Browse > Article
http://dx.doi.org/10.7836/kses.2019.39.2.071

Evaluation of UM-LDAPS Prediction Model for Daily Ahead Forecast of Solar Power Generation  

Kim, Chang Ki (New and Renewable Energy Resource & Policy Center, Korea Institute of Energy Research)
Kim, Hyun-Goo (New and Renewable Energy Resource & Policy Center, Korea Institute of Energy Research)
Kang, Yong-Heack (New and Renewable Energy Resource & Policy Center, Korea Institute of Energy Research)
Yun, Chang-Yeol (New and Renewable Energy Resource & Policy Center, Korea Institute of Energy Research)
Publication Information
Journal of the Korean Solar Energy Society / v.39, no.2, 2019 , pp. 71-80 More about this Journal
Abstract
Daily ahead forecast is necessary for the electricity balance between load and supply due to the variability renewable energy. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for more than 12 hours forecast horizon. UM-LDAPS model is the numerical weather prediction operated by Korea Meteorological Administration and it generates the 36 hours forecast of hourly total irradiance 4 times a day. This study attempts to evaluate the model performance against the in situ measurements at 37 ground stations from January to May, 2013. Relative mean bias error, mean absolute error and root mean square error of hourly total irradiance are averaged over all ground stations as being 8.2%, 21.2% and 29.6%, respectively. The behavior of mean bias error appears to be different; positively largest in Chupoongnyeong station but negatively largest in Daegu station. The distinct contrast might be attributed to the limitation of microphysics parameterization for thick and thin clouds in the model.
Keywords
Numerical weather prediction; UM-LDAPS(Unified Model-Local Data Assimilation and Prediction System); In-situ measured solar irradiance;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Kleissl, J., Solar Energy Forecasting and Resource Assessment, Academic Press, 1st Ed., pp. 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.   DOI
3 Lee, Y.-M., J.-H. Bae, and 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.   DOI
4 Korea Meteorological Administration, Evaluation of Numerical Weather Prediction System (2016), TR11-1360709-000001-10, pp. 198, 2016
5 Kim, C. K., Kim, H.-G., Kang, Y.-H., and Yun, C.-Y., Toward Improved Solar Irradiance Forecasts: Comparison of the Global Horizontal Irradiances Derived from the COMS Satellite Imagery Over the Korean Peninsula, Pure Appl. Geophys., Vol. 174, pp. 2773-2792, 2017.   DOI
6 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.   DOI