ENR et réseaux de neurones

Multi-layer Perceptron and Pruning


Nouvel article accepté dans la revue "Turkish Journal of Forecasting"
A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks often
used in TS modeling and forecasting. Because of its “black box” aspect, many
researchers refuse to use it. Moreover, the optimization (often based on the exhaustive
approach where “all” configurations are tested) and learning phases of this artificial
intelligence tool (often based on the Levenberg-Marquardt algorithm; LMA) are
weaknesses of this approach (exhaustively and local minima). These two tasks must be
repeated depending on the knowledge of each new problem studied, making the process,
long, laborious and not systematically robust. In this short communication, a pruning
process is presented. This method allows, during the training phase, to carry out an
inputs selecting method activating (or not) inter-nodes connections in order to verify if
forecasting is improved. We propose to use iteratively the popular damped least-squares
method to activate inputs and neurons. A first pass is applied to 10% of the learning
sample to determine weights significantly different from 0 and delete other. Then a
classical batch process based on LMA is used with the new MLP. The validation is done
using 25 measured meteorological TS and cross-comparing the prediction results of the
classical LMA and the 2-stage LMA

https://www.researchgate.net/publication/317740944_Multi-layer_Perceptron_and_Pruning https://www.researchgate.net/publication/317740944_Multi-layer_Perceptron_and_Pruning



Jeudi 22 Juin 2017

-Tilos, the first autonomous renewable green island in Mediterranean: A Horizon 2020 project
-Solar potential for building integrated solar collectors: Application in Bulgaria, Romania & France
-Bounded global irradiation prediction based on multilayer perceptron and time series formalism


Participation au conngrés "15-th INTERNATIONAL CONFERENCE ON ELECTRICAL MACHINES, DRIVES, At Sofia (Bulgaria)"
Grâce a ce congrès nous avons pu développer et exposer nos dernières avancées concernant la prévision du rayonnement solaire global relatives au projet TILOS

https://www.researchgate.net/publication/315698876_Global_irradiation_forecasting_based_on_multilayer_perceptron https://www.researchgate.net/publication/315698876_Global_irradiation_forecasting_based_on_multilayer_perceptron
https://www.researchgate.net/publication/315698868_Bounded_global_irradiation_prediction_based_on_multilayer_perceptron_and_time_series_formalism https://www.researchgate.net/publication/315698868_Bounded_global_irradiation_prediction_based_on_multilayer_perceptron_and_time_series_formalism
https://www.researchgate.net/publication/315698862_Solar_potential_for_building_integrated_solar_collectors_Application_in_Bulgaria_Romania_France https://www.researchgate.net/publication/315698862_Solar_potential_for_building_integrated_solar_collectors_Application_in_Bulgaria_Romania_France



Jeudi 22 Juin 2017

Conference: IRCC; 4th International Conference on Energy, Sustainability and Climate Change, At Santorini (Greece)


Participation à un congés en Grèce: "Kalman filtering and classical time series tools for global irradiation forecasting"
Cette conférence nous permet de présenter nos derniers travaux publier en 2017 relatifs à la prévision du l'irradiation solaire avec une méthode dite "trainless" qui ne nécessite pas d'historique d'apprentissage (à opposer aux méthodes de machine learning et de data driven)






Jeudi 22 Juin 2017

Global irradiation forecasting based on multilayer perceptron


European energy dependence costs 350 billion Euros annually. For that Europe built an European strategy for energy with three objectives that fall under the security of supply, competitiveness and sustainability. This aims is to ensure citizens and EU companies a safe and environmentally friendly energy at an affordable price. For this, the share of RES in Europe will rise from 14.1 % up to 27% by 2030 .

In this context, scientific research is directed towards an intelligent energy management at various levels from housing to city or regional scale.

At each level, you must have access to energy-efficient items, including intelligent management of stationary or mobile equipment, whether consumers, producers or storage of electricity included in renewable energy smart grids.

An example of the concept of intelligent building is the integration of energy management solutions in housing and business buildings, especially to achieve positive energy and consequently optimally manage this energy on a larger scale.

