Solar radiation forecasts are important for predicting the amount of ice on road and the potential solar energy. In an attempt to improve solar radiation predictability in Jeju, we conducted machine learning with various data mining techniques such as tree models, conditional inference tree, random forest, support vector machines and logistic regression. To validate machine learning models, the results from the simulation was compared with the solar radiation data observed over Jeju observation site. According to the model assesment, it can be seen that the solar radiation prediction using random forest is the most effective method. The error rate proposed by random forest data mining is 17%.

Abstract
 1. 서 론
 2. 재료 및 방법
  2.1. 기계학습 이론
  2.2. 분석 방법
 3. 결과 및 고찰
  3.1. 분석 결과
  3.2. 검증 결과
 4. 결 론
 REFERENCES

• 이영미((주)에코브레인) | Young-Mi Lee (Eco Brain Co., Ltd.) Corresponding author
• 배주현((주)에코브레인) | Joo-Hyun Bae (Eco Brain Co., Ltd.)
• 박정근((주)에코브레인) | Jeong-keun Park (Eco Brain Co., Ltd.)