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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Cited articles

Bartlett, D.: Power Conversion: Plant-level vs. Turbine-Level, Temperature, Static vs. Self-learning, Energy System Integration Group Forecasting Workshop, St. Paul, MN, 21 June 2018. 
Breiman, L.: Random Forest, Mach. Learn., 45, 5–32, 2001. 
Bulaevskaya, V., Wharton, S., Clifton, A., Qualley, G., and Miller, W. O.: Wind power curve modeling in complex terrain using statistical models, J. Renew. Sustain. Energ., 7, 013103, https://doi.org/10.1063/1.4904430, 2015. 
Denny, M.: The fallacy of the average: on the ubiquity, utility and continuing novelty of Jensen's inequality, J. Exper. Biol., 220, 139–146, https://doi.org/10.1242/jeb.140368, 2017. 
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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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