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Offshore Wind Speed Forecasting: The Correlation between Satellite-Observed Monthly Sea Surface Temperature and Wind Speed over the Seas around the Korean Peninsula


  • Jin-Young Kim

    (New & Renewable Energy Data Center, Korea Institute of Energy Research, 152 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Hyun-Goo Kim

    (New & Renewable Energy Data Center, Korea Institute of Energy Research, 152 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Yong-Heack Kang

    (New & Renewable Energy Data Center, Korea Institute of Energy Research, 152 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)


Wind power forecasting is a key role for large-scale wind power penetration on conventional electric power systems by understanding stochastic nature of winds. This paper proposes an empirical statistical model for forecasting monthly offshore wind speeds as a function of remotely sensed sea surface temperatures over the seas around the Korean Peninsula. The model uses the optimal lagged multiple linear regression method, and predictors are characterized by mixed periodicities derived from the autocorrelation between spatially variable satellite-observed sea surface temperatures and wind speeds at all grid points over a period of about ten years (2001 to 2008). Offshore wind speeds were found to be correlated with sea surface temperatures within a seasonal range of two- to four-month lags. In particular, offshore wind speeds were closely associated with the sea surface temperature at lag 4 M, followed by lag 3 M and lag 2 M. Correlation is less at lag 1 M as compared lag 2 M, lag 3 M and lag 4 M. The results demonstrate that this approach successfully produces accurate depictions of monthly wind speeds at the gridded network. The hindcast offshore wind speeds and wind power density showed slightly improved skills compared to the seasonally varying climatology with the value of root-mean square errors, +18% and +23%, respectively. The spatial distributions of the monthly gridded wind speed and wind power density remained fairly stable from one month to another, whereas individual regions displayed slight differences in variability. The results of this study are expected to be useful in establishing guidelines for operating and managing nascent offshore farms around the Korean Peninsula.

Suggested Citation

  • Jin-Young Kim & Hyun-Goo Kim & Yong-Heack Kang, 2017. "Offshore Wind Speed Forecasting: The Correlation between Satellite-Observed Monthly Sea Surface Temperature and Wind Speed over the Seas around the Korean Peninsula," Energies, MDPI, vol. 10(7), pages 1-15, July.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:994-:d:104659

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    References listed on IDEAS

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    Cited by:

    1. Markus Gross & Vanesa Magar & Alfredo Peña, 2020. "The Effect of Averaging, Sampling, and Time Series Length on Wind Power Density Estimations," Sustainability, MDPI, vol. 12(8), pages 1-13, April.
    2. Christopher Jung & Dirk Schindler & Alexander Buchholz & Jessica Laible, 2017. "Global Gust Climate Evaluation and Its Influence on Wind Turbines," Energies, MDPI, vol. 10(10), pages 1-18, September.
    3. Lei Ren & Diarmuid Nagle & Michael Hartnett & Stephen Nash, 2017. "The Effect of Wind Forcing on Modeling Coastal Circulation at a Marine Renewable Test Site," Energies, MDPI, vol. 10(12), pages 1-27, December.

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