Cited by 1,836Solar Radiation and PhotovoltaicsEnergy Load and Power ForecastingSolar Thermal and Photovoltaic Systems

Machine learning methods for solar radiation forecasting: A review

Open access
Voyant, Cyril, Notton, Gilles, Kalogirou, Soteris, Nivet, Marie-Laure, Paoli, Christophe, Motte, Fabrice, Fouilloy, Alexis·2017·Renewable Energy

Integrity signals

Registry

Crossref · 10.1016/j.renene.2016.12.095

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93 of 93 references verified0 citation errors · 0 not checkable

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  • Since weather fundamentally drives both variable generation and electricity demand, ML algorithms forecasting these quantities should draw from innovations in climate modeling and weather forecasting and in hybrid physics-plus-ML modeling techniques.

    “since weather fundamentally drives both variable generation and electricity demand, ML algorithms forecasting these quantities should draw from innovations in climate modeling and weather forecasting (Section 8) and in hybrid physics-plus-ML modeling techniques [161,818,822].”

    From Tackling climate change with machine learning (2023) · cites this paper as [818]

  • Many ML methods have been used to forecast electricity supply and demand.

    “To date, many ML methods have been used to forecast electricity supply and demand.”

    From Tackling climate change with machine learning (2023) · cites this paper as [818]

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Authors
Voyant, C., Notton, G., Kalogirou, S., Nivet, M.-L., Paoli, C., Motte, F., & Fouilloy, A.
Affiliations
  • Centre National de la Recherche Scientifique (FR)
  • Cyprus University of Technology (CY)
Corresponding author
Voyant, Cyril
Venue
Renewable Energy
Publisher
Elsevier BV
Published
2017-05
Volume
105
Pages
569-582
ISSN
0960-1481 (ISSN-L)
OpenAlex
W2569349941
Cited by
1,836
References
93
Citations by year
2026: 236 · 2025: 166 · 2024: 219 · 2023: 236 · 2022: 247 · 2021: 247 · 2020: 205 · 2019: 145 · 2018: 101 · 2017: 34
FWCI
79.11
Type
journal article
Language
en
Open access
Locations
  • Renewable Energy (publishedVersion)
  • HAL (submittedVersion, other-oa)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Ktisis at Cyprus University of Technology (Cyprus University of Technology) (submittedVersion)
  • Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by)
Funders
European Union (646529)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Solar Radiation and Photovoltaics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
  • Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Solar Thermal and Photovoltaic Systems
Keywords
solar irradiance forecasting, global radiation, machine learning, artificial neural networks, support vector regression, random forest, gradient boosting, regression trees, hybrid forecasting model, ensemble forecasting, solar power output prediction, forecast performance comparison

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