1 citations · 1 across the 3 of their papers we have counts for
3 papers
stat.AP2024
Climate data selection for multi-decadal wind power forecasts
Sofia Morelli, Nina Effenberger, Luca Schmidt +1
Reliable wind speed data is crucial for applications such as estimating local (future) wind power. Global Climate Models (GCMs) and Regional Climate Models (RCMs) provide forecasts…
stat.AP2024★ 1 cited
Wind Power Assessment based on Super-Resolution and Downscaling -- A Comparison of Deep Learning Methods
Luca Schmidt, Nicole Ludwig
The efficient placement of wind turbines relies on accurate local wind speed forecasts. Climate projections provide valuable insight into long-term wind speed conditions, yet their…
stat.AP2023
Mind the (spectral) gap: How the temporal resolution of wind data affects multi-decadal wind power forecasts
Nina Effenberger, Nicole Ludwig, Rachel H. White
To forecast wind power generation in the scale of years to decades, outputs from climate models are often used. However, one major limitation of the data projected by these models…