Earticle

초록

영어

This paper develops and evaluates a wind speed prediction model for Jeju area based on artificial neural networks, aiming at providing an accurate estimation of wind power generation to the smart grid system. For the history data accumulated for 10 years, the monthly speed change is modeled mainly to find the seasonal effect on tracing and resultant error patterns. A 3-layer model experimentally selects the number of hidden nodes to 10 and learns from 115 patterns, each of which consists of 5 consecutive speed values as input and one estimation output. The evaluation result shows that the error size is less than 5 % for 50 % of tracing and that slow charging over the median value opens a chance of further improvement. Finally, the monthly model makes it possible to build a refined day-by-day and hour-by-hour wind speed model based on the classification of months into winter, rainy, and other intervals.

목차

Abstract
 1. Introduction
 2. Background and Related Work
 3. Wind Speed Modeling
  3.1 Artificial Neural Network Model
  3.2 Training Pattern Generation
 4. Prediction Analysis
 5. Conclusions
 References

키워드

저자

• Junghoon Lee [ Jeju National University, Republic of Korea ]
• Gyung-Leen Park [ Jeju National University, Republic of Korea ]
• Eel-Hwan Kim [ Jeju National University, Republic of Korea ]
• Young-cheol Kim [ Jeju National University, Republic of Korea ]
• Il-Woo Lee [ Smart Grid Technology Research Team, ETRI ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605