Support Vector Regression of multiple predictive models of downward short-wave radiation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86092658" target="_blank" >RIV/61989100:27240/14:86092658 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/67985807:_____/14:00429748

Výsledek na webu

<a href="http://dx.doi.org/10.1109/IJCNN.2014.6889812" target="_blank" >http://dx.doi.org/10.1109/IJCNN.2014.6889812</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/IJCNN.2014.6889812" target="_blank" >10.1109/IJCNN.2014.6889812</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Support Vector Regression of multiple predictive models of downward short-wave radiation

Popis výsledku v původním jazyce

Accurate forecasts of weather conditions are of the utmost importance for the management and operation of renewable energy sources with intermittent (stochastic) production. With the growing amount of intermittent energy sources, the need for precise weather predictions increases. Production of energy from renewable power sources, such as wind and solar, can be predicted using numerical weather prediction models. These models can provide high-resolution, localized forecast of wind speed and solar irradiation. However, different instances of numerical weather prediction models may provide different forecasts, depending on their properties and parameterizations. To alleviate this problem, it is possible to employ multiple models and to combine their outputs to obtain more accurate localized forecasts. This work uses the machine-learning tool of Support Vector Regression to amalgamate downward short-wave radiation forecasts of several numerical weather prediction models. Results of SVR-ba

Název v anglickém jazyce

Support Vector Regression of multiple predictive models of downward short-wave radiation

Popis výsledku anglicky

Accurate forecasts of weather conditions are of the utmost importance for the management and operation of renewable energy sources with intermittent (stochastic) production. With the growing amount of intermittent energy sources, the need for precise weather predictions increases. Production of energy from renewable power sources, such as wind and solar, can be predicted using numerical weather prediction models. These models can provide high-resolution, localized forecast of wind speed and solar irradiation. However, different instances of numerical weather prediction models may provide different forecasts, depending on their properties and parameterizations. To alleviate this problem, it is possible to employ multiple models and to combine their outputs to obtain more accurate localized forecasts. This work uses the machine-learning tool of Support Vector Regression to amalgamate downward short-wave radiation forecasts of several numerical weather prediction models. Results of SVR-ba

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

<a href="/cs/project/LD12009" target="_blank" >LD12009: Pokročilé metody pro predikci výroby elektrické energie z fotovoltaických systémů s využitím numerických modelů počasí s vysokým prostorovým i časovým rozlišením</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the International Joint Conference on Neural Networks

ISBN

978-1-4799-1484-5

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

651-657

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

New York

Místo konání akce

Beijing

Datum konání akce

6. 7. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—