In this approach, the need to observe, analyze and control physical phenomena at these different levels requires environmental and urban metrology applications. This new way of looking metrology, by detecting a phenomenon at various points scattered on a site, inevitably leads to new technological issues (type and number of sensors, energy independence, need communications), new scientific issues (smart initiatives, sensor cooperation, humans interaction, connected objects).

Providing answers to these concepts opens up many opportunities for innovative applications to make smart building or smart cities. Not only the concept of smart building is a major concern for environmental issues (optimization of energy consumption) but the smart building must also meet the needs of user comfort by providing a set of individual services. Finally, the definition of uses, the design of an adequate network of sensors, data collection and fusion of sensor data, the implementation of a human interaction network interface are among the key parameters to be addressed to meet socio- economic issues.

This multidisciplinary and interdisciplinary approach is positioning itself on the border of two complementary areas. The innovation is the coupling between the BCIM (Building Information Modeling) and sensor networks for the development of business intelligence processes in Smart Building. This approach is part of a commitment in order to upgrading the technology platform “Paglia-Orba” implemented at Ajaccio (University of Corsica/CNRS).


https://www.researchgate.net/publication/315698876_Global_irradiation_forecasting_based_on_multilayer_perceptron https://www.researchgate.net/publication/315698876_Global_irradiation_forecasting_based_on_multilayer_perceptron



Jeudi 22 Juin 2017

-Machine Learning methods for solar radiation forecasting: a review (Renewable energy)

-Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case (Energy)

-Forecasting method for global radiation time series without training phase: comparison with other well-known prediction méthodologies (Energy)


Projet TILOS: trois articles acceptés dans les revues "Energy" et "Renewable Energy"

https://www.researchgate.net/project/TILOS-project-H2020 https://www.researchgate.net/project/TILOS-project-H2020



Lundi 2 Janvier 2017

Mean absolute log-return and solar radiation forecastabity
Cyril Voyant, Ted Soubdhan, Philippe Lauret, Mathieu David, Marc Muselli


Papier accepté pour la conférence ISES Solar World Congress 2015
Lors de cette conférence nous exposerons les principaux résultats de nos travaux publiés dans l'article Statistical parameters as a means to a priori assess the accuracy of solar forecasting models. Le résumé est:
"In this paper we propose to determinate and to test a set of statistical parameters (20) to estimate the predictability of the global horizontal irradiation time series and thereby propose a new prospective tool indicating the expected error regardless the forecasting methods a modeller can possibly implement. The mean absolute log return, which is a tool usually used in econometry, proves to be a very good estimator. Some examples of the use of this tool are exposed, showing the interest of this statistical parameter in concrete cases of predictions or optimizations."

ises.pdf ISES.pdf  (483.2 Ko)



Vendredi 24 Juillet 2015

Statistical parameters as a means to a priori assess the accuracy of solar forecasting models
Cyril Voyant, Ted Soubdhan, Philippe Lauret, Mathieu David, Marc Muselli


Nouveau papier accepté dans la revue Energy Journal
Dans ce papier , nous démontrons qu'il existe des paramètres statistiques (tel le rendement logarithmique) qui permettent d'estimer l'erreur de prédiction relative à une série temporelle spécifique, et ce quelque soit la méthode de machine learning utilisée. A travers 3 exemples simples, nous proposons une méthodologie d'utilisation de ces paramètres. Le résumé est:

"In this paper we propose to determinate and to test a set of statistical parameters (20) to estimate the predictability of the global horizontal irradiation time series and thereby propose a new prospective tool indicating the expected error regardless the forecasting methods a modeller can possibly implement. The mean absolute log return, which is a tool usually used in econometry, proves to be a very good estimator. Some examples of the use of this tool are exposed, showing the interest of this statistical parameter in concrete cases of predictions or optimizations."

discriminent_param.pdf discriminent_param.pdf  (836.37 Ko)



Vendredi 24 Juillet 2015

Energétique, génie des procédés (62° CNU)


Nous recherchons des candidats pour une thèse débutant en septembre 2015. Cette thèse sera rattachée à l'UMR CNRS SPE 6134 (Projet Structurant ENR) de l'université de Corse. L'étudiant sera basé sur Ajaccio.

Modélisation stochastique et prévisions numériques du rayonnement solaire pour le pilotage de la plateforme hybride de R&D MYRTE.

