Posters

Abstract

In the current wind farm energy yield assessment, past wind data is commonly employed to forecast future wind resource conditions. However, this approach neglects the potential impact of climate change on wind patterns. To address this limitation, our study introduces the cumulative distribution function transform (CDF-t) downscaling method to downscale CMIP6 data and generate long-term corrected data at a monthly level. By establishing the relationship between monthly wind speed and wind farm production, we can calculate the energy yield during the projected lifetime. Furthermore, we present the spread of energy across different climate models in different scenarios to provide a comprehensive understanding of uncertainties.