Accurate forecasting of wind power production is essential for power scheduling purposes and is thus a key part of the economic viability of the integration of wind power. Mesoscale weather models such as the Weather Research and Forecasting model (WRF) are increasingly being looked to as a way to improve forecasts from global models as input to operational wind power prediction models.
The goal of the study is to study to what extent forecasts from global weather models can be improved by downscaling with WRF, and what impact some key model settings have on these forecasts, with an emphasis on their usability as input for wind power prediction models. The model performance is evaluated mainly from the perspective of increasing correlation, since this has been identified in previous research as being the most relevant for inputs to statistical power prediction models such as WPPT.
Data from several tall met masts in South Africa will be used to evaluate model output for a region in South Africa with varying terrain and vegetation characteristics. Emphasis is put
on wind speed and wind direction close to hub height as these are the most relevant parameters for wind power forecasting. South Africa is chosen as a test region since it offers a unique measurement data set consisting of wind measurements up to a height of 60 m a.g.l. at 19 different sites in varying terrain types and complexities. This dataset is the result of a measurement campaign orchestrated by DTU, who have further done extensive wind atlas studies in parallel which might be of use to the thesis project. The results are not meant to be specific to South Africa however.
The study is part of a masters thesis project undertaken by a student in the European Wind Energy Master (EWEM). EWEM is a joint degree program organized by a consortium of four european universities leading in the field of wind energy research. This thesis is supervised by faculty members of DTU in Copenhagen and the University of Oldenburg, Germany.