Cyril Voyant: research news

Visiting Professor at the University of Kragujevac, Serbia

2026-10-23T00:00:00Z

I am honoured to have been appointed Visiting Professor at the Faculty of Engineering, University of Kragujevac, together with my colleague and friend Prof. Gilles Notton.

Tunnel Load Simulator: an open-source generator for synthetic road-tunnel electricity demand

2026-05-08T00:00:00Z

The Tunnel Load Simulator is an open-source Streamlit application developed within the SmartTunnel project to generate synthetic electrical load profiles for road tunnels. It combines simplified representations of lighting, ventilation, auxiliary loads, traffic patterns, seasonal effects and stochastic events such as pollution episodes or accidents.

When Sunlight Stops Being Direct

2026-04-28T00:00:00Z

Diffuse solar radiation remains one of the most complex components of the solar resource, particularly in regions affected by aerosols, humidity, and strong climatic contrasts.

Revisiting Persistence: Symmetry-Constrained Forecasting of Periodically Correlated Energy Processes

2026-04-24T00:00:00Z

This work revisits persistence forecasting for periodically varying statistics, including the daily cycle of solar irradiance. BLEND combines the latest observation with an observation aligned to the target phase of the cycle.

From Pixels to Tolerances: Rethinking Forecast Validation with the γ-Index

2026-03-28T00:00:00Z

Classical metrics like RMSE evaluate forecasts as if every pixel must match perfectly in space and time. Machines optimize for that. Humans do not think that way.

Excellence scientifique à l’ère de l’IA : du SEO au GEO

2026-10-23T00:00:00Z

La production scientifique mondiale connaît une croissance sans précédent, amplifiée par l’essor des modèles d’intelligence artificielle générative. Dans ce contexte, la question n’est plus seulement de produire du savoir, mais d’en garantir la visibilité, la robustesse et la réutilisabilité.

Coherent Multi-Energy Forecasting for Real-Time Grid Management: A Parsimonious MIMO-ELM Approach

2026-02-26T00:00:00Z

Fast, transparent, and operational short-term forecasting of demand and generation across multiple energy sources.

Invisible Research Has No Impact: Engineering Scientific Influence in the AI Era

2026-10-23T00:00:00Z

The research landscape has shifted from scarcity to saturation: producing knowledge is no longer the bottleneck: being discovered, trusted, and reused is.

Appointed Associate Editor for Scientific Reports (Nature Portfolio)

2026-10-23T00:00:00Z

This role aligns with my trajectory as Research Director at Mines Paris–PSL O.I.E. (since 2024), with >130 peer-reviewed publications (h-index ~35, >7000 citations, Stanford Top 2%). Scientific Reports covers all scientific domains.

Quality Control Framework for Tilted Solar Irradiance

2025-12-20T00:00:00Z

This work is part of the doctoral research of Mohammed Asloun (University of Corsica & Mines-PSL).

Towards Personalized Radio-Iodine Therapy: Modeling, Dynamics, and Clinical Perspectives

2026-10-23T00:00:00Z

A doctoral contribution by Marie Fusella Giuntini, in collaboration with Aix-Marseille, Corsica Universities, Mines-PSL, and la Timone Hospital : Marie Fusella Giuntini, Cyril Voyant, David Taieb and Dominique Barbolosi.

Grateful to be included in the 2025 Stanford-Elsevier Top 2% Scientists ranking.

2026-10-23T00:00:00Z

Proud to contribute to research in solar forecasting, PV systems, AI for energy, and medical physics at Mines Paris – PSL.

Transfer Learning for Wind Speed Forecasting: A Scalable Approach for Data-Scarce Environments

2025-10-13T00:00:00Z

This paper demonstrates that transfer learning can accurately forecast wind speed in data-scarce regions by reusing models trained on data-rich sites. Across 4,830 experiments, TL achieved accuracy comparable to direct learning (ΔnRMSE < 0.01). The approach offers a scalable, low-data solution for expanding wind forecasting to under-instrumented areas.

From Trends to Insights: A Text Mining Analysis of Solar Energy Forecasting (2017–2023)

2025-10-01T00:00:00Z

The study applies text mining to 500 papers (2017–2023) to map global trends in solar energy forecasting. It reveals a strong rise of AI and deep learning, led by China, the USA, and India, and shows that deterministic metrics still dominate probabilistic ones. The work delivers a reproducible overview and standardized terminology, advancing transparency and collaboration in solar forecasting research .

A Simple Method for Quickly Estimating Solar Irradiance Forecast Errors

2025-08-22T00:00:00Z

Researchers have developed linear models that predict solar irradiance forecast errors using only solar variability at a specific location. This variability is measured by the standard deviation of hourly changes in the clear sky index, essentially capturing how much solar conditions fluctuate hour to hour.

New Release: LQL-Equiv: Open‑Source Software for Biologically Equivalent Dose Calculation in Radiotherapy

2025-08-04T00:00:00Z

LQL‑Equiv is a free & open‑source software (GNU‑based) written in MATLAB and distributed as a standalone executable, developed by Cyril Voyant & Daniel Julian. It computes voxel‑wise Equivalent Dose in 2 Gy fractions (EQD₂) and Biologically Effective Dose (BED) using a Linear‑Quadratic‑Linear (LQL) model that explicitly accounts for fraction size, overall treatment time, and cellular repopulation effects, outperforming standard LQ-based calculators.

