Downscaling and improving the wind forecasts from NWP for wind energy applications using support vector regression

The stochastic nature of wind poses challenges in the large scale integration of wind energy with the grid. Wind characteristics at a site may significantly vary with time, which will be reflected on the wind power production. Understanding and managing such variations could be challenging for wind farm owners, energy traders and grid operators. In this work, we propose models based on support vector regression (SVR) to downscale the speed and direction of wind at a specific site using both historical observed measurements and numerical weather predictions (NWP). Several meteorological variables, considered to have potential influence on the wind, were used in the feature selection for the models. The models are then optimally developed and used to predict the wind speed and direction at the site considered. In view of the two of Nord pool's energy markets namely the intraday and day ahead markets, approaches for short-term forecasts (t + 1 hours) and medium-term recursive forecasts (t + 36 hours) were developed. The proposed SVR models are found to be accurate and efficient in correcting the NWP information and predicting the wind speed and direction for the short-term forecasts. For medium-term forecasts, the developed models could outperform the NWP, especially for the wind speed predictions.