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

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...

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Bibliographic Details
Published in:Energy (Oxford) 2015-12, Vol.93, p.1296-1302
Main Authors: Koo, Junmo, Han, Gwon Deok, Choi, Hyung Jong, Shim, Joon Hyung
Format: Article
Language:English
Subjects:
Artificial neural networks
Climate data
Wind energy
Wind prediction
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Summary: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. •Accuracy of the wind-speed prediction at the designated target site was evaluated.•Wind-speed data from reference stations employing ANN were used.•Effect of topography and distance were determined using ANN for wind-speed prediction.•Correlation, wind-speed comparison, MAE, and MSE were used.
ISSN:0360-5442
DOI:10.1016/j.energy.2015.10.026