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

Hafsa Bousbiat, Yassine Himeur, Iraklis Varlamis, Faycal Bensaali () and Abbes Amira
Additional contact information
Hafsa Bousbiat: Department of Informatics, University of Klagenfut, 9020 Klagentfurt, Austria
Yassine Himeur: College of Engineering and Information Technology, University of Dubai, Dubai P.O. Box 14143, United Arab Emirates
Iraklis Varlamis: Department of Informatics and Telematics, Harokopion University of Athens, Tavros, 177 78 Athens, Greece
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

Energies, 2023, vol. 16, issue 2, 1-22

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.

Keywords: load disaggregation; neural NILM; federated learning; energy recommender systems (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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