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

Result code in 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>
Alternative codes found
RIV/67985807:_____/14:00429748
Result on the web
<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>

Result language
angličtina
Original language name
Support Vector Regression of multiple predictive models of downward short-wave radiation
Original language description
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
Czech name
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Czech description
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Project
<a href="/en/project/LD12009" target="_blank" >LD12009: Advanced methods for energy production forecasting by photovoltaic systems using high resolution NWP models</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the International Joint Conference on Neural Networks
ISBN
978-1-4799-1484-5
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
651-657
Publisher name
Institute of Electrical and Electronics Engineers
Place of publication
New York
Event location
Beijing
Event date
Jul 6, 2014
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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