Forecasting skills for a wind farm would significantly degrade if the complex wake effects of the upstream wind farms are excluded, especially when they are spatially close to each other. In this study, the Weather Research and Forecasting (WRF) model has been used to predict wind speed and power for a wind farm in Pakistan in the presence of wake interference from neighboring wind farms for two different seasons. Forecasting is done for two different cases i.e. without and with inter-farm wake effects, and different statistical error parameters were evaluated based on the real observations. A significant reduction in errors was observed in the latter case. For instance, the mean absolute errors in wind speed prediction were reduced by 7.7% and 14% in June (summer) and January (winter) respectively, by the inclusion of inter-farm wake effects. Similarly, an improved forecast of power output was obtained by incorporating the interaction of upstream wind farms i.e. a reduction of 15% and 26% in the normalized mean absolute error in power output values was observed for June and January, respectively. However, the prediction accuracy of power output substantially deteriorated in the winter season.

如果排除上游风电场的复杂尾流影响，尤其是当它们在空间上彼此靠近时，风电场的预测技能将大大降低。在这项研究中，天气研究和预报（WRF）模型已用于预测巴基斯坦风电场在两个不同季节受到相邻风电场的尾流干扰时的风速和功率。对两种不同的情况进行了预测，即没有和有农场间唤醒效应，并根据实际观察结果评估了不同的统计误差参数。在后一种情况下，观察到错误显着减少。例如，通过包含农场间的唤醒效应，风速预测中的平均绝对误差分别在6月（夏季）和1月（冬季）分别降低了7.7％和14％。同样，通过结合上游风电场的相互作用获得了更好的电力输出预测，即分别在6月和1月观察到了电力输出值的标准化平均绝对误差降低了15％和26％。但是，在冬季，功率输出的预测精度大大降低。