics and Telematics, Harokopion University of Athens, Tavros, 177 78 Athens, Greece
    These authors contributed equally to this work.)

  • Faycal Bensaali

    (Department of Electrical Engineering, Qatar University, Doha P.O. Box 2713, Qatar)

  • Abbes Amira

    (College of Computing and Informatics, Sharjah University, Sharjah P.O. Box 27272, United Arab Emirates)

Abstract

Non-intrusive load monitoring (NILM) techniques are central techniques to achieve the energy sustainability goals through the identification of operating appliances in the residential and industrial sectors, potentially leading to increased rates of energy savings. NILM received significant attention in the last decade, reflected by the number of contributions and systematic reviews published yearly. In this regard, the current paper provides a meta-analysis summarising existing NILM reviews to identify widely acknowledged findings concerning NILM scholarship in general and neural NILM algorithms in particular. In addition, this paper emphasizes federated neural NILM, receiving increasing attention due to its ability to preserve end-users’ privacy. Typically, by combining several locally trained models, federated learning has excellent potential to train NILM models locally without communicating sensitive data with cloud servers. Thus, the second part of the current paper provides a summary of recent federated NILM frameworks with a focus on the main contributions of each framework and the achieved performance. Furthermore, we identify the non-availability of proper toolkits enabling easy experimentation with federated neural NILM as a primary barrier in the field. Thus, we extend existing toolkits with a federated component, made publicly available and conduct experiments on the REFIT energy dataset considering four different scenarios.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:991-:d:1037197
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    References listed on IDEAS

    as
    1. 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.
    2. Schäuble, Dominik & Marian, Adela & Cremonese, Lorenzo, 2020. "Conditions for a cost-effective application of smart thermostat systems in residential buildings," Applied Energy, Elsevier, vol. 262(C).
    Full references (including those not matched with items on IDEAS)

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