Machine learning methods for solar radiation forecasting: A review
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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
- Licence
- 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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