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@article{ART001975996},
author={Seyoung Kim and Jeongmin Kim and RYU， KWANG RYEL},
title={Learning Wind Speed Forecast Model based on Numeric Prediction Algorithm},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2015},
volume={20},
number={3},
pages={19-27}

TY - JOUR
AU - Seyoung Kim
AU - Jeongmin Kim
AU - RYU， KWANG RYEL
TI - Learning Wind Speed Forecast Model based on Numeric Prediction Algorithm
JO - Journal of The Korea Society of Computer and Information
PY - 2015
VL - 20
IS - 3
PB - The Korean Society Of Computer And Information
SP - 19
EP - 27
SN - 1598-849X
AB - Technologies of wind power generation for development of alternative energy technology have beenaccumulated over the past 20 years. Wind power generation is environmentally friendly and economicalbecause it uses the wind blowing in nature as energy resource. In order to operate wind power generationefficiently, it is necessary to accurately predict wind speed changing every moment in nature. It isimportant not only averagely how well to predict wind speed but also to minimize the largest absolute error between real value and prediction value of wind speed. In terms of generation operating plan,minimizing the largest absolute error plays an important role for building flexible generation operating planbecause the difference between predicting power and real power causes economic loss. In this paper, wepropose a method of wind speed prediction using numeric prediction algorithm-based wind speed forecastmodel made to analyze the wind speed forecast given by the Meteorological Administration and patternvalue for considering seasonal property of wind speed as well as changing trend of past wind speed. Thewind speed forecast given by the Meteorological Administration is the forecast in respect to comparativelywide area including wind generation farm. But it contributes considerably to make accuracy of wind speedprediction high. Also, the experimental results demonstrate that as the rate of wind is analyzed in moredetail, the greater accuracy will be obtained.
KW - wind speed prediction;numeric prediction algorithm;ensemble of model trees
DO -
UR -
ER -

Seyoung Kim, Jeongmin Kim and RYU， KWANG RYEL. (2015). Learning Wind Speed Forecast Model based on Numeric Prediction Algorithm. Journal of The Korea Society of Computer and Information, 20(3), 19-27.

Seyoung Kim, Jeongmin Kim and RYU， KWANG RYEL. 2015, "Learning Wind Speed Forecast Model based on Numeric Prediction Algorithm", Journal of The Korea Society of Computer and Information, vol.20, no.3 pp.19-27.

Seyoung Kim, Jeongmin Kim, RYU， KWANG RYEL "Learning Wind Speed Forecast Model based on Numeric Prediction Algorithm" Journal of The Korea Society of Computer and Information 20.3 pp.19-27 (2015) : 19.

Seyoung Kim, Jeongmin Kim, RYU， KWANG RYEL. Learning Wind Speed Forecast Model based on Numeric Prediction Algorithm. 2015; 20(3), 19-27.

Seyoung Kim, Jeongmin Kim and RYU， KWANG RYEL. "Learning Wind Speed Forecast Model based on Numeric Prediction Algorithm" Journal of The Korea Society of Computer and Information 20, no.3 (2015) : 19-27.

Seyoung Kim; Jeongmin Kim; RYU， KWANG RYEL. Learning Wind Speed Forecast Model based on Numeric Prediction Algorithm. Journal of The Korea Society of Computer and Information, 20(3), 19-27.

Seyoung Kim; Jeongmin Kim; RYU， KWANG RYEL. Learning Wind Speed Forecast Model based on Numeric Prediction Algorithm. Journal of The Korea Society of Computer and Information. 2015; 20(3) 19-27.

Seyoung Kim, Jeongmin Kim, RYU， KWANG RYEL. Learning Wind Speed Forecast Model based on Numeric Prediction Algorithm. 2015; 20(3), 19-27.

Seyoung Kim, Jeongmin Kim and RYU， KWANG RYEL. "Learning Wind Speed Forecast Model based on Numeric Prediction Algorithm" Journal of The Korea Society of Computer and Information 20, no.3 (2015) : 19-27.