Bayesian statistical analysis applied to solar radiation modelling
Identifieur interne : 005079 ( Main/Exploration ); précédent : 005078; suivant : 005080Bayesian statistical analysis applied to solar radiation modelling
Auteurs : Philippe Lauret [France] ; John Boland [Australie] ; Barbara Ridley [Australie]Source :
- Renewable energy [ 0960-1481 ] ; 2013.
Descripteurs français
- Pascal (Inist)
English descriptors
Abstract
This paper proposes to use a rather new statistical approach in the realm of solar radiation modelling namely Bayesian inference. In this work, the theory of Bayesian inference will be presented at length. The Bayesian analysis consists in two levels. The first one is related to the parameter estimation while the second one concerns the model selection problem. As an illustration, a Bayesian parameter estimation method is used to derive a logistic hourly solar diffuse fraction model. A major difference between Bayesian and frequentist (or classical) methods is that the Bayesian inference offers a framework (through the use of prior information) to continuously update our posterior beliefs. In other words, all previous work is not wasted as the preceding model's parameters can be used as prior information for the derivation of the parameters estimates of the next (new) model. For this particular application, it is also shown that the use of Bayesian methods instead of classical statistical techniques lead to a less biased model.
Affiliations:
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School of Mathematics and Statistics, University of South Australia, Mawson Lakes Boulevard</s1><s2>Mawson Lakes, SA 5095</s2><s3>AUS</s3><sZ>2 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>Australie</country><wicri:noRegion>Mawson Lakes, SA 5095</wicri:noRegion></affiliation></author><author><name sortKey="Ridley, Barbara" sort="Ridley, Barbara" uniqKey="Ridley B" first="Barbara" last="Ridley">Barbara Ridley</name><affiliation wicri:level="1"><inist:fA14 i1="02"><s1>Barbara Hardy Institute and School of Mathematics and Statistics, University of South Australia, Mawson Lakes Boulevard</s1><s2>Mawson Lakes, SA 5095</s2><s3>AUS</s3><sZ>2 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>Australie</country><wicri:noRegion>Mawson Lakes, SA 5095</wicri:noRegion></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">15-0051949</idno><date when="2013">2013</date><idno type="stanalyst">PASCAL 15-0051949 INIST</idno><idno 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wicri:level="1"><inist:fA14 i1="02"><s1>Barbara Hardy Institute and School of Mathematics and Statistics, University of South Australia, Mawson Lakes Boulevard</s1><s2>Mawson Lakes, SA 5095</s2><s3>AUS</s3><sZ>2 aut.</sZ><sZ>3 aut.</sZ></inist:fA14><country>Australie</country><wicri:noRegion>Mawson Lakes, SA 5095</wicri:noRegion></affiliation></author></analytic><series><title level="j" type="main">Renewable energy</title><title level="j" type="abbreviated">Renew. energy</title><idno type="ISSN">0960-1481</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Renewable energy</title><title level="j" type="abbreviated">Renew. energy</title><idno type="ISSN">0960-1481</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Modeling</term><term>Renewable energy</term><term>Solar radiation</term><term>Statistical analysis</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Analyse statistique</term><term>Rayonnement solaire</term><term>Modélisation</term><term>Energie renouvelable</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">This paper proposes to use a rather new statistical approach in the realm of solar radiation modelling namely Bayesian inference. In this work, the theory of Bayesian inference will be presented at length. The Bayesian analysis consists in two levels. The first one is related to the parameter estimation while the second one concerns the model selection problem. As an illustration, a Bayesian parameter estimation method is used to derive a logistic hourly solar diffuse fraction model. A major difference between Bayesian and frequentist (or classical) methods is that the Bayesian inference offers a framework (through the use of prior information) to continuously update our posterior beliefs. In other words, all previous work is not wasted as the preceding model's parameters can be used as prior information for the derivation of the parameters estimates of the next (new) model. For this particular application, it is also shown that the use of Bayesian methods instead of classical statistical techniques lead to a less biased model.</div></front></TEI><affiliations><list><country><li>Australie</li><li>France</li></country></list><tree><country name="France"><noRegion><name sortKey="Lauret, Philippe" sort="Lauret, Philippe" uniqKey="Lauret P" first="Philippe" last="Lauret">Philippe Lauret</name></noRegion></country><country name="Australie"><noRegion><name sortKey="Boland, John" sort="Boland, John" uniqKey="Boland J" first="John" last="Boland">John Boland</name></noRegion><name sortKey="Ridley, Barbara" sort="Ridley, Barbara" uniqKey="Ridley B" first="Barbara" last="Ridley">Barbara Ridley</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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