Near real-time machine learning framework in distribution networks with low-carbon technologies using smart meter data

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dc.check.date2027-02-01
dc.check.infoAccess to this article is restricted until 24 months after publication by request of the publisheren
dc.contributor.authorDokur, Emrahen
dc.contributor.authorErdogan, Nuhen
dc.contributor.authorSengor, Ibrahimen
dc.contributor.authorYüzgeç, Uğuren
dc.contributor.authorHayes, Barry P.en
dc.date.accessioned2025-01-27T16:18:47Z
dc.date.available2025-01-27T16:18:47Z
dc.date.issued2025en
dc.descriptionArticle peer-revieweden
dc.description.abstractThe widespread adoption of low carbon technologies (LCTs) such as photovoltaics, electric vehicles, heat pumps, and energy storage units introduces challenges to distribution network congestion and power quality, particularly raising concerns about voltage stability. Enhanced voltage visibility in low-voltage (LV) networks is increasingly vital for an active grid management, making efficient voltage forecasting tools essential. This study introduces a novel data-driven approach for forecasting node voltages in LCT-enriched LV distribution networks. Using time series of power measurements from smart meter data, the study integrates an Extreme Learning Machine (ELM) with the Single Candidate Optimizer (SCO) to enhance both computational efficiency and forecasting accuracy. The model is validated using a realistic LCT-enriched LV network dataset and benchmarked against several established machine learning models. Results demonstrate that the SCO algorithm effectively optimizes ELM parameters, achieving up to a 17-fold reduction in computation time compared to the fastest metaheuristic methods implemented. The proposed model demonstrated superior accuracy, with an average voltage deviation of 0.56%. While the computation time per node achieved is not yet suitable for real-time applications, the study proves that SCO significantly enhances ELM performance.en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationDokur, E., Erdogan, N., Sengor, I., Yüzgeç, U. and Hayes, B. P. (2025) 'Near real-time machine learning framework in distribution networks with low-carbon technologies using smart meter data', Applied Energy.en
dc.identifier.doien
dc.identifier.eissn1872-9118
dc.identifier.endpage19en
dc.identifier.issn0306-2619en
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/16902
dc.language.isoenen
dc.publisherElsevier Ltden
dc.rights© 2025, Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.en
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectLow carbon technologiesen
dc.subjectMachine learningen
dc.subjectMeta-heuristicen
dc.subjectSingle candidate optimizeren
dc.subjectSmart meteren
dc.subjectVoltage forecastingen
dc.titleNear real-time machine learning framework in distribution networks with low-carbon technologies using smart meter dataen
dc.typeArticle (peer-reviewed)en
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