Improving Clinical Decision-Making in Radiotherapy: A Comparative Analysis of Linear-Quadratic LQ and Linear-Quadratic-Linear LQL Dose Models

2025-06-24T00:00:00Z

This collaborative paper compares the linear-quadratic (LQ) and linear-quadratic-linear (LQL) dose models in radiotherapy. Read the published article for the methods, results and clinical limitations.

Stochastic Coefficient of Variation: Assessing the Variability and Forecastability of Solar Irradiance

2025-07-17T00:00:00Z

This work presents a robust framework for quantifying solar irradiance variability and forecastability through the Stochastic Coefficient of Variation (sCV) and the Forecastability (F). Traditional metrics, such as the standard deviation, fail to isolate stochastic fluctuations from deterministic trends in solar irradiance. By considering clear-sky irradiance as a dynamic upper bound of measurement, sCV provides a normalized, dimensionless measure of variability that theoretically ranges from 0 to 1. F extends sCV by integrating temporal dependencies via maximum autocorrelation, thus linking sCV with F. The proposed methodology is validated using synthetic cyclostationary time series and experimental data from 68 meteorological stations (in Spain). Our comparative analyses demonstrate that sCV and F proficiently encapsulate multi-scale fluctuations, while addressing significant limitations inherent in traditional metrics. This comprehensive framework enables a refined quantification of solar forecast uncertainty, supporting improved decision-making in flexibility procurement and operational strategies. By assessing variability and forecastability across multiple time scales, it enhances real-time monitoring capabilities and informs adaptive energy management approaches, such as dynamic outage management and risk-adjusted capacity allocation

NICE^k Metrics: Unified and Multidimensional Framework for Evaluating Deterministic Solar Forecasting Accuracy

2025-09-25T00:00:00Z

Accurate solar energy output prediction is fundamental to integrating renewable energy sources into electrical grids, maintaining system stability, and enabling effective energy management. However, conventional error metrics—such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Skill Scores (SS)—fail to capture the multidimensional complexity of solar irradiance forecasting. These metrics lack sensitivity to forecastability, rely on arbitrary baselines (e.g., clear-sky models), and are poorly adapted to operational needs.

On the importance of clearsky model in short-term solar radiation forecasting

2025-04-11T00:00:00Z

Clearsky models are widely used in solar energy for many applications such as quality control, resource assessment, satellite-base irradiance estimation and forecasting. However, their use in forecasting and nowcasting is associated with a number of challenges. Synchronization errors, reliance on the Clearsky index (ratio of the global horizontal irradiance to its cloud-free counterpart) and high sensitivity of the clearsky model to errors in aerosol optical depth at low solar elevation limit their added value in real-time applications. This paper explores the feasibility of short-term forecasting without relying on a clearsky model. We propose a Clearsky-Free forecasting approach using Extreme Learning Machine (ELM) models. ELM learns daily periodicity and local variability directly from raw Global Horizontal Irradiance (GHI) data. It eliminates the need for Clearsky normalization, simplifying the forecasting process and improving scalability. Our approach is a non-linear adaptative statistical method that implicitly learns the irradiance in cloud-free conditions removing the need for an clear-sky model and the related operational issues. Deterministic and probabilistic results are compared to traditional benchmarks, including ARMA with McClear-generated Clearsky data and quantile regression for probabilistic forecasts. ELM matches or outperforms these methods, providing accurate predictions and robust uncertainty quantification. This approach offers a simple, efficient solution for real-time solar forecasting. By overcoming the stationarization process limitations based on usual multiplicative scheme Clearsky models, it provides a flexible and reliable framework for modern energy systems.

Comparative Study of Feature Selection Techniques for Machine Learning-Based Solar Irradiation Forecasting to Facilitate the Sustainable Development of Photovoltaics: Application to Algerian Climatic Conditions

2025-07-12T00:00:00Z

Forecasting future solar power plant production is essential to continue the development of photovoltaic energy and increase its share in the energy mix for a more sustainable future. Accurate solar radiation forecasting greatly improves the balance maintenance between energy supply and demand and grid management performance. This study assesses the influence of input selection on short-term global horizontal irradiance (GHI) forecasting across two contrasting Algerian climates: arid Ghardaïa and coastal Algiers. Eight feature selection methods (Pearson, Spearman, Mutual Information (MI), LASSO, SHAP (GB and RF), and RFE (GB and RF)) are evaluated using a Gradient Boosting model over horizons from one to six hours ahead. Input relevance depends on both the location and forecast horizon. At t+1, MI achieves the best results in Ghardaïa (nMAE = 6.44%), while LASSO performs best in Algiers (nMAE = 10.82%). At t+6, SHAP- and RFE-based methods yield the lowest errors in Ghardaïa (nMAE = 17.17%), and RFE-GB leads in Algiers (nMAE = 28.13%). Although performance gaps between methods remain moderate, relative improvements reach up to 30.28% in Ghardaïa and 12.86% in Algiers. These findings confirm that feature selection significantly enhances accuracy (especially at extended horizons) and suggest that simpler methods such as MI or LASSO can remain effective, depending on the climate context and forecast horizon.

Academic Visibility: Why Publishing Isn't Enough Anymore

2026-10-23T00:00:00Z

I've experienced this troubling phenomenon firsthand: an article I had little hope for exploded with hundreds of citations, while another piece I considered my best work remained in obscurity. After twenty years of research, one thing is clear: in a rapidly evolving academic ecosystem, publishing alone is no longer sufficient.