Ensemble forecast of solar radiation using TIGGE weather forecasts and HelioClim database

International audienceMedium-range forecasts (one day to two weeks) of solar radiation are commonly assessed with a single forecast at a given location. In this paper, we forecast maps of surface solar irradiance, using ensembles of forecasts from the THORPEX Interactive Grand Global Ensemble (TIGGE) with a 6-h timestep. We compare our forecasts with observations derived from MeteoSat Second Generation (MSG) and provided by the HelioClim-3 database as gridded observations over metropolitan France. First, we study the ensembles from six meteorological centers. Second, we use sequential aggregation to linearly combine all the forecasts with weights that vary in space and time. Sequential aggregation updates the weights before any forecast, using available observations. We use the global numerical weather prediction from the European Center for Medium-range Weather Forecasts (ECMWF) as a reference forecast. The issue of spatial resolution is discussed because the low resolution forecasts from TIGGE are compared to high resolution irradiance estimated from MSG data. We found that the TIGGE ensembles are under-dispersed but rather different from one to another. Aggregation decreases the forecast error by 20%, and produces a more realistic spatial pattern of predicted irradiance

Prévision d’ensemble par agrégation séquentielle appliquée à la prévision de production d’énergie photovoltaïque

Our main objective is to improve the quality of photovoltaic power forecasts deriving from weather forecasts. Such forecasts are imperfect due to meteorological uncertainties and statistical modeling inaccuracies in the conversion of weather forecasts to power forecasts. First we gather several weather forecasts, secondly we generate multiple photovoltaic power forecasts, and finally we build linear combinations of the power forecasts. The minimization of the Continuous Ranked Probability Score (CRPS) allows to statistically calibrate the combination of these forecasts, and provides probabilistic forecasts under the form of a weighted empirical distribution function. We investigate the CRPS bias in this context and several properties of scoring rules which can be seen as a sum of quantile-weighted losses or a sum of threshold-weighted losses. The minimization procedure is achieved with online learning techniques. Such techniques come with theoretical guarantees of robustness on the predictive power of the combination of the forecasts. Essentially no assumptions are needed for the theoretical guarantees to hold. The proposed methods are applied to the forecast of solar radiation using satellite data, and the forecast of photovoltaic power based on high-resolution weather forecasts and standard ensembles of forecasts.Notre principal objectif est d'améliorer la qualité des prévisions de production d'énergie photovoltaïque (PV). Ces prévisions sont imparfaites à cause des incertitudes météorologiques et de l'imprécision des modèles statistiques convertissant les prévisions météorologiques en prévisions de production d'énergie. Grâce à une ou plusieurs prévisions météorologiques, nous générons de multiples prévisions de production PV et nous construisons une combinaison linéaire de ces prévisions de production. La minimisation du Continuous Ranked Probability Score (CRPS) permet de calibrer statistiquement la combinaison de ces prévisions, et délivre une prévision probabiliste sous la forme d'une fonction de répartition empirique pondérée.Dans ce contexte, nous proposons une étude du biais du CRPS et une étude des propriétés des scores propres pouvant se décomposer en somme de scores pondérés par seuil ou en somme de scores pondérés par quantile. Des techniques d'apprentissage séquentiel sont mises en oeuvre pour réaliser cette minimisation. Ces techniques fournissent des garanties théoriques de robustesse en termes de qualité de prévision, sous des hypothèses minimes. Ces méthodes sont appliquées à la prévision d'ensoleillement et à la prévision de production PV, fondée sur des prévisions météorologiques à haute résolution et sur des ensembles de prévisions classiques

Ensemble forecasting using sequential aggregation for photovoltaic power applications

Notre principal objectif est d'améliorer la qualité des prévisions de production d'énergie photovoltaïque (PV). Ces prévisions sont imparfaites à cause des incertitudes météorologiques et de l'imprécision des modèles statistiques convertissant les prévisions météorologiques en prévisions de production d'énergie. Grâce à une ou plusieurs prévisions météorologiques, nous générons de multiples prévisions de production PV et nous construisons une combinaison linéaire de ces prévisions de production. La minimisation du Continuous Ranked Probability Score (CRPS) permet de calibrer statistiquement la combinaison de ces prévisions, et délivre une prévision probabiliste sous la forme d'une fonction de répartition empirique pondérée.Dans ce contexte, nous proposons une étude du biais du CRPS et une étude des propriétés des scores propres pouvant se décomposer en somme de scores pondérés par seuil ou en somme de scores pondérés par quantile. Des techniques d'apprentissage séquentiel sont mises en oeuvre pour réaliser cette minimisation. Ces techniques fournissent des garanties théoriques de robustesse en termes de qualité de prévision, sous des hypothèses minimes. Ces méthodes sont appliquées à la prévision d'ensoleillement et à la prévision de production PV, fondée sur des prévisions météorologiques à haute résolution et sur des ensembles de prévisions classiques.Our main objective is to improve the quality of photovoltaic power forecasts deriving from weather forecasts. Such forecasts are imperfect due to meteorological uncertainties and statistical modeling inaccuracies in the conversion of weather forecasts to power forecasts. First we gather several weather forecasts, secondly we generate multiple photovoltaic power forecasts, and finally we build linear combinations of the power forecasts. The minimization of the Continuous Ranked Probability Score (CRPS) allows to statistically calibrate the combination of these forecasts, and provides probabilistic forecasts under the form of a weighted empirical distribution function. We investigate the CRPS bias in this context and several properties of scoring rules which can be seen as a sum of quantile-weighted losses or a sum of threshold-weighted losses. The minimization procedure is achieved with online learning techniques. Such techniques come with theoretical guarantees of robustness on the predictive power of the combination of the forecasts. Essentially no assumptions are needed for the theoretical guarantees to hold. The proposed methods are applied to the forecast of solar radiation using satellite data, and the forecast of photovoltaic power based on high-resolution weather forecasts and standard ensembles of forecasts

