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2020, Volume 14, Issue 2

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Frontiers in Energy >> 2020, Volume 14, Issue 2 doi: 10.1007/s11708-018-0553-3

Comparison of modeling methods for wind power prediction: a critical study

. Department of Mechanical Engineering, National Institute of Technology Karnataka, Surathkal 575025, India.. Department of Mechanical Engineering, NMAMIT, Nitte, KarkalaTaluk 574110, India

Accepted: 2018-04-12 Available online: 2018-04-12
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Abstract

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.

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