Integrating additional data sources, such as numerical weather prediction (NWP) models and sky imagers, can significantly enhance the accuracy and reliability of satellite-derived solar irradiance estimates. NWP models provide detailed atmospheric data, including temperature, humidity, wind speed, and precipitation forecasts, which can be crucial for understanding the factors influencing solar irradiance. By incorporating NWP data, the models can better account for atmospheric conditions that affect cloud formation and behavior, leading to more accurate GHI predictions.
Sky imagers, which capture real-time images of the sky, can provide valuable information about cloud cover and type. This additional layer of data can be used to refine cloud index calculations and improve the model's ability to predict short-term fluctuations in solar irradiance. By analyzing the visual data from sky imagers, the models can learn to recognize specific cloud patterns and their impacts on solar radiation, enhancing the overall forecasting capability.
Furthermore, combining these data sources with the existing satellite imagery can create a more comprehensive dataset for training the deep learning models. This multi-faceted approach allows for a richer understanding of the interactions between various atmospheric parameters and solar irradiance, ultimately leading to improved model performance.
Additionally, the integration of these data sources can facilitate the development of hybrid models that leverage both physical and data-driven approaches. Such models can utilize the strengths of each method, providing a more robust framework for solar irradiance estimation that is adaptable to varying conditions and capable of delivering reliable forecasts across different geographic regions.