In wind integration studies, accurate representations of the wind power output from potential wind power plants and corresponding representations of wind power forecasts are needed, and typically used in a production cost simulation. Two methods for generating "synthetic" wind power forecasts that capture the statistical trends and characteristics found in commercial forecasting techniques are presented. These two methods are based on auto-regressive moving average (ARMA) models and the Markov random walk method. Statistical criteria are suggested for evaluation of wind power forecast performance, and both synthetic forecast methods proposed are evaluated quantitatively and qualitatively. The forecast performance is then compared with a commercial forecast used for an operational wind power plant in the Northwestern United States evaluated using the same statistical performance measures. These quantitative evaluation parameters are monitored during specific months of the year, during rapid ramping events, and at all times. The best ARMA based models failed to replicate the auto-regressive decay of forecast errors associated with commercial forecasts. A modification to the Markov method, consisting of adding a dimension to the state transition array, allowed the forecast time series to depend on multiple inputs. This improvement lowered the artificial variability in the original time series. The overall performance of this method was better than for the ARMA based models, and provides a suitable technique for use in creating a synthetic wind forecast for a wind integration study.