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Forecasting the Long-Term Wind Data via Measure-Correlate-Predict (MCP) Methods

Author

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  • Sajid Ali

    (Smart City Construction Engineering, University of Science & Technology (UST), 217, Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea
    Environmental & Plant Engineering Research Division, Korea Institute of Civil Engineering and Building Technology (KICT), Daehwa-dong 283, Goyangdae-ro, Ilsanseo-Gu, Goyang-si 10223, Korea)

  • Sang-Moon Lee

    (Environmental & Plant Engineering Research Division, Korea Institute of Civil Engineering and Building Technology (KICT), Daehwa-dong 283, Goyangdae-ro, Ilsanseo-Gu, Goyang-si 10223, Korea)

  • Choon-Man Jang

    (Smart City Construction Engineering, University of Science & Technology (UST), 217, Gajeong-ro, Yuseong-gu, Daejeon 34113, Korea
    Environmental & Plant Engineering Research Division, Korea Institute of Civil Engineering and Building Technology (KICT), Daehwa-dong 283, Goyangdae-ro, Ilsanseo-Gu, Goyang-si 10223, Korea)

Abstract

The current study aims to forecast and analyze wind data such as wind speed at a test site called “Urumsill” on Deokjeok Island, South Korea. The measured wind data available at the aforementioned test site are only for two years (2015 and 2016), making it impossible to analyze the long-term wind characteristics. In order to overcome this problem, two measure-correlate-predict (MCP) techniques were adopted using long-term wind data (2000–2016), measured by a meteorological mast (met-mast) installed at a distance of 3 km from the test site. The wind data measured at the test site in 2016 were selected as training data to build the MCP models, whereas wind data of 2015 were used to test the accuracy of MCP models (test data). The wind data at both sites were measured at a height of 10 m and showed a good agreement for the year 2016 (training period). Using the comparison results of the year 2016, wind speed predictions were made for the rest of the years (2000–2016) at the test site. The forecasted values of wind speed had maximum relative error in the range of ±0.8 m/s for the test year of 2105. The predicted wind data values were further analyzed by estimating the mean wind speed, the Weibull shape, and the scale parameters, on a seasonal and an annual basis, in order to understand the wind behavior in the region. The accuracy and presence of possible errors in the forecasted wind data are discussed and presented.

Suggested Citation

  • Sajid Ali & Sang-Moon Lee & Choon-Man Jang, 2018. "Forecasting the Long-Term Wind Data via Measure-Correlate-Predict (MCP) Methods," Energies, MDPI, vol. 11(6), pages 1-17, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1541-:d:152305
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    References listed on IDEAS

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    Cited by:

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    3. Mekalathur B Hemanth Kumar & Saravanan Balasubramaniyan & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen, 2019. "Wind Energy Potential Assessment by Weibull Parameter Estimation Using Multiverse Optimization Method: A Case Study of Tirumala Region in India," Energies, MDPI, vol. 12(11), pages 1-21, June.

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