基于虚拟预测与小波包变换的风电功率组合预测

为了提高风电功率的预测精度,针对风机功率不稳定性和非线性强的特点,使用小波包变换将风机出力分解成多个频率的子序列,然后运用组合预测方法分别对各子序列进行提前24 h预测,叠加各子序列的预测值,得出实际预测结果。其中组合预测方法权系数是通过虚拟预测的方法由方差倒数法确定。选择广东某风电场实测数据作为案例,对连续7天风电功率进行了预测。结果表明:小波包变换能有效把握风电功率变化规律,对小波包变换后的各子序列的预测结果表明组合预测效果优于单一预测方法。

Wind power combination forecasting based on wavelet packet transform and virtual forecasting method

Considering the instability and strong nonlinearity of wind power, in order to improve the accuracy of wind power forecasting, the original wind power sequence is decomposed into a series of sub-sequences. Then each sequence is forecasted 24 h ahead by combination forecasting model. And the weight coefficients of each sequence are identified using variance reciprocal method through a virtual forecasting method. Consequently, all the subsequence forecasting outputs are superposed to obtain the final forecasted results. At the end, a wind farm in Guangdong is chosen to validate the feasibility of the proposed model. Through seven consecutive days of forecasting of the wind power, the results indicate that the wavelet packet transform can grasp the variation law of wind power effectively, and the combination forecasting method can obtain a better forecasting result than single method.