Comparative Analysis of Regression and Artificial Neural Network Models for Wind Turbine Power Curve Estimation

[+] Author and Article Information
Shuhui Li

Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville TX 78363e-mail shuhui.li@tamuk.edu

Donald C. Wunsch

Department of Electrical and Computer Engineering, University of Missouri-Rolla, Rolla MO 65409

Edgar O’Hair, Michael G. Giesselmann

Department of Electrical Engineering, Texas Tech University, Lubbock TX 79409

J. Sol. Energy Eng 123(4), 327-332 (Jul 01, 2001) (6 pages) doi:10.1115/1.1413216 History: Received January 01, 2001; Revised July 01, 2001
Copyright © 2001 by ASME
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Central and South West renewable project small wind farm, Fort Davis, Texas
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Wind power generation vs. wind speed (turbine No. 6, March 1996)
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Wind rose diagram on Fort Davis wind farm (Roxal, 40m level, 5/1/94 to 4/30/95). It shows probability distribution of wind directions in a circle
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Structure of the multilayered perceptron network with single hidden layer (Node 0 is bias.)
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Using wind speed of met. 18 to estimate that of met. 19 (March 1996)
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Using wind speed of met. 18 to estimate that of met. 19 (April 1996) (shows the scatter plot of the measured wind speeds, the prediction by a linear regression model, and the prediction by neural network model)
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Wind turbine power prediction by polynomial regression models for test data (turbine No. 6, April 1996)
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Comparison of wind turbine power prediction by 3rd order regression and neural network models for test data (turbine No. 6, April 1996)
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Comparison of RMS errors for training a high-order and the first-order neural networks. For the high-order network, the network inputs includes 1st, 2nd, and 3rd orders of wind speeds, and two products of wind speeds with transformed wind directions, i.e., the same items as those in Eq. (8).




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