The increased integration of wind power into the electric grid poses new challenges due to its fluctuation and volatility. Short term wind power forecasting is one of the most effective ways to deal with it. Various individual non-linear models are proposed to meet the data requirement to forecast short term wind power. However, as every model has its advantage and weakness, when these models are applied to different wind farms, the forecasting accuracy of every model varies because of distinct data character. This paper analyzes individual forecast models like Wavelet Transform and Support Vector Machine (SVM), and then puts forward a complex-valued forecasting model which is based on Artificial Natural Network in accordance with forecasting data provided by National Climatic Data Center in U.S. The existing individual models are matched and trained according to certain means by Natural Network to propose a multistage model. For variable data from different wind farms, the model can adjust and optimize portion of individual models. Compared with each single model, the multistage model has more robust adaptation and faster calculation speed, which can improve the forecasting precision and have more engineering value.

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