Résumé: Lors de cette thèse, l’étudiant aura la tâche de manipuler des séries temporelles, des outils de modélisation stochastique (ANN, SVM, etc.), des sorties de modèles globaux (ECMWF, AROME, etc.), et des modèles numériques régionaux haute résolution (WRF ou Méso-NH) dans le but de développer un outil de prévision du rayonnement solaire à différents horizons, intégré au système de contrôle-commande de la plateforme PV MYRTE, pour la production d’énergie électrique sur un réseau non-interconnecté.

Contact:
-MUSELLI Marc, PRU, directeur de thèse
marc.muselli@univ-corse.fr / 06 08 07 05 01
-VOYANT Cyril, Dr, (PAST – adossement Projet ENR), co-directeur de thèse
voyant@univ-corse.fr

1_fiche_these_2015_16.doc 1 Fiche_Thèse 2015-16.doc  (63 Ko)



Dimanche 19 Avril 2015

Short-term solar irradiance and irradiation forecasts via different time series techniques: A preliminary study

C. Join, C. Voyant, M. Fliess, M. Muselli, ML. Nivet, C. Paoli, F. Chaxel.


Papier accepté lors de " the 3rd International Symposium on Environment-Friendly Energies and Applications (EFEA 2014)"
Avec ce papier, nous proposons une comparaison des méthodes de prédictions du rayonnement global nécessitant beaucoup d'historique de mesure (ANN) et d'autre plus simples à mettre en oeuvre et ne nécessitant que de très peu de de données (pas de phase d'apprentissage, i.e. persistance, model-free control). Le résumé de ce papier est:
"This communication is devoted to solar irradiance and irradiation short-term forecasts, which are useful for electricity production. Several different time series approaches are employed. Our results and the corresponding numerical simulations show that techniques which do not need a large amount of historical data behave better than those who need them, especially when those data are quite noisy."

http://hal.archives-ouvertes.fr/index.php?halsid=j888b60cn1eica5seponkg6pr3&view_this_doc=hal-01068569&version=1 http://hal.archives-ouvertes.fr/index.php?halsid=j888b60cn1eica5seponkg6pr3&view_this_doc=hal-01068569&version=1



Jeudi 25 Septembre 2014

Meteorological time series forecasting with pruned multi-layer perceptron and 2-stage Levenberg-Marquardt method

cyril voyant, Wani Tamas, Marie-Laure Nivet, Gilles Notton, Christophe Paoli, Aurélia Balu, Marc Muselli


Nouvel article accepté dans un journal indersceince: "International Journal of Modelling, Identification and Control"
Cet article fait suite au travail mené et valorisé lors de l'ICM² 2014 de Prague. La nouvelle méthodologie de pruning exposée semble donner de bon résultats, il convient maintenant de la comparer avec les méthodes existantes afin de d'établir une hiérarchisation des algorithmes sur le cas réel de séries temporelles de rayonnement global. L'abstract :

"A Multi-Layer Perceptron (MLP) defines a family of artificial neural networks often used in TS modeling and forecasting. Because of its “black box” aspect, many researchers refuse to use it. Moreover, the optimization (often based on the exhaustive approach where “all” configurations are tested) and learning phases of this artificial intelligence tool (often based on the Levenberg-Marquardt algorithm; LMA) are weaknesses of this approach (exhaustively and local minima). These two tasks must be repeated depending on the knowledge of each new problem studied, making the process, long, laborious and not systematically robust. In this paper a pruning process is proposed. This method allows, during the training phase, to carry out an inputs selecting method activating (or not) inter-nodes connections in order to verify if forecasting is improved. We propose to use iteratively the popular damped least-squares method to activate inputs and neurons. A first pass is applied to 10% of the learning sample to determine weights significantly different from 0 and delete other. Then a classical batch process based on LMA is used with the new MLP. The validation is done using 25 measured meteorological TS and cross-comparing the prediction results of the classical LMA and the 2-stage LMA."

https://www.researchgate.net/publication/263717805_Meteorological_time_series_forecasting_with_pruned_multi-layer_perceptron_and_2-stage_Levenberg-Marquardt_method?ev=prf_pub https://www.researchgate.net/publication/263717805_Meteorological_time_series_forecasting_with_pruned_multi-layer_perceptron_and_2-stage_Levenberg-Marquardt_method?ev=prf_pub



