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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
- Issue Date
- 15-Dec-2015
- Publisher
- PERGAMON-ELSEVIER SCIENCE LTD
- Keywords
- Wind energy; Artificial neural networks; Wind prediction; Climate data
- Citation
- ENERGY, v.93, pp.1296 - 1302
- Indexed
- SCIE
SCOPUS
- Journal Title
- ENERGY
- Volume
- 93
- Start Page
- 1296
- End Page
- 1302
- ISSN
- 0360-5442
- 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. (C) 2015 Elsevier Ltd. All rights reserved.
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Related Researcher
- Shim, Joon Hyung
- College of Engineering (Department of Mechanical Engineering)