Comparative Analysis of the Similarity of Solar Irradiance Across Domestic Meteorological Datasets for Short-term Solar Power Generation Forecasting

Photovoltaic power generation varies depending on the weather conditions.The selection of meteorological data is important for securing reliable short-term forecasts.This study analyzed errors in forecasting global horizontal irradiance (GHI) and PVWatts model-based power generation for 11 sites in Korea.Automated synoptic observing system (ASOS) 2025 data were used as reference against which past ASOS data (2021-2024), a typical meteorological year, and the local data assimilation and prediction system (LDAPS) 2025 were compared.Errors were evaluated for all periods as well as sunshine periods using the mean absolute percentage error (MAPE) and R².The comparisons revealed that the predictive performance of the LDAPS 2025 is the best.In the case of sunshine periods, for GHI and power generation, the R² values were 0.832 and 0.829, and the MAPE values were 15.9% and 14.8%, respectively.These results show that the LDAPS 2025 accurately estimates weather variability at the time of forecasting .Overall, the results can be used as a basis for selecting meteorological input data for short-term photovoltaic power generation forecasting.