Journal of the Korean Solar Energy Society (한국태양에너지학회 논문집)

The Korean Solar Energy Society (한국태양에너지학회)
권호 표지
DOI QR코드

DOI QR Code

Evaluation of UM-LDAPS Prediction Model for Solar Irradiance by using Ground Observation at Fine Temporal Resolution

고해상도 일사량 관측 자료를 이용한 UM-LDAPS 예보 모형 성능평가

  • Kim, Chang Ki (New and Renewable Energy Resource Map Laboratory, Korea Institute of Energy Research) ;
  • Kim, Hyun-Goo (New and Renewable Energy Resource Map Laboratory, Korea Institute of Energy Research) ;
  • Kang, Yong-Heack (New and Renewable Energy Resource Map Laboratory, Korea Institute of Energy Research) ;
  • Kim, Jin-Young (New and Renewable Energy Resource Map Laboratory, Korea Institute of Energy Research)
  • 김창기 (한국에너지기술연구원 신재생자원지도연구실) ;
  • 김현구 (한국에너지기술연구원 신재생자원지도연구실) ;
  • 강용혁 (한국에너지기술연구원 신재생자원지도연구실) ;
  • 김진영 (한국에너지기술연구원 신재생자원지도연구실)
  • Received : 2020.07.16
  • Accepted : 2020.09.11
  • Published : 2020.10.30
Download PDF
⟨ Previous Next ⟩

Abstract

Day ahead forecast is necessary for the electricity market to stabilize the electricity penetration. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for longer than 12 hours forecast horizon. Korea Meteorological Administration operates the UM-LDAPS model to produce the 36 hours forecast of hourly total irradiance 4 times a day. This study interpolates the hourly total irradiance into 15 minute instantaneous irradiance and then compare them with observed solar irradiance at four ground stations at 1 minute resolution. Numerical weather prediction model employed here was produced at 00 UTC or 18 UTC from January to December, 2018. To compare the statistical model for the forecast horizon less than 3 hours, smart persistent model is used as a reference model. Relative root mean square error of 15 minute instantaneous irradiance are averaged over all ground stations as being 18.4% and 19.6% initialized at 18 and 00 UTC, respectively. Numerical weather prediction is better than smart persistent model at 1 hour after simulation began.

Keywords

References

  1. Kleissl, J., Solar Energy Forecasting and Resource Assessment, Academic Press, 1st Ed., p. 416.
  2. Diagne, M., David, M., Lauret, P., Boland, J., and Schmutz, N., Review of Solar Irradiance Forecasting Methods and a Proposition for Small-scale Insular Grids. Renewable and Sustainable Energy Reviews, Vol. 27, pp. 65-76, 2013. https://doi.org/10.1016/j.rser.2013.06.042
  3. Lee, Y.-M., Bae, J.-H., Park, J.-K., A Study on Prediction Techniques through Machine Learning of Real-time Solar Radiation in Jeju, Journal of Environmental Science International, Vol.26, No.4, pp.521-527, 2017. https://doi.org/10.5322/JESI.2017.26.4.521
  4. Kim, C. K., Kim, H.-G., Kang, Y.-H., and Yun, C.-Y., Evaluation of UM-LDAPS Prediction Model for Daily Ahead Forecast of Solar Power Generation, Journal of the Korean Solar Energy Society, Vol. 39, pp. 71-80, 2019. https://doi.org/10.7836/KSES.2019.39.2.071
  5. Korea Meteorological Administration, Evaluation of Numerical Weather Prediction System (2016), TR11-1360709-000001-10, pp. 198, 2016.
  6. Sengupta, M., Habte, A., Gueymard, C., Wilbert, S., and Renne, D., Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications (NREL/TP-5D00-68886). Golden, CO: National Renewable Energy Laboratory, 2017.
  7. Mathiesen, P. and Kleissl, J., Evaluation of Numerical Weather Prediction for Intra-day Solar Forecasting in the Continental United States. Solar Energy, Vol. 85, pp. 967-977, 2011. https://doi.org/10.1016/j.solener.2011.02.013