AI-enabled reliability and yield increase for utility-scale PV

Summary

The project will use AI and advanced testing to improve the reliability and performance of utility-scale solar PV.

Need

As solar farms grow, operators need to better understand how PV modules perform over time. Degradation, lower energy yield and uncertainty about long-term performance can increase project risk and costs. Better testing, modelling and forecasting tools can help improve confidence in advanced solar technologies and support lower-cost solar electricity.

Action

ANU will work with Runergy and PV Lab Australia to develop more reliable TOPCon back-contact PV modules. The project will also build outdoor testing capability and create AI-enabled tools to forecast degradation, model performance and improve cell-to-module design.

Outcome

The project aims to improve the reliability, energy yield and commercial readiness of advanced PV technologies. If successful, the technologies developed in the R&D stage may be integrated into manufacturing and commercial applications, including enhanced testing services and AI-enabled solar analytics products.

Additional Impact

The project is expected to share new knowledge about PV module reliability, performance testing and AI-enabled solar analytics. This may help build Australian expertise in advanced solar PV and support wider adoption of more reliable, lower-cost solar technologies.