Correction Method of ShortTerm Wind Speed in Wind Farm Research Based on PCA and RBF Neural Network
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Abstract:
Wind speed is the most important input factor of wind power forecasting, and the accurate wind speed forecasting is the premise and foundation of wind power prediction. In order to improve the accuracy of shortterm wind speed forecasting, the WRF model is used to predict the wind speed of a wind farm along the east coasts of China. Besides, the WRF model forecasted wind direction, air temperature, barometric pressure and other meteorological factors are combined by the PCARBF algorithm to further correct the forecasting wind speed. The results show that, after the correction of the PCARBF algorithm for the wind speed forecasting of the WRF model, the error of wind speed forecasting becomes smaller, and the relative root mean square error is reduced by 20% to 30%, and the relative mean absolute error is decreased by 15% to 20%. The PCARBF algorithm is qualified with better correction for the wind speed of WRF model forecasting compared with other intelligent algorithms (BP algorithm, LSSVM algorithm), and improves the accuracy of wind speed forecasting effectively.
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Supported by:Beijing E-Tiller Technology Development Co., Ltd.
Supported by:Beijing E-Tiller Technology Development Co., Ltd.