Maximising solar ROI: Advanced diagnostics for PV systems

Chief Investigators

Dr Fiacre Rougieux (UNSW)

Dr Ibrahim Ibrahim (UTS)

Chris Martell (GSES)

Purpose of project

Australia is rapidly transitioning to renewable energy, with solar power playing a key role. However, the performance of rooftop solar photovoltaic (PV) systems, especially for the commercial and industrial sectors, is often hindered by issues such as component degradation, shading, and grid instability. This project aims to develop advanced diagnostic tools using data analytics and machine learning to enhance PV system reliability and safety, reduce operational costs, and maximise energy yield.

By addressing these challenges, we can accelerate solar adoption, ensure grid stability, and drive down energy costs for Australian businesses. 

Impact of project

Current PV diagnostic systems provide only high-level insights, limiting their effectiveness. This research project offers detailed fault detection and actionable insights, allowing for proactive maintenance and optimisation. Unlike conventional monitoring tools, our approach will enable a much more granular fault detection and diagnosis with directly actionable information. In the short term, this will increase solar system reliability, reduce maintenance costs, and improve energy output. By 2035, the project aims to generate up to $179.5 million in customer savings and cut 2.3 million tonnes of CO₂ emissions annually. The broader impact includes greater confidence in solar investments, enhanced grid integration, and positioning Australia as a global leader in renewable energy solutions.

The output of this project will be a diagnostic and fulfillment tool which would be of interest and applicability to a diverse group of industry stakeholders, including solar system operators, utility companies, researchers, green investors and insurers, policy makers and equipment manufacturers. Key partners include major Australian businesses such as Woolworths, ALDI, and Coles, as well as councils and government agencies. Through collaboration, we aim to drive innovation, enhance energy productivity, and contribute to a sustainable, low-carbon future for Australia.

Work packages

Work package 1: Data Acquisition, Processing, and Fault Pattern Analysis

Project Output: Data ingestion pipelines, AWS central research data infrastructure, statistical and machine learning fault pattern identification models.

Work package 2: Diagnostic Model Development and Analysis

Output: Degradation and diagnostic model prototypes, analysis, impact quantification, including savings calculations.

Work package 3: Pilot Deployment, Validation, and Refinement

Output: Pilot deployment case studies, test reports from partner commercial solar sites, refined diagnostic models and tools based on feedback and performance data.

Work package 4: Software Integration, Platform Development, and Large-scale Deployment

Output: Software platforms with integrated diagnostic tools, optimised efficiency, and deployment across diverse PV systems.

Work package 5: Recommendations, Reporting, and Knowledge Transfer

Output: Root cause analysis, specific recommendations for corrective actions, implementation strategies, final project report, knowledge base of research outputs, models, and best practices, handover of data, models, and technology to industry partners.

Project partners – industry and research

UNSW (Lead), UTS (Co-lead), Green Peak Energy, GSES