Wind energy is a rapidly growing industry in Turkey. Wind power potential studies revealed that the most promising region for electricity generation is the western part of Turkey. Wind speed forecasting is necessary for power systems because of the intermittent nature of wind. Thus, accurate forecasting of wind power is recognized as a major contribution to reliable wind power integration. This paper assesses the performance of the weather research forecasting (WRF) model for wind speed and wind direction predictions up to 72 h ahead. The wind speeds and wind directions are evaluated based on the mean absolute error (MAE). Evaluations were also performed seasonally. Moreover, in order to improve the WRF simulations, a multi-input–single output artificial neural network (ANN) approach is applied to both wind speeds of the WRF model and wind power estimates, which are estimated from the wind speeds of the WRF model by using a power curve for the Soma wind power plant. Traditional error metrics were used for validations using wind tower mast data installed nearby the wind farm. The results from up to 72 h forecast horizon show that the WRF model slightly overpredicts the wind speeds. Wind speed predictions by the WRF model are found highly depending on the season, location, and wind direction. The model is also able to reproduce wind directions except for low wind speeds. Large MAEs are found for the winds less than 5 m/s. The performance of the WRF model for wind power prediction decreases with the increasing runtime. Root mean square error and normalized root mean square error (nRMSE) in wind powers range in between 123–261 kW and 13%–32% without performing the ANN approach, respectively. The improvement of the ANN depends on the forecast horizon, season, and location of turbine groups, as well as its application on either the wind speed outputs of the WRF model or wind power estimations. The ANN significantly improves the WRF at large forecast horizons for wind power estimations, for which it gives better results in the summer and reaches 29% improvement for summer on average for nRMSE. On the other hand, ANN adjusts the wind speed outputs of the model better than that of wind power estimations. For instance, the nRMSE is approximately 13% for 24 h winter wind speed simulations of the WRF for the turbine groups G1 and G4, after ANN adjustment. The ANN improves the results better for turbine group 1, because of less complexity of this group in the direction of prevailing wind. The evaluation of the ANN suggests that the approach can be used for improving the performance of the wind power forecast for this power plant.

中文翻译：

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使用WRF模型的土耳其西部某地区短期风能资源预测

风能是土耳其快速发展的产业。风力发电潜力研究表明，最有前途的发电地区是土耳其西部。由于风的间歇性，风速预测对于电力系统是必要的。因此，对风电的准确预测被认为是对可靠的风电集成的主要贡献。本文评估了天气预报研究（WRF）模型对未来72小时之内的风速和风向预测的性能。根据平均绝对误差（MAE）评估风速和风向。还按季节进行评估。此外，为了改善WRF仿真，多输入-单输出人工神经网络（ANN）方法应用于WRF模型的风速和风能估计，这是通过使用Soma风能的功率曲线从WRF模型的风速来估计的植物。使用安装在风电场附近的风塔桅杆数据，将传统的错误度量标准用于验证。长达72小时的预测范围的结果表明，WRF模型稍微高估了风速。通过WRF模型预测的风速很大程度上取决于季节，位置和风向。除低风速外，该模型还可以重现风向。发现风速小于5 m / s的大型MAE。WRF模型用于风电功率预测的性能会随着运行时间的增加而降低。在不执行ANN方法的情况下，风能的均方根误差和归一化均方根误差（nRMSE）分别在123-261 kW和13％-32％之间。人工神经网络的改进取决于涡轮机组的预测水平，季节和位置，以及其在WRF模型的风速输出或风能估计中的应用。人工神经网络在风电估计的较大预测范围内显着改善了WRF，因此在夏季可获得更好的结果，而nRMSE在夏季平均可提高29％。另一方面，人工神经网络比风能估计更好地调整了模型的风速输出。例如，经过ANN调整后，对于G1和G4汽轮机组WRF的24小时冬季风速模拟，nRMSE约为13％。ANN可以更好地改善1组涡轮机的结果，因为该组在盛行风向上的复杂性较低。ANN的评估表明，该方法可用于改善该电厂的风电预测性能。