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2 results on '"Jacques Moussafir"'

1. An adaptive Bayesian inference algorithm to estimate the parameters of a hazardous atmospheric release

Author

Christophe Olry, Yves Delignon, François Septier, Armand Albergel, Jacques Moussafir, Patrick Armand, Harizo Rajaona, Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS), DAM Île-de-France (DAM/DIF), Direction des Applications Militaires (DAM), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Institut TELECOM/TELECOM Lille1, Institut Mines-Télécom [Paris] (IMT), and ARIA Technologies

Subjects
Pointwise, Atmospheric Science, [STAT.AP]Statistics [stat]/Applications [stat.AP], Source term estimation, Computer science, Bayesian probability, Bayesian inference, Probabilistic logic, Markov chain Monte Carlo, Adaptive multiple importance sampling, Inverse problem, symbols.namesake, Monte-Carlo techniques, Prior probability, symbols, [STAT.CO]Statistics [stat]/Computation [stat.CO], Algorithm, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [STAT.ME]Statistics [stat]/Methodology [stat.ME], Importance sampling, General Environmental Science
Abstract

International audience; In the eventuality of an accidental or intentional atmospheric release, the reconstruction of the source term using measurements from a set of sensors is an important and challenging inverse problem. A rapid and accurate estimation of the source allows faster and more efficient action for first-response teams, in addition to providing better damage assessment.This paper presents a Bayesian probabilistic approach to estimate the location and the temporal emission profile of a pointwise source. The release rate is evaluated analytically by using a Gaussian assumption on its prior distribution, and is enhanced with a positivity constraint to improve the estimation. The source location is obtained by the means of an advanced iterative Monte-Carlo technique called Adaptive Multiple Importance Sampling (AMIS), which uses a recycling process at each iteration to accelerate its convergence.The proposed methodology is tested using synthetic and real concentration data in the framework of the Fusion Field Trials 2007 (FFT-07) experiment. The quality of the obtained results is comparable to those coming from the Markov Chain Monte Carlo (MCMC) algorithm, a popular Bayesian method used for source estimation. Moreover, the adaptive processing of the AMIS provides a better sampling efficiency by reusing all the generated samples.

Published
2015
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2. The Anemos Project : Next Generation forecasting of Wind power

Author

Georges Kariniotakis, Didier Mayer, Heinemann Detlev, Jens Tambke, Julio Usaola, Ignacio Marti, Henrik Madsen, Torben Skov Nielsen, Christine Lac, Waldl, H. P., Félix Dierich, Jim Halliday, Richard Brownsword, Gregor Giebel, Jake Badger, Jacques Moussafir, George Kallos, Petroula Louka, Thierry Jouhanique, Manuel Calleja, John Toefting, Ryan, M., Carrington, M., John Indorf, Antiopi Gigantidou, Gonzales, G., Barosso, E., Barquero, C., Seguardo, P., Ignacio Cruz, Ana Maria Palomares, Hatziargyriou, Nikos D., Centre Énergétique et Procédés (CEP), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), University of Oldenburg, Universidad Carlos III de Madrid [Madrid] (UC3M), Centro Nacional de Energías Renovables - Fundación CENER-CIEMAT (CENER), Technical University of Denmark [Lyngby] (DTU), Météofrance, Météo France, overspeed GmBh&CoKG, GmBh&CoKG, CCLRC Rutherford Appleton Laboratory (RAL), Risø National Laboratory, Danish Ministry of Science, Technology and Innovation, ARIA Technologies, Institute of Accelerating Systems and Applications [Athens] (IASA), EDF R&D (EDF R&D), EDF (EDF), EHN, ELSAM, ESB National Grid - Power System Operations, ESB National Grid - Power System Operation, Energieversorgung Weser-Ems AG (EWE AG), Public Power Corporation - Grèce (PPC), PPC, Red Eléctrica de España (REE), Red Eléctrica de España, IDAE - Spain, Centro de Investigaciones Energéticas Medioambientales y Tecnológicas [Madrid] (CIEMAT), Institute of Communication and Computer Systems, Department of Electrical and Computer Engineering (ICCS), National Technical University of Athens [Athens] (NTUA), European Project: ENK5-CT-2002-00665,ANEMOS, Mines Paris - PSL (École nationale supérieure des mines de Paris), Danmarks Tekniske Universitet = Technical University of Denmark (DTU), Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)

Subjects
on-line software, numerical w eather predictions, short-term forecasting, [SPI.NRJ]Engineering Sciences [physics]/Electric power, wind energy, forecasting, Wind power, energy meteorology, renewable energy
Abstract

International audience; This paper presents the objectives and the research work carried out in the frame of the ANEMOS project on short-term wind power forecasting. The aim of the project is to develop accurate models that substantially outperform current state-of-the-art methods, for onshore and offshore wind power forecasting, exploiting both statistical and physical modeling approaches. The project focus on prediction horizons up to 48 hours ahead and investigates predictability of wind for higher horizons up to 7 days ahead useful i.e. for maintenance scheduling. Emphasis is given on the integration of high-resolution meteorological forecasts. For the offshore case, marine meteorology is considered as well as information by satellite-radar images. Specific modules are also developed for on-line uncertainty and prediction risk estimation. An integrated software platform, 'ANEMOS', is developed to host the various models. This system will be installed by several end-users for on-line operation at onshore and offshore wind farms for prediction at a local, regional and national scale. The applications include different terrain types and wind climates, on- and offshore cases, and interconnected or island grids. The on-line operation by the utilities will allow validation of the models and an analysis of the value of wind prediction for a competitive integration of wind energy in the developing liberalized electricity markets.

Published
2004

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