This document summarizes a study on short-term wind power forecasting for a wind farm in complex terrain in China. The study combines micro-scale computational fluid dynamics modeling with artificial neural networks to minimize forecast errors. Testing was performed from March 2012 to November 2012 with forecasts made every 15 minutes up to 46 hours ahead. Results showed the combined approach reduced mean absolute error by 5% and bias by 42% compared to using just the physical modeling alone.