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

doi:10.1016/j.energy.2015.10.026

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^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

41 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 language	English
Pages (from-to)	1296-1302
Number of pages	7
Journal	Energy
Volume	93
DOIs	https://doi.org/10.1016/j.energy.2015.10.026
Publication status	Published - 2015

Bibliographical note

Funding Information:
This work was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20124010203250 ). The Brain Korea 21 Plus program (No. 21A20131712520 ) and the Korea University Grant are also acknowledged for their partial support.

Publisher Copyright:
© 2015 Elsevier Ltd.

Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.

Keywords

Artificial neural networks
Climate data
Wind energy
Wind prediction

ASJC Scopus subject areas

Civil and Structural Engineering
Building and Construction
Pollution
Mechanical Engineering
Industrial and Manufacturing Engineering
Electrical and Electronic Engineering

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10.1016/j.energy.2015.10.026

Cite this

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

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 = "Artificial neural networks, Climate data, Wind energy, Wind prediction",

author = "Junmo Koo and Han, \{Gwon Deok\} and Choi, \{Hyung Jong\} and Shim, \{Joon Hyung\}",

note = "Funding Information: This work was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant financial resource from the Ministry of Trade, Industry \& Energy, Republic of Korea (No. 20124010203250 ). The Brain Korea 21 Plus program (No. 21A20131712520 ) and the Korea University Grant are also acknowledged for their partial support. Publisher Copyright: {\textcopyright} 2015 Elsevier Ltd. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.",

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doi = "10.1016/j.energy.2015.10.026",

language = "English",

volume = "93",

pages = "1296--1302",

journal = "Energy",

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T2 - A case study in South Korea

AU - Koo, Junmo

AU - Han, Gwon Deok

AU - Choi, Hyung Jong

AU - Shim, Joon Hyung

N1 - Funding Information: This work was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant financial resource from the Ministry of Trade, Industry & Energy, Republic of Korea (No. 20124010203250 ). The Brain Korea 21 Plus program (No. 21A20131712520 ) and the Korea University Grant are also acknowledged for their partial support. Publisher Copyright: © 2015 Elsevier Ltd. Copyright: Copyright 2017 Elsevier B.V., All rights reserved.

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

KW - Artificial neural networks

KW - Climate data

KW - Wind energy

KW - Wind prediction

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UR - https://www.scopus.com/pages/publications/84954520579#tab=citedBy

U2 - 10.1016/j.energy.2015.10.026

DO - 10.1016/j.energy.2015.10.026

M3 - Article

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SN - 0360-5442

VL - 93

SP - 1296

EP - 1302

JO - Energy

JF - Energy

ER -

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

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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