Accurate day-ahead solar irradiance forecasting is essential for reliable photovoltaic (PV) power generation and power system operation. This study proposes a machine-learning-based approach for site-specific day-ahead forecasting of plane-of-array (POA) irradiance using satellite-derived global horizontal irradiance (GHI) and meteorological predictors. Seven machine learning and deep learning models are evaluated using time-series data to forecast day-ahead POA irradiance from satellite-derived GHI. Training and evaluation are performed within a rolling-window validation framework, while hyperparameters are optimized using grid search and automated tuning. As baseline references, satellite-derived GHI is directly used as a proxy for site POA irradiance and compared with measured values, while a day-ahead persistence model is introduced as a simple benchmark. The experimental setup is designed to reflect an operational forecasting setting while relying on idealized meteorological inputs to isolate the modeling capability and assess the maximum achievable accuracy of day-ahead POA irradiance forecasting, which can be interpreted as an upper-bound performance scenario. The results show that machine learning models reduce the RMSE from 154.45 W/m2 to 75.5 W/m2 on the validation set, corresponding to an improvement of approximately 51% relative to the persistence baseline. Additionally, the impact of changepoint detection on the training process is investigated to account for structural shifts in the time series, and the influence of irradiance forecasting accuracy on photovoltaic power generation is evaluated through comparative PV energy yield calculations. The findings indicate that regression-based site adaptation of satellite-derived irradiance represents an effective approach for improving site-specific day-ahead POA irradiance forecasting while highlighting the importance of controlled evaluation conditions when assessing model performance.
Tanev et al. (Sat,) studied this question.