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KMID : 1024520170260040521
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Journal of the Environmental Sciences
2017 Volume.26 No. 4 p.521 ~ p.527
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A Study on Prediction Techniques through Machine Learning of Real-time Solar Radiation in Jeju
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Lee Young-Mi
Bae Joo-Hyun
Park Jeong-Keun
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Abstract
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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%.
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KEYWORD
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Solar radiation prediction, Machine learning, Data mining, Tree models, Conditional inference tree, Random forest, Support vector machine, Logistic regression
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