Suchergebnisse

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. ; La loi de transition énergétique votée par l'Etat français a des implications précises sur les énergies renouvelables, en particulier sur leur mécanisme de rémunération. Jusqu'en 2015, un contrat d'obligation d'achat permettait de vendre ...

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. ; La loi de transition énergétique votée par l'Etat français a des implications précises sur les énergies renouvelables, en particulier sur leur mécanisme de rémunération. Jusqu'en 2015, un contrat d'obligation d'achat permettait de vendre ...

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. ; La loi de transition énergétique votée par l'Etat français a des implications précises sur les énergies renouvelables, en particulier sur leur mécanisme de rémunération. Jusqu'en 2015, un contrat d'obligation d'achat permettait de vendre ...

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. ; La loi de transition énergétique votée par l'Etat français a des implications précises sur les énergies renouvelables, en particulier sur leur mécanisme de rémunération. Jusqu'en 2015, un contrat d'obligation d'achat permettait de vendre ...

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. ; La loi de transition énergétique votée par l'Etat français a des implications précises sur les énergies renouvelables, en particulier sur leur mécanisme de rémunération. Jusqu'en 2015, un contrat d'obligation d'achat permettait de vendre ...

The purpose of the Phd is to analyze questions raised by short-termism in takevoer bids. As a matter of fact, some shareholders only have short-terme strategies (such as Hedge funds,.) and takeover bids provide some fantastic arbitration opportunities. First, one should clearly identify these actors. Products which might be used in such situation will also have to be studied. Second, one should analyze which defence could provide the target. Third, a study of the opportunity or repealing or amending french legislation on these issues will be done. ; Cette thèse vise à analyser les aspects juridiques du rôle joué par les acteurs ayant une stratégie actionariale de court-terme (hedge funds,.) dans le cadre des offres publiques d'acquisition. Outre l'identification de ces acteurs et la description des méthodes employées, il s'agit aussi de s'interroger sur les moyens à disposition de la société cible pour se défendre et de se demander si des évolutions législatives ne seraient pas nécessaires.

The purpose of the Phd is to analyze questions raised by short-termism in takevoer bids. As a matter of fact, some shareholders only have short-terme strategies (such as Hedge funds,.) and takeover bids provide some fantastic arbitration opportunities. First, one should clearly identify these actors. Products which might be used in such situation will also have to be studied. Second, one should analyze which defence could provide the target. Third, a study of the opportunity or repealing or amending french legislation on these issues will be done. ; Cette thèse vise à analyser les aspects juridiques du rôle joué par les acteurs ayant une stratégie actionariale de court-terme (hedge funds,.) dans le cadre des offres publiques d'acquisition. Outre l'identification de ces acteurs et la description des méthodes employées, il s'agit aussi de s'interroger sur les moyens à disposition de la société cible pour se défendre et de se demander si des évolutions législatives ne seraient pas nécessaires.

The purpose of the Phd is to analyze questions raised by short-termism in takevoer bids. As a matter of fact, some shareholders only have short-terme strategies (such as Hedge funds,.) and takeover bids provide some fantastic arbitration opportunities. First, one should clearly identify these actors. Products which might be used in such situation will also have to be studied. Second, one should analyze which defence could provide the target. Third, a study of the opportunity or repealing or amending french legislation on these issues will be done. ; Cette thèse vise à analyser les aspects juridiques du rôle joué par les acteurs ayant une stratégie actionariale de court-terme (hedge funds,.) dans le cadre des offres publiques d'acquisition. Outre l'identification de ces acteurs et la description des méthodes employées, il s'agit aussi de s'interroger sur les moyens à disposition de la société cible pour se défendre et de se demander si des évolutions législatives ne seraient pas nécessaires.

Short-term financing is forgotten by theory of corporate finance. However, French medium-sized firms use a lot this source of funding. The objective of this thesis is to analyze the determinants of short-term financing for these firms. The first part aims to establish a literature review of theories to explain the use of short-term financing. The second part empirically checks these theories on two samples, specifically 201 family businesses and 1,453 managerial firms. On the one hand, it is a question of characterizing the unlisted medium-sized enterprises and on the other hand, highlighting the determinants of the use of short-term financing. The primary results indicate that short-term financing is a management tool for the medium-sized enterprise. It can also have two functions, one compensatory and / or one passive cash. Furthermore, it brings out that managerial and family businesses exploit short-term financing differently. ; Le financement à court terme est le parent pauvre de la théorie financière. Pourtant, les moyennes entreprises françaises utilisent fortement ce financement. Ce travail a pour objectif d'analyser les déterminants du financement à court terme pour ces entreprises. La première partie vise à établir une revue de littérature des théories permettant d'expliquer l'utilisation du financement à court terme. La deuxième partie vient tester empiriquement ces théories sur deux échantillons, à savoir 201 entreprises familiales et 1 453 entreprises managériales. Il s'agit, d'une part, de caractériser les moyennes entreprises non cotées et d'autres part, de mettre en évidence les déterminants à l'utilisation de financement à court terme. Les principaux résultats indiquent que le financement à court terme est un outil de gestion au service de la moyenne entreprise. Il peut aussi assumer deux rôles, un compensatoire et/ou un de trésorerie passive. Par ailleurs, on met en évidence que les entreprises managériales et familiales exploitent différemment le financement à court terme.

The aviation industry has for many years pursued the objective of an optimum level of safetyin the air transport sector. With regard to military aviation, more precisely tactical, thispriority is coupled with an increasingly high and polymorphic search for performance. Whatcharacterize this type of aviation is the relationship between the performance pursued and theaccepted risks. It depends essentially on the context and the stakes of the missions to becarried out.The human factor approach is a major leverage for achieving this challenge. Thus, within theconstrained domain of aeronautics, the design and development of tools to assist crewcognition remains a prospect for the future, even if pilot training also becomes a majorchallenge for the coming years. In this context, the management of cognitive resources, and inparticular the specific management strategies put in place by the pilots, are central to thedecision-making process under constraints.In a research and engineering approach in cognition, we undertook a study involving pilotsand allowing the understanding of these mechanisms as well as the production ofrecommendations for the design of tools to help manage their cognitive resources. On thebasis of the analysis of feedback, and results of a preliminary experimental approach, we havebuilt a protocol to highlight the strategies implemented by the pilots in the context of anactivity during the descent and the final approach on the Clermont-Ferrand airport with acritical breakdown. The experimental results reconciled with our understanding hypotheseson the management of cognitive resources and management strategies, complete our analysisand recommendations for a tool to help manage the resources of the pilots. ; L'industrie aéronautique poursuit depuis de nombreuses années l'objectif d'un niveau optimal de sécurité dans le cadre du transport aérien. En ce qui concerne l'aviation militaire, plus exactement tactique, cette priorité se double d'une recherche de performance de plus en plus élevée et ...