AI-Enhanced Solar Energy Framework Offers Significant Performance Enhancement for Sustainable Power Generation

Strategic Insights -

Researchers at Poornima University have developed a novel AI-enhanced hybrid solar energy framework that integrates spatio-temporal forecasting, adaptive control, and decentralized energy trading. This innovative system aims to improve the efficiency, responsiveness, and scalability of solar power generation using a unified multi-layer architecture. The framework 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.

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. Additionally, 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.

Key Takeaways:

  • The AI-enhanced hybrid solar energy framework integrates spatio-temporal forecasting, adaptive control, and decentralized energy trading to improve solar power generation efficiency.
  • The proposed system achieves 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.
  • Blockchain integration reduces energy dispatch latency from 180 to 48 ms, and AI-based hybrid storage management increases battery lifespan by over 60%.
  • The framework demonstrates significant performance enhancement, real-time adaptability, and deployment viability for sustainable solar energy systems.
  • The research was conducted over a full year at Sitapura, Jaipur (India), under real-world climatic conditions.
  • Udit Mamodiya, Faculty of Engineering and Technology, Poornima University, was a key researcher on the project.
  • Additional researchers include Indra Kishor, Ramakrishna Garine, Priyam Ganguly, and Nithesh Naik.

Statistics:

  • 41.4% increase in annual energy yield
  • 18.7% improvement in spectral absorption efficiency
  • 11.9 °C reduction in average panel temperature
  • 180 ms reduction in energy dispatch latency to 48 ms
  • 60% increase in battery lifespan
  • 1-year experimental validation in real-world climatic conditions at Sitapura, Jaipur (India)
  • 4 researchers involved in the project

Sources:

  • NewsRx. Research from Poornima University Provides New Data on Sustainable Energy (Artificial intelligence based hybrid solar energy systems with smart materials and adaptive photovoltaics for sustainable power generation). Ecology, Environment & Conservation. June 13, 2025; p 436.
  • Artificial intelligence based hybrid solar energy systems with smart materials and adaptive photovoltaics for sustainable power generation. Scientific Reports, 2025,15(1):1-29. (Scientific Reports - http://www.nature.com/srep/index.html).