Transfer learning for wind speed forecasting: A scalable approach for data-scarce environments

Authors

Gilles Notton (ORCID: https://orcid.org/0000-0002-6267-9632)
Milan Despotović (ORCID: https://orcid.org/0000-0003-3144-5945)

Institutions

Université de Corse Pascal Paoli (FR)
University of Kragujevac (RS)
Fondation Sophia Antipolis (FR)

Publication Details

Journal: International Journal of Green Energy
Published: 2025-10-13
DOI: https://doi.org/10.1080/15435075.2025.2574524
Primary Topic: Energy Load and Power Forecasting
Type: article
Field-Weighted Citation Impact: 0.00

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article

Transfer learning for wind speed forecasting: A scalable approach for data-scarce environments

Gilles Notton, Milan Despotović

Oct 13, 2025

International Journal of Green Energy

Energy Load and Power Forecasting

article

Transfer learning for wind speed forecasting: A scalable approach for data-scarce environments

Gilles Notton, Milan Despotović

October 13, 2025article en

Abstract

No abstract available for this paper.

International Journal of Green EnergyVol. 23(3)

Université de Corse Pascal Paoli (FR), University of Kragujevac (RS), Fondation Sophia Antipolis (FR)

Openalex Percentile: Top 72%

Energy Load and Power Forecasting

DOI Open Access PDF OpenAlex

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Transfer learning for wind speed forecasting: A scalable approach for data-scarce environments — Gilles Notton, Milan Despotović · International Journal of Green Energy (2025) | TGRS Research Map | TGRS