This paper examines and compares regression and artificial neural network models used for the estimation of wind turbine power curves. First, characteristics of wind turbine power generation are investigated. Then, models for turbine power curve estimation using both regression and neural network methods are presented and compared. The parameter estimates for the regression model and training of the neural network are completed with the wind farm data, and the performances of the two models are studied. The regression model is shown to be function dependent, and the neural network model obtains its power curve estimation through learning. The neural network model is found to possess better performance than the regression model for turbine power curve estimation under complicated influence factors.
Comparative Analysis of Regression and Artificial Neural Network Models for Wind Turbine Power Curve Estimation
Contributed by the Solar Energy Division of THE AMERICAN SOCIETY OF MECHANICAL ENGINEERS for publication in the ASME JOURNAL OF SOLAR ENERGY ENGINEERING. Manuscript received by the ASME Solar Energy Division, January, 2001; final revision, July, 2001. Associate Editor: D. Berg.
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Li, S., Wunsch, D. C., O’Hair , E., and Giesselmann, M. G. (July 1, 2001). "Comparative Analysis of Regression and Artificial Neural Network Models for Wind Turbine Power Curve Estimation ." ASME. J. Sol. Energy Eng. November 2001; 123(4): 327–332. https://doi.org/10.1115/1.1413216
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