Zoeken
Zoeken kan via de modus 'eenvoudig zoeken' (één veld) of uitgebreid via 'geavanceerd zoeken' (meerdere velden). Zo kan je bv. zoeken op een combinatie van een auteursnaam (auteur), een jaartal (jaar) en een documenttype.
Boekenmand
Nuttige resultaten kan je aanvinken en toevoegen aan een mandje. De inhoud hiervan kan je exporteren of afdrukken (naar bv. PDF).
RSS
Op de hoogte blijven van nieuw toegevoegde publicaties binnen uw interessegebied? Dit kan door een RSS-feed (?) te maken van jouw zoekopdracht.
nieuwe zoekopdracht
Improving wind and power predictions via four-dimensional data assimilation in the WRF model: case study of storms in February 2022 at Belgian offshore wind farms
Ivanova, T.; Porchetta, S.; Buckingham, S.; Glabeke, G.; van Beeck, J.; Munters, W. (2025). Improving wind and power predictions via four-dimensional data assimilation in the WRF model: case study of storms in February 2022 at Belgian offshore wind farms. Wind Energy Science 10(1): 245-268. https://dx.doi.org/10.5194/wes-10-245-2025
In: Wind Energy Science. Copernicus Publications: Germany. ISSN 2366-7443; e-ISSN 2366-7451
|   |
| Auteurs | | Top |
- Ivanova, T.
- Porchetta, S.
- Buckingham, S.
|
- Glabeke, G.
- van Beeck, J.
- Munters, W.
|
|
| Abstract |
Accurate modeling of wind conditions is vital for the efficient operation and management of wind farms. This study investigates the enhancement of weather simulations by assimilating local offshore light detection and ranging (lidar) and/or supervisory control and data acquisition (SCADA) data into a numerical weather prediction model while considering the presence of neighboring wind farms through wind farm parameterization. We focus on improving model output during storms impacting the Belgian–Dutch wind farm cluster located in the Southern Bight of the North Sea via the four-dimensional data assimilation (nudging) technique in the Weather Research and Forecasting (WRF) model. Our findings indicate that assimilating wind observations significantly reduces the relative root-mean-square error for wind speed at a wind farm located 47 km downwind from the offshore lidar platform. This leads to more accurate power production outputs. Specifically, at wind turbines experiencing wake effects, the wind speed error decreased from 10.5 % to 5.2 %, and the wind direction error was reduced by a factor of 2.4. A proposed artificial configuration, leveraging the upwind lidar measurements, showcases the potential for improving hour-ahead wind and power predictions. Moreover, we perform a thorough study to investigate the sensitivity to nudging parameters during versatile atmospheric conditions, which helps to identify the best assimilation practices for this offshore setting. These insights are expected to refine wind resource mapping and reanalysis of weather events, as well as motivate more measurement campaigns offshore. |
IMIS is ontwikkeld en wordt gehost door het VLIZ.
