![]() | INFLUENCE OF GLOBAL SOLAR RADIATION TYPICAL DAYS ON FORECASTING MODELS ERROR Auteur(s) : Soubdhan, Ted Voyant, Cyril Lauret, Philippe Auteurs secondaires : Laboratoire de Recherche en Géosciences et Énergies (LaRGE) ; Université des Antilles et de la Guyane (UAG) Sciences pour l'environnement (SPE) ; Université Pascal Paoli (UPP) - Centre National de la Recherche Scientifique (CNRS) Physique et Ingénierie Mathématique pour l'Énergie, l'environnemeNt et le bâtimenT (PIMENT) ; Université de la Réunion (UR) Éditeur(s) : HAL CCSD Résumé : International audience In this work, we have led an analysis of the error of different global solar radiation prediction models according to the global solar radiation variability. Different predictions models where performed such as machine learning techniques (Neural Networks, Gaussian processes and support vector machines) in order to forecast the Global Horizontal solar Irradiance (GHI). We also include in this study a simple linear autoregressive (AR) model as well as two naive models based on persistence of the GHI and persistence of the clear sky index (denoted herein scaled persistence model). The models are calibrated and tested with data from three 3 French islands: Corsica (42.15°N ; 9.08°E), Guadeloupe (16.25°N ; 61.58°W) and Reunion (21.15°S ; 55.5°E). Guadeloupe and Reunion are located in a subtropical climatic zone whereas Corsica is in a tempered climatic zone. Hence the global solar radiation variation differs significantly. The output error of the different models was quantified by the nRSME. In order to quantify the influence of the global solar radiation variability on the forecasting models error we performed a classification of typical days according to different typical days. Each class of typical day is defined by a variation of global solar radiation rate. For each class and for each location, the selected forecasting models where performed and the error was quantified. With this analysis a global solar radiation forecasting models can be selected according to the location, the global solar radiation fluctuations and hence the meteorological conditions. INTRODUCTION Large and frequent variations of solar radiation can be observed in tropical climates with amplitudes reaching 800 W/m² and occurring within a short time interval, from few seconds to few minutes, according to the geographical location. Such fluctuations can be due for example to the dynamic of clouds which can be very complex and depend on cloud type, size, speed and spatial distribution and, more generally, due to some specific local meteorological conditions. Thus, the solar energy forecasting, a process used to predict the amount of solar energy available in the current and near terms, might be a difficult task. Some of the best predictors found in literature are Autoregressive and moving average (ARMA) [5,7,8], Bayesian inferences [9,10], Markov chains [11], k-Nearest-Neighbors predictors [12] or artificial intelligence techniques as the Artificial Neural Network (ANN) [9-11]. Although these methodologies are potentially good in many areas, we observed in our previous studies on global radiation prediction [9,13,14] that the simple model based on the persistence of the clear sky index gives often very good results with acceptable errors [15] for short term forecasting time horizon (<= 1 hour). The goal of this paper is to determinate the influence of solar radiation variability regarding different classes of days on the expected error provided by different forecasting methods that the modeller can possibly implement. The paper is organized as follow: Section 2 describes the data we have used. Section 3 exposes the classification methodology and the results obtained for the three studied locations. In the two following sections, the forecasting methods are exposed and then 3 the errors on the forecasting results for each location and for each class are exposed. The Third Southern African Solar Energy Conference (SASEC2015) Kruger National Park, South Africa Droits : info:eu-repo/semantics/OpenAccess hal-01099487 https://hal.archives-ouvertes.fr/hal-01099487 https://hal.archives-ouvertes.fr/hal-01099487/document https://hal.archives-ouvertes.fr/hal-01099487/file/article%20SASEC.pdf | Partager |
![]() | Outils de prédiction pour la production d’électricité d’origine éolienne : application à l’optimisation du couplage aux réseaux de distributions d’électricité ; Forecasting tools for the electrical production of winds origin : application for the optimization of the coupling of electric power distribution networks Auteur(s) : Monjoly, Stéphanie Auteurs secondaires : Antilles-Guyane Zahibo, Narcisse Résumé : La forte variabilité de la vitesse du vent fait que l'énergie produite par un parc éolien n'est pas constante dans le temps. Le gestionnaire ne peut donc pas dimensionner son réseau électrique en prenant intégralement ce type de production en compte. L' une des solutions préconisées pour permettre le développement de l' éolien et son intégration avec une plus grande sureté aux réseaux, est de développer et d'améliorer les outils de prévisions. Le travail de thèse consiste à améliorer les performances d'un outil de prédiction basé sur les réseaux de neurones bayesiens, permettant la prédiction de la puissance à très court terme . Le prédicteur fonctionne notamment par J'ajustement de paramètres, certain se détermine « automatiquement » via le mécanisme des réseaux de neurones bayesiens d' autres, que nous nommerons paramètres temporels, sont à l' appréciation de l'utilisateur. Le travail mené consiste à établir un protocole pour la fixation de ces paramètres tout en améliorant les performances du prédicteur . Nous avons donc décidé de conditionner leurs valeurs en fonction de la variabilité des séquences de puissance précédent l'instant de prévision. Tout d'abord nous avons classifié des séquences de puissance en fonction de leurs coefficients de variation en appliquant la méthode des C-moyennes floues. Puis, chaque classe formée a été testée sur plusieurs valeurs de paramètres, les valeurs associées aux meilleures prédictions ont été retenues. Enfin, ces résultats couplés au formalisme des Chaines de Markov, par le biais de la matrice de transition , ont perm is d'obtenir des taux d'amélioration par rapport à la persistance allant de 7,73 à 23,22 % selon l'horizon de prédiction considéré The high variability of the wind speed has for conse quences that the energy produced by a wind farm is not constant over time. Therefore, the manager can't size the electrical network by takin g into account this type of production. One solution advocated for the development of wind energy and its integrati on with greater security at network, is to develop and improve fore casting tools. The thesi s objective is to improve the performance of a predi ction tool based on Bayesian neural networks, allowing the predi ction of wind power for short timescales. The predictor works, in part icular by the adjustment of parameters, sorne is determined "automatically" through the mechan ism of neural networks Bayesian other , which we cali temporal parameters are at the discretion of the user. The work involves establishing a protocol for the determination of these parameters and improving the performance of the predictor. So, we decided to condition their values depending on the sequence variability of wind power previous the moment of the forecast. First we classified sequences of power according to their coefficients of variation using the method of fuzzy C-means. Then, each formed class was tested for several parameters values, the values associated with the best predictions were selected. Finally , these result s coupled with the formalism of Markov chains , through the transition matrix allowed to obtain rates of improvement over the persistence ranging from 7.73 to 23.22 % depending on the prediction horizon considered http://www.theses.fr/2013AGUY0679/document | Partager |