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Abstract Details
Activity Number:
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136
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #305279 |
Title:
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A Bayesian Nonlinear Regression Model with Application to Wind Turbine Power Curve
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Author(s):
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Zhanpan Zhang*+ and Colin C McCulloch
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Companies:
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GE Global Research and GE Global Research
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Address:
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One Research Circle, Niskayuna, NY, 12309, United States
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Keywords:
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Annual Energy Production ;
Gibbs Sampling ;
Nonlinear Regression ;
Power Curve
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Abstract:
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A method is described for fitting a highly parameterized power curve to wind turbine data. The power curve has very few parameters relative to traditional methods, which facilitates its estimation when much less data is available than current power curve methods require. A robust error model is employed to reduce the influence of outliers that are commonly seen in wind turbine data in the high wind speed/low power regime. Two Bayesian statistical techniques, normal approximation of the posterior distribution and Gibbs sampling, are used to estimate the power curve and quantify uncertainty. Given the empirical long term wind speed distribution, the power curve estimate can be used to estimate the annual energy production (AEP). The performance of the proposed method is studied through a simulation study and real data analysis.
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