Journal of Environmental Science International (한국환경과학회지)

The Korean Environmental Sciences Society (한국환경과학회)
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A Study on Prediction Techniques through Machine Learning of Real-time Solar Radiation in Jeju

제주 실시간 일사량의 기계학습 예측 기법 연구

  • Received : 2017.03.16
  • Accepted : 2017.04.12
  • Published : 2017.04.30
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Abstract

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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References

  1. Arthur, S., 1959, Some studies in machine learning using the game of checkers, IBM Journal, 3(3), 210-229. https://doi.org/10.1147/rd.33.0210
  2. Jee, J. B., Lee, S. W., Choi, Y. J., Lee, K. T., 2012, The generation of typical meteorological year for research of the solar energy on the Korean Peninsula, New & Renewable Energy, 8(2), 12-23.
  3. Kim, H. Y., Kim, J., 2016, Prediction correlation of solar insolation using relationships between meteorological data and solar insolation in 2012(I), Journal of KSES, 36(1), 1-9.
  4. Lee, K. T., Zo, I. S., Jee, J. B., Choi, Y. J., 2011, Temporal and spatial distributions of the surface solar radiation by spatial resolutions on Korea Peninsula, New & Renewable Energy, 7(1), 22-28. https://doi.org/10.7849/ksnre.2011.7.1.022
  5. Lee, Y. M., Bae, J. H., Park, D. B., 2016, A Study on fog forecasting method through data mining techniques in Jeju, Journal of Environmental Science International, 25(3), 417-424. https://doi.org/10.5322/JESI.2016.25.3.417
  6. Martin, L., Zarzalejo, L. F., Polo, J., Navarro, A., Marchante, R., Cony, M., 2010, Benchmarking of different approaches to forecast solar irradiance, Solar Energy, 84(10), 1772-1781. https://doi.org/10.1016/j.solener.2010.07.002
  7. Mellit, A., Massi, P. A., 2010, A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy, Solar Energy, 84(5), 807-821. https://doi.org/10.1016/j.solener.2010.02.006
  8. Voyant, C., Randimivololona, P., Nivet, M. L., Poli, C., Muselli, M., 2013, Twenty four hours ahead global irradiation forecasting using multilayer perceptron, Meteorological Applications, 1387.