LEADER 00880nam a2200241 i 4500 001 991003569649707536 005 19851216000000.0 008 080421s1977 fr b r 000 0 fre 035 $ab13717133-39ule_inst 040 $aDip.to Beni Arti e Storia$bita 082 00$a780/.25/4 100 1 $aDarde, Jean-Noèel$0628349 245 10$aGuide pratique de la musique /$cJean-Noèel Darde ; con la collaborazione di Jean Rolin. 260 $aParigi :$bSeghers,$c1977. 300 $a442 p. :$btav. ;$c21 cm. 650 0$aMusiac$zEuropa 700 1 $aRolin, Jean 907 $a.b13717133$b02-04-14$c21-04-08 912 $a991003569649707536 945 $aLE019 A24 MUS F 16$g1$i2019000080477$lle019$op$pE19.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i14731253$z21-04-08 996 $aGuide pratique de la musique$91230479 997 $aUNISALENTO 998 $ale019$b21-04-08$cm$da $e-$ffre$gfr $h0$i0 LEADER 05331nam 22006255 450 001 9910303445503321 005 20251230061125.0 010 $a3-319-99052-7 024 7 $a10.1007/978-3-319-99052-1 035 $a(CKB)4100000007335162 035 $a(MiAaPQ)EBC5626873 035 $a(DE-He213)978-3-319-99052-1 035 $a(PPN)232965250 035 $a(EXLCZ)994100000007335162 100 $a20181227d2018 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aRenewable Energy: Forecasting and Risk Management $eParis, France, June 7-9, 2017 /$fedited by Philippe Drobinski, Mathilde Mougeot, Dominique Picard, Riwal Plougonven, Peter Tankov 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (252 pages) 225 1 $aSpringer Proceedings in Mathematics & Statistics,$x2194-1017 ;$v254 311 08$a3-319-99051-9 327 $aPart I Renewable energy: modeling and forecasting -- Alain Bensoussan and Alexandre Brouste -Marginal Weibull diffusion model for wind speed modeling and short-term forecasting -- Bastien Alonzo, Riwal Plougonven, Mathilde Mougeot, Aurélie Fischer, Aurore Dupré, and Philippe Drobinski-From Numerical Weather Prediction outputs to accurate localsurface wind speed : statistical modeling and forecasts. - Mireille Bossy, Aurore Dupr´e, Philippe Drobinski, Laurent Violeau, and Christian Briard-Stochastic Lagrangian approach for wind farm simulation -- Jordi Badosa, Emmanuel Gobet, Maxime Grangereau and Daeyoung Kim-Day-ahead probabilistic forecast of solar irradiance: a Stochastic Differential Equation approach -- Mathilde Mougeot, Dominique Picard, Vincent Lefieux, Miranda Marchand-Homogeneous climate regions using learning algorithms -- Andrs Castrillejo, Jairo Cugliari, Fernando Massa, and Ignacio Ramirez- Electricity Demand Forecasting: the Uruguayan Case Eugene A. Feinberg and Jun Fei -A Flexible Mixed Additive-Multiplicative Model for Load Forecasting in a Smart Grid Setting -- Jérôme Collet and Michael Richard- A Generic Method for Density Forecasts Recalibration -- Part II Renewable energy: risk management -- Vera Silva, Miguel López-Botet Zulueta, Ye Wang, Paul Fourment,Timothee Hinchliffe, Alain Burtin, and Caroline Gatti-Bono-Technical and economic analysis of the European electricity system with 60% renewable energy sources -- Deschatre and Almut E. D. Veraart-A joint model for electricity spot prices and wind penetration with dependence in the Thomas -- James Cruise and Stan Zachary-The optimal control of storage for arbitrage and buffering, with energy applications -- Jérôme Collet, Olivier Féron, and Peter Tankov-Optimal management of a wind power plant with storage capacity.-References. 330 $aGathering selected, revised and extended contributions from the conference ?Forecasting and Risk Management for Renewable Energy FOREWER?, which took place in Paris in June 2017, this book focuses on the applications of statistics to the risk management and forecasting problems arising in the renewable energy industry. The different contributions explore all aspects of the energy production chain: forecasting and probabilistic modelling of renewable resources, including probabilistic forecasting approaches; modelling and forecasting of wind and solar power production; prediction of electricity demand; optimal operation of microgrids involving renewable production; and finally the effect of renewable production on electricity market prices. Written by experts in statistics, probability, risk management, economics and electrical engineering, this multidisciplinary volume will serve as a reference on renewable energy risk management and at the same time as a source of inspiration for statisticians and probabilists aiming to work on energy-related problems. 410 0$aSpringer Proceedings in Mathematics & Statistics,$x2194-1017 ;$v254 606 $aGeography$xMathematics 606 $aStatistics 606 $aRenewable energy sources 606 $aProbabilities 606 $aMathematics of Planet Earth 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 606 $aRenewable Energy 606 $aProbability Theory 615 0$aGeography$xMathematics. 615 0$aStatistics. 615 0$aRenewable energy sources. 615 0$aProbabilities. 615 14$aMathematics of Planet Earth. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aRenewable Energy. 615 24$aProbability Theory. 676 $a621.042 702 $aDrobinski$b Philippe$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMougeot$b Mathilde$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPicard$b Dominique$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPlougonven$b Riwal$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aTankov$b Peter$4edt$4http://id.loc.gov/vocabulary/relators/edt 906 $aBOOK 912 $a9910303445503321 996 $aRenewable Energy: Forecasting and Risk Management$91563743 997 $aUNINA