To extend the proposed methodology for solar irradiance forecasts at higher temporal resolutions, such as 10-minute or 5-minute intervals, several strategies can be implemented. First, the data acquisition frequency from the Himawari-8 satellite can be leveraged, as it provides cloud imagery every 10 minutes. By utilizing this high-frequency data, the model can be trained to predict GHI based on shorter time intervals, allowing for more granular forecasting.
Second, the deep learning models, particularly those like LSTM, Informer, and Transformer, can be adapted to handle shorter input sequences. This involves modifying the input data structure to include more frequent cloud index readings and corresponding meteorological data, thereby enhancing the model's ability to capture rapid changes in solar irradiance due to fluctuating cloud cover.
Additionally, incorporating real-time ground measurements from solar PV systems can improve the model's responsiveness to immediate changes in solar conditions. By integrating these data points, the model can adjust its predictions dynamically, providing more accurate forecasts that align with the operational needs of grid integration.
Finally, implementing ensemble forecasting techniques that combine predictions from multiple models can enhance reliability. By aggregating outputs from various models trained on different aspects of the data, the overall forecast accuracy can be improved, making it more suitable for operational planning and grid management.