Proposals are expected to:

• Demonstrate integrated multi-aspect sensing (optical, thermal, electrical) into PV modules to suppress degradation, detect unwanted operating conditions and avoid failures with emphasis on achieving high MWh/Wp (e.g. shade tolerant; more advanced electronic design with in-module components).
• Demonstrate smart control/tracking systems (e.g. coupled with real-time monitoring data, forecasting, EMS, etc.) for performance optimisation in specific PV applications (e.g. “self-protection” under extreme events in harsh environments like dust/snow storms).
• Demonstrate hybrid or integrated monitoring-diagnostic imagery solutions for maximum spatiotemporal granularity and diagnostic resolution. Multispectral imagery inspections linked with electrical signature, synchronisation of field techniques with monitoring.
• Apply edge AI and Big Data to improve the energy yield (advanced module control, self-reconfigurable topologies, etc.), module and plant models, monitoring and yield forecasting considering user behaviour and modelling of the entire electricity system including storage.
• Build large (time and scale-wise), wide (including not only yield but multisensory operational, thermal, mechanical and environmental data) and possibly publicly available datasets to enable, foster and empower AI for Digital PV at European scale.
• Demonstrate automated and predictive PV asset management software based on sensor-data-image fusion and/or AI / Machine learning techniques to reduce human effort and increase trustworthiness of current PV asset management software.
• Enable AI-based energy trading at plant level, taking care of specific climates /applications / conditions (snow, dust, environmental pollution, water…)/user behaviours.