Artificial intelligence based hybrid solar energy systems with smart materials and adaptive photovoltaics for sustainable power generation

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.