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Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea

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  • Koo, Junmo
  • Han, Gwon Deok
  • Choi, Hyung Jong
  • Shim, Joon Hyung

Abstract

In this study, we investigate the accuracy of wind-speed prediction at a designated target site using wind-speed data from reference stations that employ an ANN (artificial neural network). The reference and target sites fall into three geographical categories: plains, coast, and mountains of South Korea. Accurate wind-speed predictions are calculated by means of a correlation coefficient between the actual and simulated wind-speed data obtained by ANN. We investigate the effect of the geological characteristics of each category and the distance between reference and target sites on the accuracy of wind-speed prediction using ANN.

Suggested Citation

  • Koo, Junmo & Han, Gwon Deok & Choi, Hyung Jong & Shim, Joon Hyung, 2015. "Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea," Energy, Elsevier, vol. 93(P2), pages 1296-1302.
    • Handle: RePEc:eee:energy:v:93:y:2015:i:p2:p:1296-1302
    DOI: 10.1016/j.energy.2015.10.026
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    7. Jiang, Ping & Wang, Biao & Li, Hongmin & Lu, Haiyan, 2019. "Modeling for chaotic time series based on linear and nonlinear framework: Application to wind speed forecasting," Energy, Elsevier, vol. 173(C), pages 468-482.
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