Predicting the Wind

TL;DR: In this paper, three measures are taken as best practices to reduce prediction errors: Combinations of different models can be done with power output forecast models as well as with numerical weather prediction models (multimodel and multischeme approaches).

Abstract: Due to increasing wind power penetration, the need for and usage of wind power prediction systems have increased. At the same time, much research has been done in this field, which has led to a significant increase in the prediction accuracy recently. With many ongoing research programs in the field of numerical weather prediction (NWP), as well as in the power output prediction models (transforming wind speed into electrical power output), one can expect further improvements in the future. For the time being, three measures are taken as best practices to reduce prediction errors: Combinations of different models can be done with power output forecast models as well as with NWP models (multimodel and multischeme approaches). Reductions in RMSE of up to 20% were shown with intelligent combinations. As expected, a shorter forecast horizon leads to lower prediction errors. However, the organization of the electricity market as well as the conventional generation pool has a large influence on the needed forecast horizon. The forecast error depends on the number of wind turbines and wind farms and their geographical spread. In Germany, typical forecast errors for representative wind farm forecasts are 10-15% RMSE of installed power, while the error for the control areas calculated from these representative wind farms is typically 6-7% and that for the whole of Germany only 5-6%. Whenever possible, aggregating wind power over a large area should be performed as it leads to significant reduction of forecast errors as well as short-term fluctuations. a large area should be performed as it leads to significant reduction of forecast errors as well as short-term fluctuations.