ReIPV - Resilient and Intelligent PV Systems for Arid Environments

The proposed research project introduces an integrated framework that combines PV energy yield forecasting, sustainability assessment, and predictive diagnostics tailored specifically for desert climates. The project creates new datasets, models, and decision tools tailored to hot/dry conditions by combining five synergistic WPs that seamlessly integrate advanced materials validation, autonomous operation, AI-driven analytics, and life-cycle assessment into a unified decision-support system. The work is structured into five synergistic work packages (WPs): • Technology Benchmarking and Environmental Degradation Modeling – Field testing and accelerated degradation modeling of emerging PV technologies (TOPCon, HJT, PERC, IBCM, and perovskite–Si tandem), mounting configurations (HSAT, vertical, fixed), and innovative applications (floating PV, Agri-PV, and building-integrated). • Autonomous PV System Monitoring and Operation – Development of AI-based platforms for autonomous monitoring and robotic cleaning, informed by real-time sensing and soiling forecasts from the Environment Center. • Materials-Integrated Smart Coatings and Module Design – Validation of anti-soiling and IR-reflective coatings developed by the Materials Unit, assessing their impact on yield, cleaning frequency, and mechanical degradation. • PV Failure Analysis and Predictive Diagnostics—Conducts root cause failure analysis and develops AI-driven predictive maintenance systems using real-time monitoring and digital twin technologies to anticipate and mitigate system faults. • Integrated Energy Yield Analysis and Sustainability Assessment—Combines energy yield forecasting with comprehensive life-cycle assessment (LCA) and techno-economic analysis (LCOE) to provide a holistic evaluation of PV performance, reliability, and environmental impact under desert conditions.

Hamad Bin Khalifa University (HBKU)