State of the art wind forecasting models, like Numerical Weather Predictions utilize huge amounts of computing time. Some of them have rather low spatio-temporal resolution. Time series prediction model accomplish good results in high temporal settings. Moreover, their consumption of computing capacities is relatively low and return accurate short-term to medium-term forecasts. The recent literature shows increasing interest in the topic of spatial interdependence. This article deals with a spatial and temporal model for wind speed. We describe the temporal model structure independently on spatial correlations. Therefore, seasonality and a huge correlation structure are included. Subsequently, the model is extended and a spatial structure is included. The data set includes ten minute observations of several measurement stations in Eastern Germany. The validation procedure shows that the model is reliable.
(2015). Spatio-temporal wind speed predictions for Germany [conference presentation - intervento a convegno]. Retrieved from http://hdl.handle.net/10446/48747
Spatio-temporal wind speed predictions for Germany
2015-01-01
Abstract
State of the art wind forecasting models, like Numerical Weather Predictions utilize huge amounts of computing time. Some of them have rather low spatio-temporal resolution. Time series prediction model accomplish good results in high temporal settings. Moreover, their consumption of computing capacities is relatively low and return accurate short-term to medium-term forecasts. The recent literature shows increasing interest in the topic of spatial interdependence. This article deals with a spatial and temporal model for wind speed. We describe the temporal model structure independently on spatial correlations. Therefore, seasonality and a huge correlation structure are included. Subsequently, the model is extended and a spatial structure is included. The data set includes ten minute observations of several measurement stations in Eastern Germany. The validation procedure shows that the model is reliable.File | Dimensione del file | Formato | |
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