Comparative analysis of regression and artificial neural network models for wind turbine power curve estimation

Shuhui Li, Donald C. Wunsch, Edgar O’Hair, Michael G. Giesselmann

Research output: Contribution to journalArticlepeer-review

89 Scopus citations

Abstract

This paper examines and compares regression and artificial neural network modelsused 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.

Original languageEnglish
Pages (from-to)327-332
Number of pages6
JournalJournal of Solar Energy Engineering, Transactions of the ASME
Volume123
Issue number4
DOIs
StatePublished - Nov 1 2001

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