When you think about AI in energy, you might think of smart home gadgets or chatbots. But some of the most impactful AI applications are happening at industrial scale — on the massive solar farms that dot regional Australia.

I spent a day at a solar farm near Dalby, Queensland, last month, and the sophistication of the technology running these operations genuinely surprised me.

The scale of utility solar in Australia

Australia now has over 10GW of operational large-scale solar capacity, spread across farms ranging from 50MW to over 500MW. These facilities cover hundreds of hectares, contain hundreds of thousands of panels, and generate enough electricity to power small cities.

At this scale, even small efficiency improvements translate into millions of dollars. A 1% improvement in output from a 200MW farm is worth roughly $500,000 per year in additional revenue. This is why operators are investing heavily in AI-driven optimisation.

What AI does on a solar farm

Predictive maintenance. Each panel and inverter has performance characteristics that AI models can track. When a panel starts degrading faster than expected, or an inverter shows early signs of failure, the system flags it for inspection before it fails completely. This prevents costly downtime and extends equipment life.

Traditional maintenance follows a fixed schedule — inspect every panel every six months, for example. AI-driven maintenance inspects based on need, directing maintenance crews to the specific panels or inverters showing anomalies. This is both cheaper and more effective.

Soiling detection and cleaning optimisation. Dust accumulation on solar farms varies across the site based on terrain, wind patterns, and proximity to roads or agricultural land. AI models trained on performance data can identify which sections need cleaning and when, optimising the expensive robotic or manual cleaning operations.

Tracking system optimisation. Many solar farms use single-axis trackers that rotate panels to follow the sun throughout the day. The optimal tracking angle isn’t always “point directly at the sun” — diffuse light conditions, cloud cover, and inter-row shading can make alternative angles more productive. Machine learning models continuously adjust tracking algorithms based on real-time conditions.

Weather integration. Advanced weather forecasting integrated with farm management systems allows operators to anticipate output changes and manage grid dispatch commitments. If a storm front is approaching, the system can pre-position trackers to minimise hail damage or optimise the final hours of production before cloud cover arrives.

The Australian companies leading this

Several Australian firms are doing interesting work in solar farm AI:

Fulcrum3D: Their CloudCAM system uses sky-imaging cameras and AI to predict cloud shadows across solar farm sites, enabling short-term generation forecasting accurate to the minute-level.

Solcast (DNV): Provides irradiance forecasting data that feeds into farm management systems across the world. Their Australian roots mean their models are particularly well-calibrated for Australian conditions.

Power Factors and Also Energy: Offer portfolio management platforms that use machine learning to benchmark and optimise performance across multiple solar assets.

Broader AI capabilities are being brought to the sector by firms like AI consultants in Brisbane, who help energy companies implement machine learning solutions for operational optimisation across their asset portfolios. The crossover between general AI expertise and energy-specific domain knowledge is producing increasingly sophisticated tools.

Lessons for residential solar

Some of the techniques used at utility scale are trickling down to residential:

Performance benchmarking. Comparing your system against similar systems in your area (via PVOutput or Solar Analytics) is the residential equivalent of utility-scale AI benchmarking.

Predictive alerts. Solar Analytics and Enphase both offer performance anomaly detection that flags potential issues before they become failures.

Weather-responsive optimisation. Battery management systems from Tesla and Reposit already use weather forecasts to optimise charging and discharging. This is directly descended from utility-scale forecasting work.

The bigger picture

Solar farms are becoming genuinely smart infrastructure. They’re not just fields of panels sitting in the sun — they’re complex systems continuously optimised by algorithms that learn from millions of data points.

This matters for the energy transition because it means solar is getting cheaper not just through panel cost reductions, but through operational efficiency improvements. The cost of solar energy from a well-optimised farm today is lower than the cost from the same farm five years ago, even with identical hardware, because the software is better.

That’s the kind of compounding improvement that makes renewable energy increasingly unbeatable. And it’s happening right here in Australia, on farms most people will never visit, managed by AI that most people will never see.