Prediction of power generation of a wind turbine is crucial, which calls for accurate and reliable models. In this work, six different models have been developed based on wind power equation, concept of power curve, response surface methodology (RSM) and artificial neural network (ANN), and the results have been compared. To develop the models based on the concept of power curve, the manufacturer’s power curve, and to develop RSM as well as ANN models, the data collected from supervisory control and data acquisition (SCADA) of a 1.5 MW turbine have been used. In addition to wind speed, the air density, blade pitch angle, rotor speed and wind direction have been considered as input variables for RSM and ANN models. Proper selection of input variables and capability of ANN to map input-output relationships have resulted in an accurate model for wind power prediction in comparison to other methods.

中文翻译：

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风电预测建模方法的比较：一项重要研究

风力涡轮机发电量的预测至关重要，这需要精确而可靠的模型。在这项工作中，基于风力方程，功率曲线的概念，响应面方法（RSM）和人工神经网络（ANN）开发了六个不同的模型，并对结果进行了比较。为了基于功率曲线，制造商的功率曲线的概念来开发模型，以及为了开发RSM和ANN模型，已使用从1.5 MW涡轮机的监督控制和数据采集（SCADA）收集的数据。除了风速之外，空气密度，叶片桨距角，转子速度和风向也被视为RSM和ANN模型的输入变量。