0477859 - ÚI 2018 RIV CH eng C - Conference Paper (international conference)
Brabec, Marek - Krč, Pavel - Eben, Kryštof - Pelikán, Emil
Wind Speed Forecasting for a Large-Scale Measurement Network and Numerical Weather Modeling.
Advances in Time Series Analysis and Forecasting. Cham: Springer, 2017 - (Rojas, I.; Pomares, H.; Valenzuela, O.), s. 361-373. Contributions to Statistics. ISBN 978-3-319-55788-5. ISSN 1431-1968.
[ITISE 2016. International Work-Conference on Time Series. Granada (ES), 27.06.2016-29.06.2016]
R&D Projects: GA ČR GA13-34856S
Institutional support: RVO:67985807
Keywords : semiparametric modeling * GAM * wind speed forecasting * numerical weather prediction mode * measurement network
OECD category: Statistics and probability
DOI: https://doi.org/10.1007/978-3-319-55789-2_25

We investigate various problems encountered when forecasting wind speeds for a network of measurements stations using outputs of numerical weather prediction (NWP) model as one of the predictors in a statistical forecasting model. First, it is interesting to analyze prediction error properties for different station types (professional and amateur). Secondly, the statistical model can be viewed as a calibration of the original NWP model. Hence, careful semi-parametric smoothing of NWP input can discover various weak points of the NWP, and at the same time, it improves forecasting performance. It turns out that useful information is contained not only in the latest prediction available. It is beneficial to combine different horizon NWP predictions to one target time. GARCH sub-model for the residuals then shows complicated structure usable for short-term forecasts.

Permanent Link: http://hdl.handle.net/11104/0274077