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France has one of the largest wind potentials in Europe, yet far from being fully exploited. The wind resource and energy yield assessment is a key step before building a wind farm, aiming at predicting the future electricity production. Any over-estimation in the assessment process puts in jeopardy the project's profitability. This has been the case in the recent years, when wind farm managers have noticed that they produced less than expected.The under-production problem leads to questioning both the validity of the assessment methods and the inter-annual wind variability. This thesis tackles these two issues. In a first part are investigated the errors linked to the assessment methods, especially in two steps: the vertical extrapolation of wind measurements and the statistical modelling of wind-speed data by a Weibull distribution. The second part investigates the inter-annual to decadal variability of wind speeds, in order to understand how this variability may have contributed to the under-production and so that it is better taken into account in the future., La France possède un gisement éolien parmi les plus importants d'Europe, et qui est encore loin d'être exploité à son maximum. L'évaluation du potentiel éolien d'un site est une étape clé en amont de la construction d'un parc éolien pour estimer la production électrique future et toute sur-estimation dans cette évaluation met en péril la rentabilité du parc. Or, cela a été le cas ces dernières années, les exploitants éoliens ont constaté qu'ils avaient produit moins que prévu.Cette question de la sous-production soulève deux problématiques sur lesquelles se penche ce travail de thèse: d'une part les incertitudes associées aux méthodes utilisées pour évaluer le potentiel éolien et d'autre part la variabilité interannuelle du vent. La première partie porte sur les erreurs des méthodes d'évaluation du potentiel, en particulier lors de l'extrapolation verticale des mesures de vent et lors de la modélisation statistique du vent par la distribution de Weibull. La deuxième partie porte sur la variabilité du vent aux échelles annuelles à décennales, afin de comprendre comment cette variabilité a pu contribuer à une sous-production, et de mieux l'appréhender à l'avenir.

As the wind industry is developing, it is asking for more reliable short-term wind forecasts to better manage the wind farms’ operations and electricity production. Developing new wind farms also requires correct assessments of the long-term wind potentials to decide whether to install a wind farm at a specific location. This thesis is studying a new generation of numerical weather forecasting models, named mesoscale models, to see how they could answer those needs. It is held at the company Maïa Eolis which operates several wind farms in France. A mesoscale model, the Weather Research and Forecasting model (WRF), was chosen and used to generate high resolution forecasts based on lower resolution forecasts from NCEP’s Global Forecasting System. The stages for implementation of daily forecasts for the company’s wind farms were: explore and configure the model, automate the runs, develop post-processing tools and forecasts visualization software which was intended to be used by the management team. WRF was also used to downscale wind archives of NCEP’s Final Analysis and determine the possibility to use these in assessing wind potentials. Finally the precision of the model in both cases and for each wind farm was assessed by comparing attained data from the model with real power production.