The AMS Committees on Artificial Intelligence Applications to Environmental Science, Energy, and Probability and Statistics have organized a Wind Power Prediction Contest. The goal of this year's contest is to encourage students and professionals alike to apply their statistical learning techniques to advance the state of wind power forecasting. This contest focuses on both a day-ahead and a short-term (1, 2, and 3-hr) wind power forecast. Contestants were asked to use any computational intelligence or statistical learning technique, or combination of techniques, to make predictions of total farm power for a wind farm consisting of 53 turbines. Those forecasts are based on datasets of numerical weather prediction model forecasts of several meteorological quantities, as well as turbine level wind and power observations. The data are described in the talk. Prizes are awarded to the contestant or contestants who make the most accurate power predictions in terms of root mean square error (RMSE). This paper will give an overview of both contests as a prelude to the remaining session talks by selected contestants presenting their methods and results.