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The advancement of solar energy systems requires intelligent, scalable solutions that adapt to dynamic environmental conditions. This research proposes a novel AI-enhanced hybrid solar energy framework integrating spatio-temporal forecasting, adaptive control, and decentralized energy trading. The core objective is to improve the efficiency, responsiveness, and scalability of solar power generation using a unified multi-layer architecture. The system comprises a CNN-LSTM model for accurate solar irradiance forecasting, reinforcement learning for real-time dual-axis tracking, and Edge AI for low-latency control decisions. To enhance optical and thermal efficiency, the design incorporates hybrid nanocoatings with self-cleaning and anti-reflective properties, along with dual-layer phase-change materials for real-time heat regulation. Additionally, adaptive perovskite-silicon photovoltaic cells were implemented to dynamically tune electrical characteristics such as bandgap and voltage based on irradiance levels. A blockchain-enabled smart grid facilitates secure and decentralized peer-to-peer energy transactions. Experimental validation was conducted over a full year at Sitapura, Jaipur (India), under real-world climatic conditions. The proposed system achieved a 41.4% increase in annual energy yield, an 18.7% improvement in spectral absorption efficiency, and an 11.9 °C reduction in average panel temperature compared to conventional MPPT and static PV setups. Furthermore, blockchain integration reduced energy dispatch latency from 180 to 48 ms, and AI-based hybrid storage management increased battery lifespan by over 60%. The framework demonstrates significant performance enhancement, real-time adaptability, and deployment viability, offering a transformative step toward intelligent, resilient, and sustainable solar energy systems.
Mamodiya et al. (Mon,) studied this question.