Lepore, Antonio (2009) An Integrated Approach to shorten wind potential assessment. [Tesi di dottorato] (Inedito)

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Tipologia del documento:	Tesi di dottorato
Lingua:	Italiano
Titolo:	An Integrated Approach to shorten wind potential assessment
Autori:	Autore Email Lepore, Antonio antonio.lepore@unina.it
Data:	2009
Numero di pagine:	120
Istituzione:	Università degli Studi di Napoli Federico II
Dipartimento:	Ingegneria aerospaziale
Scuola di dottorato:	Ingegneria industriale
Dottorato:	Ingegneria aerospaziale, navale e della qualità
Ciclo di dottorato:	22
Coordinatore del Corso di dottorato:	nome email Moccia, Antonio antonio.moccia@unina.it
Tutor:	nome email Erto, Pasquale [non definito] Lanzotti, Antonio [non definito]
Data:	2009
Numero di pagine:	120
Parole chiave:	Wind Energy, MCMC Bayesian evaluation, Plotting positions, Process Capability
Settori scientifico-disciplinari del MIUR:	Area 13 - Scienze economiche e statistiche > SECS-S/02 - Statistica per la ricerca sperimentale e tecnologica
Depositato il:	20 Mag 2010 08:36
Ultima modifica:	30 Apr 2014 19:38
URI:	http://www.fedoa.unina.it/id/eprint/3833

Abstract

The growth of new wind projects continues to be hampered by the lack of wind resource data. Such data are needed to enable governments and/or private developers to identify potential areas suitable for development. The principal aim of the work is the formalization of an innovative approach that hastens a robust parameter estimation of statistical models on anemometric data. The developed methodologies have been theoretical and have concerned: -parameter estimation of wind speed stochastic model, based on short-run samples, able to integrate both the information contained in the wind atlases, and the expert opinion through a Practical Bayesian approach, after an opportune data filtering strategy; -the determination of new "plotting positions", crucial for the graphical estimation of the parameters in the engineering and environmental fields. Particular attention has been given to the application on the (asymptotic) distributions of extreme values, widely used in environmental modeling and in "return period." estimation. Such methodologies have also been implemented via numerical code developed in Mathematica® e/o Matlab®. Particularly, computational problems arisen from the proposed Bayesian estimation are faced through MCMC (Markov Chain Monte Carlo) technique, numerically implemented in R/WinBugs. The aforementioned methodologies are proposed as solution to real problems raised by renewable energies companies. The interaction with the latter was essential to elicitate the expert opinion on the wind sites and for allowing methodologies to be validated on real anemometric data, collected from southern Italian sites. It has been possible to observe (ex post) smaller estimation errors in comparison to those that would be derived from the application of the usual estimation methods presented in the literature.

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