Statistical Modeling for Improvement of Numerical-Model-Based Solar Radiation Forecasts

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F18%3A00477273" target="_blank" >RIV/67985807:_____/18:00477273 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Statistical Modeling for Improvement of Numerical-Model-Based Solar Radiation Forecasts

Popis výsledku v původním jazyce

ZÁKLADNÍ ÚDAJE: Proceedings of the Third International Afro-European Conference for Industrial Advancement - AECIA 2016. Cham: Springer, 2018 - (Abraham, A., Haqiq, A., Ella Hassanien, A., Snášel, V., Alimi, A.), s. 248-257. Advances in Intelligent Systems and Computing, 565. ISBN 978-3-319-60833-4. ISSN 2194-5357. [AECIA 2016. International Afro-European Conference for Industrial Advancement /3./. Marrakesh (MA), 21.11.2016-26.11.2016]. Podpora: GA13-34856S. ANOTACE: We first analyze some features of numerical weather predictions (NWP) for global solar radiation and notice that they are undersmooth. This finding opens a way to improvements via various smoothing strategies. Then we introduce a statistical modeling framework based on modern semiparametric regression. We use a numerical weather prediction (NWP) model output as one of the inputs for our statistical model. The statistical model is build on the modern regression formalism, utilizing nonparametric B-splines for nonlinear parts whose exact shape is unknown a priori (apart from physically motivated smoothness). Then we illustrate its abilities for systematic development of strategies for NWP calibration and further development. The results are useful both for practical forecasting and as a source of feedback for NWP modelers.

Název v anglickém jazyce

Statistical Modeling for Improvement of Numerical-Model-Based Solar Radiation Forecasts

Popis výsledku anglicky

ZÁKLADNÍ ÚDAJE: Proceedings of the Third International Afro-European Conference for Industrial Advancement - AECIA 2016. Cham: Springer, 2018 - (Abraham, A., Haqiq, A., Ella Hassanien, A., Snášel, V., Alimi, A.), s. 248-257. Advances in Intelligent Systems and Computing, 565. ISBN 978-3-319-60833-4. ISSN 2194-5357. [AECIA 2016. International Afro-European Conference for Industrial Advancement /3./. Marrakesh (MA), 21.11.2016-26.11.2016]. Podpora: GA13-34856S. ANOTACE: We first analyze some features of numerical weather predictions (NWP) for global solar radiation and notice that they are undersmooth. This finding opens a way to improvements via various smoothing strategies. Then we introduce a statistical modeling framework based on modern semiparametric regression. We use a numerical weather prediction (NWP) model output as one of the inputs for our statistical model. The statistical model is build on the modern regression formalism, utilizing nonparametric B-splines for nonlinear parts whose exact shape is unknown a priori (apart from physically motivated smoothness). Then we illustrate its abilities for systematic development of strategies for NWP calibration and further development. The results are useful both for practical forecasting and as a source of feedback for NWP modelers.

Druh

O - Ostatní výsledky

CEP obor

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OECD FORD obor

10103 - Statistics and probability

Projekt

<a href="/cs/project/GA13-34856S" target="_blank" >GA13-34856S: Pokročilé metody náhodných polí v asimilaci dat pro krátkodobou předpověď počasí</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů