Abstract:
The authors of this paper explores the use of cutting-edge artificial intelligence (AI) technologies—
specifically Deep Learning (DL), Explainable AI (XAI), and Generative AI (Gen AI)—for
predicting solar energy irradiance. Given the critical role of accurate irradiance forecasting for
optimizing solar energy production, this paper proposes a hybrid AI framework combining these
technologies to improve prediction accuracy while ensuring economic feasibility. The proposed
methodology integrates a DL-based model for time-series forecasting, with XAI techniques to
enhance interpretability, and Gen AI to address data scarcity issues. We analyze the advantages and
disadvantages of each technology in the context of solar energy prediction and provide
recommendations on their practical deployment. The results show that the hybrid approach offers
substantial improvements in forecasting accuracy and model transparency, making it a promising
solution for real-world applications.
