Articles | Volume 4, issue 2
https://doi.org/10.5194/wes-4-343-2019
https://doi.org/10.5194/wes-4-343-2019
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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Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Tyler McCandless on behalf of the Authors (03 May 2019)
ED: Publish as is (16 May 2019) by Athanasios Kolios
ED: Publish as is (16 May 2019) by Joachim Peinke (Chief editor)
AR by Tyler McCandless on behalf of the Authors (16 May 2019)  Manuscript 
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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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