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| Received:December 01, 2009Revised:September 28, 2011 |
| 基金项目:This work was partly supported by the National Natural Science Foundation of China (No. 50967001), and the project for returned talents after studying abroad. |
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| Multiscale prediction of wind speed and output power for the wind farm |
| Xiaolan WANG,Hui LI |
| (School of Electrical and Information Engineering, Lanzhou University of Technology; Key Laboratory of Gansu Advanced Control for Industrial Processes) |
| Abstract: |
| 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. |
| Key words: Multiscale prediction Wind power Least square support vector machine Wavelet transform Empirical mode decomposition Recursive least square |
