• Nikin Tharan

Advanced solar forecasting has emerged as a critical factor in integrating renewable energy into modern power grids. This theoretical review examines a range of AI-driven hybrid models—combining deep learning architectures (e.g., CNN-LSTM) with statistical or physics-based approaches—to demonstrate how improved forecasts enhance grid reliability and efficiency. By leveraging diverse data sources such as satellite imagery and ground-based measurements, these methods offer more accurate short-term and long-term predictions, enabling grid operators to better balance supply and demand, minimize curtailment, and reduce operational costs. The paper also discusses how reliable solar forecasts inform policy decisions, from regulatory frameworks that encourage transparent and precise forecasts to market mechanisms rewarding accurate generation scheduling. Additionally, industry professionals—including energy companies, solar farm managers, and system operators—can leverage advanced forecasting to optimize maintenance, storage integration, and financial planning. Future research avenues highlight the integration of climate models with AI forecasting techniques, paving the way for scalable and adaptive systems capable of handling evolving weather patterns and accelerating the global transition to renewable energy.

Solar forecasting, Hybrid AI models, Grid stability, Renewable energy integration, Energy policy, Deep learning, Climate variability

"AI-Driven Hybrid Solar Forecasting for Grid Stability and Policy: A Theoretical Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 2, page no.d870-d877, February-2025, Available :http://www.jetir.org/papers/JETIR2502397.pdf

"AI-Driven Hybrid Solar Forecasting for Grid Stability and Policy: A Theoretical Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 2, page no. ppd870-d877, February-2025, Available at : http://www.jetir.org/papers/JETIR2502397.pdf