Mardi 8 Juillet 2014

Meteorological time series forecasting based on MLP modelling using heterogeneous transfer functions
C. Voyant, ML. Nivet, C. Paoli, M. Muselli and G. Notton


Papier accepté à la conférence "International Conference on Mathematical Modeling in Physical Sciences"
Dans ce papier nous proposons une nouvelle variété de perceptrons multicouches avec plusieurs fonctions de transferts. L'apport de ce nouvel outil est testé sur différentes séries temporelles météorologiques, montrant ainsi un léger gain dés qu'un indice temporelle est positionné en entrée (variable auto-indicatice) .
Le résumé de ce papier est :
"In this paper, we propose to study four meteorological and seasonal time series coupled with a multi-layer perceptron (MLP) modeling. We chose to combine two transfer functions for the nodes of the hidden layer, and to use a temporal indicator (time index as input) in order to take into account the seasonal aspect of the studied time series. The results of the prediction concern two years of measurements and the learning step, eight independent years. We show that this methodology can improve the accuracy of meteorological data estimation compared to a classical MLP modelling with a homogenous transfer function."

icmsquare2014.pdf ICMSQUARE2014.pdf  (439.5 Ko)



Mardi 29 Avril 2014

Estimation of 5-min time-step data of tilted solar global irradiation using ANN model
K. Dahmani, R. Dizene, G. Notton, C. Paoli, ML Nivet, C. Voyant.


Nouvel article accepté dans le journal "Energy"
Ce papier est le résultat du travail de thèse de Kahina Dahmani (étudiante Algérienne co-encadrée par Gilles Notton de l'université de Corse). Il fait suite à ce qui a été présenté lors de la conférence de Jijel au mois de septembre dernier. Les outils déjà étudiés lors de la prévision de la ressource solaire (ANN+processus d’optimisation ad-hoc), sont ici exploités pour incliner le rayonnement horizontal. Les résultats exposés sont légèrement meilleurs que ce que l'on peut trouver dans la littérature (modèles analytiques de type Klutcher, Climed,...), il faudra toutefois par la suite, généraliser cette conclusion en étudiant d'autres sites, en modifiant le time step d'acquisition ou en modifiant le nombre de data utilisées lors de l'apprentissage. Le résumer de ce papier est:

"Converting measured horizontal global solar irradiance in tilted ones is a difficult task, particularly for a small time-step and for not-averaged data. Conventional methods (statistical, correlation, …) are not always efficient with time-step less than one hour; thus, we chose to use an Artificial Neural Network (ANN) to realize this conversion applied to 5-min solar radiation data of Bouzareah, Algeria. The ANN is developed and optimized using two years of solar data; the nRMSE is
around 8% for the optimal configuration, which corresponds to a very good accuracy for such a short time-step."


Dimanche 6 Avril 2014

URBAN OZONE CONCENTRATION FORECASTING WITH ARTIFICIAL NEURAL NETWORK IN CORSICA
Wani Tamas, Gilles Notton, Christophe Paoli, Cyril Voyant, Marie-Laure Nivet, Aurelia Balu


Version améliorée du papier de la conférence EENVIRO dans "Mathematical Modelling in Civil Engineering"
A la suite de la conférence de Bucarest, les organisateur ont proposé à Wani Tamas de soumettre une version améliorée de son travail. Dans ce dernier, on y trouve des résultats liés à l'utilisation d'outils classiques de prévision de séries temporelles appliqués au cas des polluants atmosphériques. Des futures améliorations des méthodologies de prédictions sont en cours d'expérimentation et devraient permettre d'améliorer encore la qualité des résulats.
Le résumé de ce papier est:
"Atmospheric pollutants concentration forecasting is an important issue in air quality monitoring. Qualitair Corse, the organization responsible for monitoring air quality in Corsica (France), needs to develop a short-term prediction model to lead its mission of information towards the public. Various deterministic models exist for local forecasting, but need important computing resources, a good knowledge of atmospheric processes and can be inaccurate because of local
climatical or geographical particularities, as observed in Corsica, a mountainous island located in the Mediterranean Sea. As a result, we focus in this study on statistical models, and particularly Artificial Neural Networks (ANNs) that have shown good results in the prediction of ozone concentration one hour ahead with data measured locally. The purpose of this study is to build a predictor realizing predictions of ozone 24 hours ahead in Corsica in order to be able to anticipate pollution peaks formation and to take appropriate preventive measures. Specific meteorological
conditions are known to lead to particular pollution event in Corsica (e.g. Saharan dust events). Therefore, an ANN model will be used with pollutant and meteorological data for operational forecasting. Index of agreement of this model was calculated with a one year test dataset and reached 0.88."

