Comparative Analysis of Regression and Artificial Neural Network Models for Wind Turbine Power Curve Estimation-Reference-Cited by-同舟云学术

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

Published:2001-07-01 Issue:4 Volume:123 Page:327-332
ISSN:0199-6231
Container-title:Journal of Solar Energy Engineering
language:en
Short-container-title:

Author:

Li Shuhui¹,Wunsch Donald C.²,O’Hair Edgar³,Giesselmann Michael G.³

Affiliation:

1. Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville, Kingsville TX 78363

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

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

Abstract

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.

Publisher

ASME International

Subject

Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Link

http://asmedigitalcollection.asme.org/solarenergyengineering/article-pdf/123/4/327/5593202/327_1.pdf

Reference20 articles.

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2. Landberg, L., 1997, “A Mathematical Look at a Physical Power Prediction Model,” presented at EWEC’97, Dublin, Denmark.

3. Kariniotakis, G. N., Stavrakakis, G. S., and Nogaret, E. F., 1996, “Wind Power Forecasting using Advanced Neural Networks Models,” IEEE Trans. on Energy Conversion, 11, No. 4, pp. 762–767.

4. Bossanyi, E. A., 1985, “Stochastic Wind Prediction for Wind Turbine System Control,” Proc. of 7th British Wind Energy Association Conf. Oxford, U.K., pp. 219–226.

5. Li, S., O’Hair, E., and Giesselmann, M., 1997, “Using neural networks to predict wind power generation,” Proc. of Int. Solar Energy Conf. Washington D.C., pp. 415–420.

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