利用某一风电场区域内33台风机观测资料以及欧洲中期天气预报中心（ECMWF）的100 m高度风速预报产品,引进最优训练期方案,通过一元线性回归方法先对模式的100 m高度风速预报进行订正,再分别采用完全预报方法（PP法）和模式输出统计预报方法（MOS法）进行风电功率预报的对比研究。结果表明：ECMWF的100 m高度风速预报值与风机轮毂高度处风速观测值之间的偏差并不大,订正后的预报误差进一步减小。将订正后的风速预报代入PP法和MOS法建立的风电功率预报方程,可以显著减小PP法的预报误差,但是并没有改进MOS法的预报结果。与利用风速预报订正产品进行风电功率预报的PP法相比,MOS法可以省去风速预报的订正环节而略胜一筹,简化了业务流程。这2种方法计算的33台风机站3~72 h预报的均方根误差和平均绝对误差分别在17%~25%和11%~18%之间,具有业务应用价值。

On the basis of data sets of wind speed and wind power observed from 33 wind turbines in a wind farm in Shaanxi Province, China and with wind speeds at a height of 100 m forecast by the ECMWF model, the wind speed prediction was corrected using simple linear regression and introducing the optimal training period and forecast models for wind power business were comparatively studied using the perfect prognostic (PP) and model output statistics (MOS) methods. The optimal training period was defined as a sliding cycle, which should be given before the forecast date. For training days varying from 10 to 100 d, taking some past forecast days as an evaluation period to calculate the root-mean-square error (RMSE) of the forecast, the optimal training period was decided according to the variation of the RMSE of the wind speed or wind power forecast with the training days. A prediction equation of wind power was established using the PP method according to historical observations of wind speed and wind power in the training sample. Then, the numerical prediction product of wind speed was used to replace the observation data in the prediction equation, which was applied to a wind power prediction business. In contrast, the MOS method directly used the wind speed of the numerical weather prediction model to establish the prediction equation, which was quite different from the PP method. The result showed that wind speed predictions after correction and wind power were improved during the optimal training period, reducing the prediction error to the greatest extent. The optimal training days were determined by the RMSE of the forecast during the evaluation period of 10 d before the forecast date. The wind speed at a height of 100 m forecast by the ECMWF model was closer to that of the observation at the height of the fan hub, and the RMSE and average absolute error (ABE) respectively ranged from 2.5 to 4.1 m·s^-1 and 1.9 to 3.3 m·s^-1. The prediction error of the wind speed after correction became smaller, with the percentage of improvement in the RMSE of the wind speed forecast ranging from 33% to 46%, and the RMSE and ABE of the wind speed prediction respectively ranged from 1.5 to 2.4 m·s^-1 and 1.1 to 1.9 m·s^-1. Thus, wind speed predictions could be applied to wind power prediction businesses using both the PP and MOS methods. Although the wind power prediction error of the MOS method was nearly the same as that of the PP method when the wind speed prediction was corrected, the MOS method, which did not need to improve the quality of wind power prediction by correcting the wind speed prediction, was slightly better than the PP method in terms of complexity. The RMSE and ABE of the wind power forecasts of both the PP and MOS methods were 17%-25% and 11%-18%, respectively, within the forecast time of 3-72 h. Thus, both the PP and MOS methods could be applied to wind power prediction businesses.