http://mmce.rs.utcb.ro/component/content/article/18-2014/49-scientific-journal-mathematical-modeling-no-1-2014.html http://mmce.rs.utcb.ro/component/content/article/18-2014/49-scientific-journal-mathematical-modeling-no-1-2014.html



Samedi 5 Avril 2014

Colloque Martinique Energie Environnement 2014 / Schoelcher, Martinique / 4 - 9 mai 2014


Participation à un colloque organisé par l'Agence Martiniquaise de l'Energie
Le colloque Martinique Energie et Environnement 2014 est organisé par le département Hygiène Sécurité Environnement de l’Institut Universitaire de Technologie (IUT-HSE) de l’Université Antilles-Guyane (UAG) et l’Agence Martiniquaise de l’Energie (AME). Pour la France, l’insularité a une implication majeure en ce qui concerne la problématique énergétique : les îles françaises (Martinique, Guadeloupe, Corse, Réunion) sont des zones non interconnectées (ZNI). L’électricité consommée doit être produite sur place. Le recours à l’énergie solaire est une bonne alternative au fuel pour produire cette électricité mais la variation de la production associée à une ressource difficilement prévisible limite son utilisation sur les réseaux îliens. L’amélioration de la prévision de la ressource solaire est l’une des voies à même de faire évoluer les limites actuelles. Notre contribution à ces journées thématiques se fera par le biais d'une présentation exposant nos dernières méthodologies utilisant le fomalisme des séries temporelles et visant à prédire le rayonnement global sur un territoire étendu. Le titre et le résumé sont:

Time series modeling and large scale global solar radiation forecasting from geostationary satellites data

Abstract. When a territory is poorly instrumented, geostationary satellites data can be useful to predict global solar radiation. In this presentation, we use geostationary satellites data to generate 2-D time series of solar radiation for the next hour. The results relate to a particular territory, the Corsica Island, but as data used are available for the entire surface of the globe, our method can be easily exploited to another place. Indeed 2-D hourly time series are extracted from the HelioClim-3 surface solar irradiation database treated by the Heliosat-2 model. Each point of the map have been used as training data and inputs of artificial neural networks (ANN) and as inputs for two persistence models (scaled or not). Comparisons between these models and clear sky estimations were proceeded to evaluate the performances.

http://energie.mq/cm2e2014/ http://energie.mq/cm2e2014/



Samedi 15 Février 2014

Numerical weather prediction or stochastic modeling: an objective criterion of choice for the global radiation forecasting
Cyril Voyant, Gilles Notton, Christophe Paoli, Marie-Laure Nivet, Marc Muselli, Kahina Dahmani


Nouvel article accepté dans le journal "International journal of energy technology and policy"
Dans cet article, nous proposons une méthodologie afin de déterminer en fonction du site, et des caractéristiques du rayonnement global sur un territoire donné, si les modèles de type Numerical weather prediction et ceux de type stochastique sont applicables et pertinents. Le résumé du papier est:

"Numerous methods exist and were developed for global radiation forecasting. The two most popular types are the numerical weather predictions (NWP) and the predictions using stochastic approaches. We propose to compute a parameter noted  constructed in part from the mutual information which is a quantity that measures the mutual dependence of two variables. Both of these are calculated with the objective to establish the more relevant method between NWP and stochastic models concerning the current problem."

https://www.researchgate.net/publication/259820729_Numerical_weather_prediction_or_stochastic_modeling_an_objective_criterion_of_choice_for_the_global_radiation_forecasting?ev=prf_pub https://www.researchgate.net/publication/259820729_Numerical_weather_prediction_or_stochastic_modeling_an_objective_criterion_of_choice_for_the_global_radiation_forecasting?ev=prf_pub



Mercredi 22 Janvier 2014
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