基于灰色－辨识模型的风电功率短期预测

为了准确预测风电机组的输出功率，针对实际风场，给出一种基于灰色GM(1,1)模型和辨识模型的风电功率预测建模方法，采用残差修正的方法对风速进行预测，得出准确的风速预测序列。同时为了提高风电功率预测的精度，引入FIR-MA迭代辨识模型，从分段函数的角度对风电场实际风速-风电功率曲线进行拟合，取得合适的FIR-MA模型。利用该模型对额定容量为850 kW的风电机组进行建模,采用平均绝对误差和均方根误差，以及单点误差作为评价指标，与风电场的实测数据进行比较分析。仿真结果表明，基于灰色-辨识模型的风电机组输出功率预测方法是有效和实用的，该模型能够很好地预测风电机组的实时输出功率，从而提高风电场输出功率预测的精确性。

Grey-identification model based wind power generation short-term prediction

To predict the output power of wind turbine accurately, based on the GM (1, 1) model and the identification method, a wind power generation short-term prediction method is presented for the real wind farm. The revision of residual error is applied to forecast the wind speed and get the accurate predicted wind speed series. Then, in order to increase the prediction precision of wind power, the FIR-MA iterative identification model is adopted to fit the real relationship between sequential wind speed and wind power and get the proper FIR-MA model. By modeling the wind turbine whose rated capacity is 850 kW, this paper compares the predicted wind generation power with the observed data using mean absolute percentage error, root mean square error and single point error as its evaluation indexes. The simulation shows the effectiveness and the practical applicability of the presented method, which can predict the real time generation power of wind turbineness and raise the accuracy of the wind power prediction. Finally, the simulation using the actual data from wind farm in China proves the efficiency of the proposed grey-identification model.