Mutually Complementary Measure-Correlate-Predict Method for Enhanced Long-Term Wind-Resource Assessment
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....
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| Format: | Article |
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MDPI AG
2020-10-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/8/10/1795 |
| _version_ | 1797550875142520832 |
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| author | Woochul Nam Ki-Yong Oh |
| author_facet | Woochul Nam Ki-Yong Oh |
| author_sort | Woochul Nam |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:36:38Z |
| format | Article |
| id | doaj.art-97c32b27ca1a454f9d562a70d39ff1be |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T15:36:38Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-97c32b27ca1a454f9d562a70d39ff1be2023-11-20T17:14:08ZengMDPI AGMathematics2227-73902020-10-01810179510.3390/math8101795Mutually Complementary Measure-Correlate-Predict Method for Enhanced Long-Term Wind-Resource AssessmentWoochul Nam0Ki-Yong Oh1School of Mechanical Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, KoreaSchool of Energy Systems Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 06974, KoreaEvaluating 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.https://www.mdpi.com/2227-7390/8/10/1795measure-correlate-predictsite compliancewind-resource assessmentwind potential prediction |
| spellingShingle | Woochul Nam Ki-Yong Oh Mutually Complementary Measure-Correlate-Predict Method for Enhanced Long-Term Wind-Resource Assessment Mathematics measure-correlate-predict site compliance wind-resource assessment wind potential prediction |
| title | Mutually Complementary Measure-Correlate-Predict Method for Enhanced Long-Term Wind-Resource Assessment |
| title_full | Mutually Complementary Measure-Correlate-Predict Method for Enhanced Long-Term Wind-Resource Assessment |
| title_fullStr | Mutually Complementary Measure-Correlate-Predict Method for Enhanced Long-Term Wind-Resource Assessment |
| title_full_unstemmed | Mutually Complementary Measure-Correlate-Predict Method for Enhanced Long-Term Wind-Resource Assessment |
| title_short | Mutually Complementary Measure-Correlate-Predict Method for Enhanced Long-Term Wind-Resource Assessment |
| title_sort | mutually complementary measure correlate predict method for enhanced long term wind resource assessment |
| topic | measure-correlate-predict site compliance wind-resource assessment wind potential prediction |
| url | https://www.mdpi.com/2227-7390/8/10/1795 |
| work_keys_str_mv | AT woochulnam mutuallycomplementarymeasurecorrelatepredictmethodforenhancedlongtermwindresourceassessment AT kiyongoh mutuallycomplementarymeasurecorrelatepredictmethodforenhancedlongtermwindresourceassessment |