The wind has emerged as one of the fastest growing and most important alternative energy sources during the past decade. However, the most serious problem being faced by human beings in wind applications is the dependence on the volatility of the wind. Thus, wind forecasting techniques in general and short term wind prediction methods in special are very helpful for wind applications. Albeit various methods including the artificial neural networks, fuzzy logic, evolutionary computation approaches have been developed for short term predictions, very few scholars tried to introduce the Grey forecasting method, which was designed for the system analysis being characterized by inadequate information. Since the short term wind prediction problem is characterized by the nature of no regularity, difficult prediction, and huge influences by terrain and obstacle. It can be a suitable problem to be resolved by the Grey forecasting method. Thus, the authors developed a GM (1,1) Grey system based pilot study for future short term wind power forecasting. The accurate predictions of wind speed by using the GM(1,1) Grey forecasting method can serve as the basis for introducing the wind power as an efficient source of alternative energies. An empirical study based on the real data being measured from a wind farm being located in Penghu, Taiwan has demonstrated the efficiency of the GM(1,1) based forecast mechanism. In the future, the GM(1,1) based forecast mechanism can further be applied to the wind forecast problems based on various time spans.