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

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

Energy, 2015, vol. 93, issue P2, 1296-1302

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.

Keywords: Wind energy; Artificial neural networks; Wind prediction; Climate data (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:93:y:2015:i:p2:p:1296-1302

DOI: 10.1016/j.energy.2015.10.026

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