Wind energy is one of the most promising resources of energy for the future. Wind is generally regarded as the most renewable and green energy type. The reason for this perception is mainly because of wind’s inexhaustible, sustainable and abundant characteristics. Recent years has witnessed a significant increase in wind energy investments. Wind speed forecasting is considered as the most important area of research with regard to better investment and planning decisions. In this study; support vector regression and multi-variable grey model with parameter optimization are applied to the wind speed forecasting problem. The main objective of this study is to reveal the possible usage and compare the performances of support vector regression against grey theory based forecasting. The performances of the selected algorithms are benchmarked on a sample dataset. The data was obtained from Cukurova region of Turkey. Experimental results indicate that multivariable grey model with parameter optimization outperforms support vector regression in terms of forecast accuracy.