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Cyril Voyant
Researcher at University of Corsica Pascal Paoli
Publications - 113
Citations - 4306
Cyril Voyant is an academic researcher from University of Corsica Pascal Paoli. The author has contributed to research in topics: Multilayer perceptron & Time series. The author has an hindex of 25, co-authored 104 publications receiving 3045 citations. Previous affiliations of Cyril Voyant include Centre national de la recherche scientifique & Society of Petroleum Engineers.
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Journal ArticleDOI
Machine learning methods for solar radiation forecasting: A review
Cyril Voyant,Gilles Notton,Soteris A. Kalogirou,Marie Laure Nivet,Christophe Paoli,Christophe Paoli,Fabrice Motte,Alexis Fouilloy +7 more
TL;DR: An overview of forecasting methods of solar irradiation using machine learning approaches is given and it will be shown that other methods begin to be used in this context of prediction.
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Forecasting of preprocessed daily solar radiation time series using neural networks
TL;DR: The optimized MLP presents predictions similar to or even better than conventional and reference methods such as ARIMA techniques, Bayesian inference, Markov chains and k-Nearest-Neighbors, as well as six prediction methods allow to predict the best daily DC PV power production at horizon d.
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Intermittent and stochastic character of renewable energy sources: Consequences, cost of intermittence and benefit of forecasting
Gilles Notton,Marie Laure Nivet,Cyril Voyant,Christophe Paoli,Christophe Darras,Fabrice Motte,Alexis Fouilloy +6 more
TL;DR: In this paper, the authors synthesize the reasons to predict solar or wind fluctuations, it shows that variability and stochastic variation of renewable sources have a cost, sometimes high, and provides useful information on the intermittence cost and on the decreasing of this cost due to an efficient forecasting of the source fluctuation.
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Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components
TL;DR: In this article, three methods, smart persistence, artificial neural network and random forest, are compared to forecast the three components of solar irradiation (global horizontal, beam normal and diffuse horizontal) measured on the site of Odeillo, France, characterized by a high meteorological variability.
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Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation
TL;DR: An original technique to predict global radiation using a hybrid ARMA/ANN model and data issued from a numerical weather prediction model (NWP) and the multi-layer perceptron (MLP) is proposed.