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, Joon Hyung Shim

Research output: Contribution to journalArticle

12 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1296-1302
Number of pages7
JournalEnergy
Volume93
DOIs
Publication statusPublished - 2015

Keywords

  • Artificial neural networks
  • Climate data
  • Wind energy
  • Wind prediction

ASJC Scopus subject areas

  • Energy(all)
  • Pollution

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