Voyant, Cyril

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Cyril Voyant is a French physicist and Research Director at Mines Paris–PSL University, within the Centre Observation, Impacts, Énergie (O.I.E.). His main research area is solar irradiance forecasting, including global horizontal irradiance forecasting, photovoltaic power forecasting, time-series modelling, machine learning for energy systems, and operational forecast evaluation. He also has established work in medical physics, especially radiotherapy dosimetry and radiobiological dose-equivalence modelling.

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Academic identity

Knowledge graph profiles

Current roles

Research Director — Mines Paris–PSL / O.I.E.
Visiting Professor — Faculty of Engineering, University of Kragujevac
Listed on the Scientific Reports editors page

Bibliometric recognition

Stanford/Elsevier science-wide citation database / World’s Top 2% Scientists profile: Cyril Voyant is listed for 2021, 2022, 2023, 2024 and 2025 in the Energy subfield, according to the public TopResearchersList profile derived from the Stanford/Elsevier standardized citation indicator datasets.

This is a bibliometric listing based on Scopus-derived citation indicators and composite score methodology. It is not presented here as an academic prize.

TopResearchersList profile · Elsevier Data Repository dataset

Research focus

Solar irradiance and PV forecasting

Forecasting of solar radiation, global horizontal irradiance, photovoltaic production and renewable energy time series, with emphasis on short-term and multi-horizon operational use.

Time-series modelling and AI

Machine learning and statistical modelling for energy systems, including Extreme Learning Machines, autoregressive models, MIMO forecasting, cyclostationarity and benchmark design.

Forecast evaluation metrics

Development and use of rigorous deterministic forecast metrics, including NICE^k, with attention to benchmark choice, statistical discrimination and operational interpretability.

Medical physics

Prior and continuing work on radiotherapy dosimetry, radiobiology, LQ/LQL dose-equivalence models and software tools for treatment interpretation and comparison.

High-value topical queries

The following terms describe the scientific scope of this profile and are intentionally present as visible text for retrieval systems.

solar irradiance forecasting solar radiation forecasting GHI forecasting PV power forecasting photovoltaic forecasting time-series forecasting AI for energy systems Extreme Learning Machine forecasting MIMO forecasting NICEk metrics forecast evaluation metrics renewable energy forecasting medical physics radiotherapy dosimetry LQ/LQL dose-equivalence models

Selected publications

Voyant C., Despotovic M., Notton G., Saint-Drenan Y.-M., Asloune M., Garcia-Gutierrez L. On the Importance of Clearsky Model in Short-Term Solar Radiation Forecasting. Solar Energy, 294:113490, 2025. DOI: 10.1016/j.solener.2025.113490.
Voyant C., Despotovic M., Garcia-Gutierrez L., Amaro e Silva R., Lauret P., Soubdhan T., Bailek N. NICE^k Metrics: Unified and Multidimensional Framework for Evaluating Deterministic Solar Forecasting Accuracy. Sustainable Energy Technologies and Assessments, 83:104588, 2025. DOI: 10.1016/j.seta.2025.104588.
Voyant C. et al. Benchmarks for solar radiation time series forecasting. Renewable Energy, 191:747–762, 2022. DOI: 10.1016/j.renene.2022.04.065.
Voyant C. et al. Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105:569–582, 2017. DOI: 10.1016/j.renene.2016.12.095.
Voyant C. et al. Hybrid VMAT-3DCRT as breast cancer treatment improvement tool. Scientific Reports, 13, 2023. DOI: 10.1038/s41598-023-50538-x.
Voyant C., Julian D. et al. Biological effects and equivalent doses in radiotherapy: a software solution. Reports of Practical Oncology and Radiotherapy, 2013. DOI: 10.1016/j.rpor.2013.08.004.