Clustering Weather Situations with Respect to Prediction of Solar Irradiance by Multiple NWP Models

0434056 - ÚI 2015 RIV NL eng C - Konferenční příspěvek (zahraniční konf.)
Krč, Pavel - Eben, Kryštof - Pelikán, Emil
Clustering Weather Situations with Respect to Prediction of Solar Irradiance by Multiple NWP Models.
ECAI 2014. Amsterdam: IOS Press, 2014 - (Schaub, T.; Friedrich, G.; O’Sullivan, B.), s. 1207-1208. Frontiers in Artificial Intelligence and Applications, 263. ISBN 978-1-61499-418-3. ISSN 0922-6389.
[ECAI 2014. European Conference on Artificial Intelligence /21./. Prague (CZ), 18.08.2014-22.08.2014]
Institucionální podpora: RVO:67985807
Klíčová slova: regression tree * numerical weather prediction * global solar radiation
Kód oboru RIV: IN - Informatika

With the photovoltaic (PV) and concentrating solar power (CSP) forming a growing portion of European power sources, there is a strong demand for a reliable prediction of solar power. Such predictions are mostly provided by physical or statistical models, both of which rely on accurate forecast of solar irradiance. For the short-to-medium-term forecast horizon (hours to days), irradiance forecast is provided mostly by numerical weather prediction (NWP) models. However, in spite of a recent effort to improve irradiance prediction within current NWP models, its quality is still not satisfactory and it is responsible for a majority of uncertainty in photovoltaic power forecasting. A promising method of improving NWP solar irradiance prediction is multi-model approach. This paper presents preliminary results from a data mining approach to combining irradiance forecasts from multiple NWP models.
Trvalý link: http://hdl.handle.net/11104/0238189