Mutually Complementary Measure-Correlate-Predict Method for Enhanced Long-Term Wind-Resource Assessment
- Authors
- Nam, Woochul; Oh, Ki-Yong
- Issue Date
- Oct-2020
- Publisher
- MDPI
- Keywords
- measure-correlate-predict; site compliance; wind-resource assessment; wind potential prediction
- Citation
- MATHEMATICS, v.8, no.10, pp 1 - 20
- Pages
- 20
- Journal Title
- MATHEMATICS
- Volume
- 8
- Number
- 10
- Start Page
- 1
- End Page
- 20
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/53825
- DOI
- 10.3390/math8101795
- ISSN
- 2227-7390
2227-7390
- 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.
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Collections - College of Engineering > School of Energy System Engineering > 1. Journal Articles
- College of Engineering > School of Mechanical Engineering > 1. Journal Articles
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