%0 Journal Article %1 Shokrzadeh2014Wind %A Shokrzadeh, S. %A Jafari Jozani, M. %A Bibeau, E. %D 2014 %I IEEE %J Sustainable Energy, IEEE Transactions on %K wind energy statistics renewables turbines %N 4 %P 1262--1269 %R 10.1109/tste.2014.2345059 %T Wind Turbine Power Curve Modeling Using Advanced Parametric and Nonparametric Methods %U http://dx.doi.org/10.1109/tste.2014.2345059 %V 5 %X Wind turbine power curve modeling is an important tool in turbine performance monitoring and power forecasting. There are several statistical techniques to fit the empirical power curve of a wind turbine, which can be classified into parametric and nonparametric methods. In this paper, we study four of these methods to estimate the wind turbine power curve. Polynomial regression is studied as the benchmark parametric model, and issues associated with this technique are discussed. We then introduce the locally weighted polynomial regression method, and show its advantages over the polynomial regression. Also, the spline regression method is examined to achieve more flexibility for fitting the power curve. Finally, we develop a penalized spline regression model to address the issues of choosing the number and location of knots in the spline regression. The performance of the presented methods is evaluated using two simulated data sets as well as an actual operational power data of a wind farm in North America.

@article{Shokrzadeh2014Wind, abstract = {Wind turbine power curve modeling is an important tool in turbine performance monitoring and power forecasting. There are several statistical techniques to fit the empirical power curve of a wind turbine, which can be classified into parametric and nonparametric methods. In this paper, we study four of these methods to estimate the wind turbine power curve. Polynomial regression is studied as the benchmark parametric model, and issues associated with this technique are discussed. We then introduce the locally weighted polynomial regression method, and show its advantages over the polynomial regression. Also, the spline regression method is examined to achieve more flexibility for fitting the power curve. Finally, we develop a penalized spline regression model to address the issues of choosing the number and location of knots in the spline regression. The performance of the presented methods is evaluated using two simulated data sets as well as an actual operational power data of a wind farm in North America.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {Shokrzadeh, S. and Jafari Jozani, M. and Bibeau, E.}, biburl = {https://www.bibsonomy.org/bibtex/2bfb1a2faee24d6f7e140f822a9d41f5b/pbett}, citeulike-article-id = {13368998}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/tste.2014.2345059}, citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6894235}, comment = {(private-note)Open access.}, doi = {10.1109/tste.2014.2345059}, institution = {Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB, Canada}, interhash = {4bc651ff5e2b4e2d01c814f894815cba}, intrahash = {bfb1a2faee24d6f7e140f822a9d41f5b}, issn = {1949-3029}, journal = {Sustainable Energy, IEEE Transactions on}, keywords = {wind energy statistics renewables turbines}, month = oct, number = 4, pages = {1262--1269}, posted-at = {2014-09-20 14:50:43}, priority = {2}, publisher = {IEEE}, timestamp = {2018-06-22T18:34:29.000+0200}, title = {Wind Turbine Power Curve Modeling Using Advanced Parametric and Nonparametric Methods}, url = {http://dx.doi.org/10.1109/tste.2014.2345059}, volume = 5, year = 2014 }