Multiscale prediction of wind speed and output power for the wind farm

This paper focuses on establishing the multiscale prediction models for wind speed and power in wind farm by the average wind speed collected from the history records. Each type of the models is built with different time scales and by different approaches. There are three types of them that a short-term model for a day ahead is based on the least squares support vector machine （LSSVM）, a medium-term model for a month ahead is on the combination of LSSVM and wavelet transform （WT）, and a long-term model for a year ahead is on the empirical mode decomposition （EMD） and recursive least square （RLS） approaches. The simulation studies show that the average value of the mean absolute percentage error （MAPE） is 4.91%, 6.57% and 16.25% for the short-term, the medium-term and the long-term prediction, respectively. The predicted data also can be used to calculate the predictive values of output power for the wind farm in different time scales, combined with the generator＇s power characteristic, meteorologic factors and unit efficiency under various operating conditions.