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A comprehensive review of machine learning and deep learning models for non-intrusive load monitoring: performance, analyses, practical insights, and emerging trends

Crossref DOI link: https://doi.org/10.1007/s10489-025-06921-4

Published Online: 2025-10-13

Published Print: 2025-10

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Saleem, Muhammad Hammad https://orcid.org/0000-0002-3625-3021
Taha, Muhammad https://orcid.org/0009-0002-2290-8872
Rehmani, Muhammad Asif Ali https://orcid.org/0000-0002-3525-7901
Tito, Shafiqur Rahman https://orcid.org/0000-0002-2173-579X
Soltic, Snjezana https://orcid.org/0000-0001-8660-7062
Nieuwoudt, Pieter https://orcid.org/0000-0002-3185-1516
Pandey, Neel

Ahmed, Mollah Daud
Funding

Funding for this research was provided by:

Manukau Institute of Technology Strategic Research Fund 2022 (SRF-2022-RP0049)
License Information

Text and Data Mining valid from 2025-10-01

Version of Record valid from 2025-10-13
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Article History

Received: 3 April 2024

Accepted: 6 September 2025

First Online: 13 October 2025

Declarations

:

: The authors declare no conflict of interest.

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