This study proposes a Long Short-Term Memory (LSTM)-based deep learning model for short-, medium-, and long-term forecasting of solar energy production. Approximately four years of hourly data from four photovoltaic power plants in Karabük were used. In addition to production data, meteorological and environmental variables were included through a multivariate forecasting approach. The model was tested under three scenarios at different time scales. Performance was evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2) metrics. Results showed high prediction accuracy, particularly with seasonal data, where R2 values exceeded 0.90 in most cases. In forecasts based on monthly data, performance was more variable, and the shorter data window limited the model’s learning capacity. Long-term analyses indicated that the model successfully captured overall production trends and achieved high accuracy across all Photovoltaic (PV) systems. The findings also revealed that incorporating meteorological and environmental variables significantly improved prediction performance. In particular, air pollution parameters were effective in long-term production forecasting. Overall, the study demonstrates that Long Short-Term Memory (LSTM)-based models are reliable and effective tools for solar energy forecasting, with strong potential for energy planning and smart grid applications.
Gultekin et al. (Tue,) studied this question.