Wind Energy Science
Wind Energy Science
WES
Articles | Volume 4, issue 2
Articles | Volume 4, issue 2
https://doi.org/10.5194/wes-4-343-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-4-343-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 4, issue 2
Research article
 | 
04 Jun 2019
Research article |  | 04 Jun 2019

The super-turbine wind power conversion paradox: using machine learning to reduce errors caused by Jensen's inequality

Tyler C. McCandless and Sue Ellen Haupt

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Latest update: 05 Nov 2024
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Short summary
Often in wind power forecasting the mean wind speed is forecasted at a plant, converted to power, and multiplied by the number of turbines to predict the plant's generating capacity. This methodology ignores the variability among turbines caused by localized weather, terrain, and array orientation. We show that the wind farm mean wind speed approach for power conversion is impacted by Jensen's inequality, quantify the differences, and show machine learning can overcome these differences.
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