TY - JOUR
T1 - Comparative analysis of regression and artificial neural network models for wind turbine power curve estimation
AU - Li, Shuhui
AU - Wunsch, Donald C.
AU - O’Hair, Edgar
AU - Giesselmann, Michael G.
N1 - Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2001/11/1
Y1 - 2001/11/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=3543104882&partnerID=8YFLogxK
U2 - 10.1115/1.1413216
DO - 10.1115/1.1413216
M3 - Article
AN - SCOPUS:3543104882
VL - 123
SP - 327
EP - 332
JO - Journal of Solar Energy Engineering, Transactions of the ASME
JF - Journal of Solar Energy Engineering, Transactions of the ASME
SN - 0199-6231
IS - 4
ER -