Power Curve Estimation With Multivariate Environmental
Factors for Inland and Offshore Wind Farms
Item Type
Article
Authors
Lee, Giwhyun; Ding, Yu; Genton, Marc G.; Xie, Le
Citation
Power Curve Estimation With Multivariate Environmental Factors
for Inland and Offshore Wind Farms 2015, 110 (509):56 Journal of
the American Statistical Association
Eprint version
Post-print
DOI
10.1080/01621459.2014.977385
Publisher
Informa UK Limited
Journal
Journal of the American Statistical Association
Rights
This is an Accepted Manuscript of an article published by
Taylor & Francis in Journal of the American Statistical
Association on Apr. 22, 2015, available online: http://
wwww.tandfonline.com/10.1080/01621459.2014.977385.
Download date
17/10/2019 09:58:19
Link to Item
Power Curve Estimation with Multivariate Environmental Factors
for Inland and Offshore Wind Farms
Giwhyun Lee
Korea Army Academy
Yeongcheon, Republic of Korea
email:
[email protected]
Yu Ding
Industrial and Systems Engineering,
Texas A&M University, TX, USA
email:
[email protected]
Marc G. Genton
CEMSE Division,
King Abdullah University of Science and Technology, Saudi Arabia
email:
[email protected]
Le Xie
Electrical and Computer Engineering,
Texas A&M University, TX, USA
email:
[email protected]
1
Abstract
In the wind industry, a power curve refers to the functional relationship between the power
output generated by a wind turbine and the wind speed at the time of power generation.
Power curves are used in practice for a number of important tasks including predicting wind
power production and assessing a turbine’s energy production efficiency. Nevertheless, actual
wind power data indicate that the power output is affected by more than just wind speed.
Several other environmental factors, such as wind direction, air density, humidity, turbulence
intensity, and wind shears, have potential impact. Yet, in industry practice, as well as in
the literature, current power curve models primarily consider wind speed and, sometimes,
wind speed and direction.
We propose an additive multivariate kernel method that can
include the aforementioned environmental factors as a new power curve model. Our model
provides, conditional on a given environmental condition, both the point estimation and
density estimation of power output. It is able to capture the nonlinear relationships between
environmental factors and the wind power output, as well as the high-order interaction
effects among some of the environmental factors. Using operational data associated with four
turbines in an inland wind farm and two turbines in an offshore wind farm, we demonstrate
the improvement achieved by our kernel method.
Keywords
:
Additive multivariate kernel regression, Nonparametric estimation, Turbine
performance assessment, Wind power forecast
2
1.
INTRODUCTION
Wind energy is one of the fastest growing renewable energy sources. According to a report
issued by the American Wind Energy Association (AWEA), wind power installation in the
U.S. increased by more than a factor of ten in the past decade, from 4,232 megawatts (MW)
in 2001 to 46,919 MW by the end of 2011 (AWEA 2012). The U.S. Department of Energy
