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Prévision court terme de la production éolienne par Machine learning

Abstract : The energy transition law passed by the French government has specific implications for renewable energies, in particular for their remuneration mechanism. Until 2015, a purchase obligation contract made it possible to sell electricity from wind power at a fixed rate. From 2015 onwards, some wind farms began to be exempted from the purchase obligation. This is because wind energy is starting to be sold directly on the market by the producers because of the breach of the purchase obligation contracts. Distribution system operators and transmission system operators require or even oblige producers to provide at least a production forecast one day in advance in order to rebalance the market. Over- or underestimation could be subject to penalties. There is, therefore, a huge need for accurate forecasts. It is in this context that this thesis was launched with the aim of proposing a model for predicting wind farms production by machine learning. We have production data and real wind measurements as well as data from meteorological models. We first compared the performances of the GFS and ECMWF models and studied the relationships between these two models through canonical correlation analysis. We then applied machine learning models to validate a first random forest prediction model. We then modeled the spatio-temporal wind dynamics and integrated it into the prediction model, which improved the prediction error by 3%. We also studied the selection of grid points by a variable group importance measure using random forests. Random forest prediction intervals associated with point forecasts of wind farm production are also studied. The forecasting model resulting from this work was developed to enable the ENGIE Group to have its own daily forecasts for all its wind farms.
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Submitted on : Monday, August 10, 2020 - 12:10:08 PM
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Mamadou Dione. Prévision court terme de la production éolienne par Machine learning. Statistiques [math.ST]. Institut Polytechnique de Paris, 2020. Français. ⟨NNT : 2020IPPAG004⟩. ⟨tel-02913708⟩

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