Forecasting wind speed using support vector regression and feature selection

Since the wind power generated by a wind farm is entirely dependent on meteorological conditions (such as wind speed, wind direction, humidity etc.), accurately forecasting wind speed based on these conditions over a 1 to 24 hour time horizon is crucial to predict the potential short term energy supply of a wind farm. These short term predictions in turn are crucial in assisting with wind farm planning so that the required base load provided to the electricity grid is always guaranteed (even in the presence of highly variable wind power outputs). In this work we show that the relative prediction performance of a short-term Support Vector Regression (SVR) wind forecasting system can be improved by up to 11.12% by systematically selecting and combining relevant input features that influence short term wind speed. We illustrate our results on meteorological data collected in Alexander Bay, South Africa over a three year period from 2011-2013.