About
SolarisAI (Solarisᴬᴵ) is an enterprise-grade Software-as-a-Service platform purpose-built for solar farm operators and large-scale photovoltaic (PV) asset owners. Developed at the University of Queensland (UQ) in partnership with leading energy companies such as Edify Energy, Weidmuller, Gentari Solar Australia, and Genex Power, it brings research-grade AI directly into day-to-day solar operations and maintenance (O&M) workflows. The platform ingests raw customer site data, applies pre-cleaning and pre-filtering processes to ensure data accuracy, and then runs advanced algorithms to identify faults, soiling events, and underperformance across an entire solar portfolio. Predictive maintenance models analyse historical and real-time data to forecast future failures before they cause costly downtime, enabling proactive intervention rather than reactive repair. SolarisAI also provides an AI-based market insights tool that supports strategic bidding decisions, helping operators optimize revenue alongside physical asset performance. Flexible customer data analysis reports can be delivered at custom intervals, giving operations teams the insights they need without requiring deep in-house data science expertise. Deployment options include fully managed cloud-based O&M services and customer-integrated SaaS configurations that connect directly with existing site data systems. The platform is best suited for utility-scale solar asset managers, independent power producers, and O&M service providers who need a scalable, intelligent solution to improve energy yield and reduce lifecycle costs across their solar portfolios.
Key Features
- AI-Driven Predictive Maintenance: Uses machine learning models to analyse site data and forecast equipment failures before they occur, minimising unplanned downtime and repair costs.
- Fault Detection & Underperformance Analysis: Continuously monitors solar farm performance to automatically detect faults, soiling events, and output degradation across the entire PV installation.
- Customer Data Prefiltering Software: Applies pre-cleaning algorithms to raw customer site data, ensuring high data quality before analytics are run to avoid garbage-in, garbage-out errors.
- AI-Based Market Insights & Bidding Tool: Provides AI-powered market intelligence to help solar operators make smarter bidding and revenue optimization decisions in energy markets.
- Flexible Cloud & Integrated SaaS Deployment: Supports both fully managed cloud-based O&M delivery and direct integration with customer data infrastructure for seamless enterprise adoption.
Use Cases
- Utility-scale solar farm operators using AI to detect panel faults and soiling losses early, reducing energy yield losses across large PV portfolios.
- O&M service providers leveraging predictive maintenance models to schedule field interventions before failures occur, cutting reactive repair costs.
- Independent power producers using the AI-based market insights tool to optimize bidding strategies and maximize revenue from energy markets.
- Solar asset managers receiving periodic data analysis reports to monitor fleet performance and demonstrate output guarantees to investors.
- Solar project developers integrating SolarisAI's prefiltering software into their data pipelines to ensure clean, reliable operational data from day one.
Pros
- Research-Backed Technology: Developed at the University of Queensland with real-world validation through partnerships with major solar energy companies, lending scientific credibility to the platform.
- End-to-End O&M Coverage: Covers the full operations and maintenance lifecycle — from data ingestion and cleaning through fault detection, predictive maintenance, and market strategy — in a single platform.
- Flexible Deployment Options: Offers both fully managed SaaS and customer-integrated configurations, making it adaptable to different IT environments and operator preferences.
- Proven Industry Partnerships: Tested and refined with established energy businesses, giving operators confidence that the platform handles real-world solar farm complexities.
Cons
- Narrow Domain Focus: Designed exclusively for solar farms and large-scale PV installations, so it cannot be used for other renewable energy asset classes like wind or storage without customisation.
- Enterprise-Oriented Pricing: As a B2B SaaS platform targeting utility-scale operators, pricing and onboarding are likely not accessible to smaller residential or community solar projects.
- Limited Public Documentation: Detailed technical specifications, integration documentation, and pricing information are not publicly available on the website, requiring direct engagement with the sales team.
Frequently Asked Questions
What types of solar installations does SolarisAI support?
SolarisAI is designed for solar farms and large-scale photovoltaic (PV) installations. It is best suited for utility-scale and commercial solar assets managed by independent power producers, asset managers, or O&M service providers.
How does SolarisAI's predictive maintenance work?
The platform ingests historical and real-time operational data from solar sites, applies pre-filtering to ensure data quality, and then runs advanced AI algorithms to identify patterns that precede equipment failures. This enables operators to schedule maintenance proactively rather than reacting to unexpected breakdowns.
Is SolarisAI a cloud-based platform?
Yes. SolarisAI is available as a fully cloud-based SaaS service, and also offers a customer-integrated configuration that connects directly with a client's existing data infrastructure and systems.
Who developed SolarisAI?
SolarisAI was developed at the University of Queensland (UQ) in collaboration with industry partners including Edify Energy, Weidmuller, Gentari Solar Australia, and Genex Power.
What is the AI-based market insights tool?
SolarisAI includes an AI-powered bidding and market strategy tool that helps solar farm operators make data-driven decisions in energy markets, optimizing revenue generation alongside physical asset performance.
