Shetty, R.P.Sathyabhama, A.Pai, P.S.2026-02-052020Frontiers in Energy, 2020, 14, 2, pp. 347-35820951701https://doi.org/10.1007/s11708-018-0553-3https://idr.nitk.ac.in/handle/123456789/23894Prediction 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. © 2018, Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature.Data acquisitionInterpolationLeast squares approximationsSurface propertiesWeather forecastingWind powerWind speedWind turbinesAccurate modelingArtificial neural networkArtificial neural network modelingComparison of modelsCubic-spline interpolationMethod of least squaresModel methodPower curvesResponse-surface methodologyWind power predictionsNeural networks