Mutually Complementary Measure-Correlate-Predict Method for Enhanced Long-Term Wind-Resource Assessment

Woochul Nam and Ki-Yong Oh
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Woochul Nam: School of Mechanical Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea
Ki-Yong Oh: School of Energy Systems Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, Korea

Mathematics, 2020, vol. 8, issue 10, 1-20

Abstract: Evaluating the economic feasibility of wind farms via long-term wind-resource assessments is indispensable because short-term data measured at a candidate wind-farm site cannot represent the long-term wind potential. Prediction errors are significant when seasonal and year-on-year variations occur. Moreover, reliable long-term reference data with a high correlation to short-term measured data are often unavailable. This paper presents an alternative solution to predict long-term wind resources for a site exhibiting seasonal and year-on-year variations, where long-term reference data are unavailable. An analysis shows that a mutually complementary measure-correlate-predict method can be employed, because several datasets obtained over short periods are used to correct long-term wind resource data in a mutually complementary manner. Moreover, this method is useful in evaluating extreme wind speeds, which is one of the main factors affecting site compliance evaluation and the selection of a suitable wind turbine class based on the International Electrotechnical Commission standards. The analysis also shows that energy density is a more sensitive metric than wind speed for sites with seasonal and year-on-year variations because of the wide distribution of wind speeds. A case study with short-term data measured at Fujeij, Jordan, clearly identifies the factors necessary to perform the reliable and accurate assessment of long-term wind potentials.

Keywords: measure-correlate-predict; site compliance; wind-resource assessment; wind potential prediction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
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