Ensemble forecast of photovoltaic power with online CRPS learning

International audienceWe provide probabilistic forecasts of photovoltaic (PV) production, for several PV plants located in France up to 6 days of lead time, with a 30-min timestep. First, we derive multiple forecasts from numerical weather predictions (ECMWF and Météo France), including ensemble forecasts. Second, our parameter-free online learning technique generates a weighted combination of the production forecasts for each PV plant. The weights are computed sequentially before each forecast using only past information. Our strategy is to minimize the Continuous Ranked Probability Score (CRPS). We show that our technique provides forecast improvements for both deterministic and probabilistic evaluation tools

Quelques mots sur une famille de Marseille du nom de Corbeau ou Courbeau, par J.-J.-A. Pilot,...

Appartient à l’ensemble documentaire : PACA1Avec mode text

Usages, fêtes & coutumes existant ou ayant existé en Dauphiné / par J.-J.-A. Pilot de Thorey,...

Collection : Bibliothèque historique du DauphinéCollection : Bibliothèque historique du DauphinéMécénat texte imprimé : Cet ouvrage a été numérisé grâce à Pascal Séguin à la mémoire de sa grand-mère maternell

DOSED: A deep learning approach to detect multiple sleep micro-events in EEG signal

International audienceBackground: Electroencephalography (EEG) monitors brain activity during sleep and is used to identify sleep disorders. In sleep medicine, clinicians interpret raw EEG signals in so-called sleep stages, which are assigned by experts to every 30 s window of signal. For diagnosis, they also rely on shorter prototypical micro-architecture events which exhibit variable durations and shapes, such as spindles, K-complexes or arousals. Annotating such events is traditionally performed by a trained sleep expert, making the process time consuming, tedious and subject to inter-scorer variability. To automate this procedure, various methods have been developed, yet these are event-specific and rely on the extraction of hand-crafted features.New method: We propose a novel deep learning architecure called Dreem One Shot Event Detector (DOSED). DOSED jointly predicts locations, durations and types of events in EEG time series. The proposed approach, applied here on sleep related micro-architecture events, is inspired by object detectors developed for computer vision such as YOLO and SSD. It relies on a convolutional neural network that builds a feature representation from raw EEG signals, as well as two modules performing localization and classification respectively.Results and comparison with other methods: The proposed approach is tested on 4 datasets and 3 types of events (spindles, K-complexes, arousals) and compared to the current state-of-the-art detection algorithms.Conclusions: Results demonstrate the versatility of this new approach and improved performance compared to the current state-of-the-art detection method

Development of a case fatality prognostic score for HIV-associated histoplasmosis

Objectives: The burden of histoplasmosis is as great as that of tuberculosis in Latin America and the attributable mortality is even higher. A better assessment of severity could help reduce mortality. Methods: From the French Guiana HIV-histoplasmosis database, we attempted to identify factors associated with 30-day death after antifungal drug initiation and constructed a prognostic score. We evaluated its discrimination performance using several resampling methods. Results: Of the 415 patients included, 56 (13.5%) died within 30 days of treatment. The fatality-associated factors were performance status ≥3, altered mental status, dyspnea, C-reactive protein ≥75 mg/l, hemoglobin <9 g/dl and/or a platelet <100000/ml, and an interstitial lung pattern on chest X-ray. We constructed a 12-point prognostic score. A threshold ≥5 classified patients as alive or dead at 30 days with a sensitivity of 84%, a specificity of 81%, a positive predicted value of 40%, and a negative predicted value of 97%. The area under the curve of the receiver operating characteristic curves from the different resamples were stable between 0.88 and 0.93. Conclusion: The histoplasmosis case fatality score, which is easy and inexpensive to perform, is a good tool for assessing severity and helping in the choice of induction therapy. An external validation remains necessary to generalize these results

Développement du premier score pronostique de l’histoplasmose associée au VIH : the Histoplasmosis case-Fatality Score

International audienceIn Latin America, the incidence of HIV-associated histoplasmosis is at least as high as that of tuberculosis, and mortality attributable to this fungal infection may even be higher. Severe forms of the disease are treated with induction therapy of liposomal amphotericin B, while others are treated with itraconazole. The choice of treatment therefore depends on an assessment of severity, which remains poorly defined and is based on the experience of the doctor.En Amérique latine, l’incidence de l’histoplasmose associée au VIH est au moins aussi élevée que celle de la tuberculose, et la mortalité attribuable à cette infection fongique pourrait même être supérieure. La prise en charge des formes graves repose sur un traitement d'induction par amphotéricine B liposomale, les autres étant traitées par itraconazole. Ce choix thérapeutique repose donc sur l'évaluation de la gravité, qui reste mal définie et repose sur l'expérience du médecine