Short-term wind power prediction based on statistical clustering

Since observations would affect the precision of predication in wind power models, it is necessary to process the history data prior to modeling. Data classification is automatically accomplished according to the statistical clustering approach. With the criterion of maximal similarity, we filtered a group of data as the samples used in modeling. Then we established the prediction model of wind speed based on ARIMA process. Compared with the conventional ARIMA process, the prediction using statistical clustering approach we proposed is more accurate. An example is used to verify the feasibility of our assumption. Finally, with power-speed curve of wind turbine, anticipated wind power can be easily obtained with the curve of power versus speed, which offers valuable reference for drawing out operation schedule of power system integrated with wind power.