One of the biggest challenges with solar energy is its variability. A cloud passes over and your output drops 80% in seconds. Multiply that by millions of rooftop systems and you’ve got a grid operator’s nightmare. But AI-driven solar forecasting is changing this picture significantly, and it’s one of the most genuinely exciting applications of machine learning in the energy sector.

The forecasting challenge

AEMO needs to balance electricity supply and demand in real-time across the National Electricity Market. With coal and gas, generation is controllable — you can ramp up or down on command. With solar, you’re at the mercy of clouds, haze, and weather patterns.

For a single rooftop system, variability doesn’t matter much. But Australia now has over 20GW of distributed solar capacity. On a sunny day, that’s equivalent to several large power stations. When a cloud band moves across a city, gigawatts of generation can disappear in minutes. AEMO needs to predict this to dispatch backup generation in time.

How AI forecasting works

Modern solar forecasting combines multiple data sources:

Satellite imagery. Cloud tracking from weather satellites can predict when clouds will move over solar-dense areas. AI models trained on historical satellite-to-generation correlations can translate cloud cover into expected output changes.

Ground-level sensors. Irradiance sensors and sky cameras at key locations provide real-time ground truth data. Machine learning models use this to calibrate satellite predictions.

Historical weather patterns. Deep learning models trained on years of weather and generation data can identify patterns that traditional weather models miss. For example, sea breeze effects that reliably bring afternoon cloud to coastal areas.

Smart inverter data. This is the fascinating new frontier. Real-time production data from internet-connected inverters (millions of them in Australia) provides an incredibly dense network of “ground truth” sensors. If production from inverters in western Sydney drops suddenly, the system can predict that the same cloud will affect eastern Sydney minutes later.

AEMO already uses some of these techniques, and the accuracy improvements have been dramatic. Five-minute-ahead forecasts are now accurate to within a few percent, and hour-ahead forecasts have improved by roughly 30% compared to traditional methods.

Who’s doing the interesting work

The Australian Renewable Energy Agency (ARENA) has funded several solar forecasting projects. Solcast (now part of DNV) is probably the best-known Australian solar forecasting company, providing data to AEMO and energy companies worldwide.

On the commercial side, companies like AI consultants in Melbourne are helping energy businesses implement machine learning models for demand forecasting and grid management. The intersection of AI and energy is producing genuinely useful tools, not just marketing hype.

Universities including UNSW and ANU have active research programs in solar forecasting, and their work feeds directly into operational tools used by grid operators.

What it means for solar owners

Better forecasting doesn’t directly change your electricity bill, but it has indirect benefits:

Fewer curtailment events. When AEMO can accurately predict solar output, they need less conservative safety margins. This means fewer situations where they need to curtail rooftop solar exports as a precaution.

Better battery optimisation. Forecasting accuracy feeds directly into battery management algorithms. If your battery management system knows that clouds will reduce your solar output at 2pm, it can delay charging until the afternoon sun breaks through rather than topping up from the grid prematurely.

More efficient grid dispatch. Better forecasting means less reliance on expensive, fast-start gas generators as backup. This should, over time, put downward pressure on wholesale prices, which filters through to retail rates.

Smarter VPP operation. Virtual power plants benefit enormously from accurate forecasting. If the VPP operator knows a cloud band will hit Brisbane at 11am, they can pre-position batteries to export during the resulting price spike rather than reacting after the fact.

The privacy question

Using inverter data for forecasting raises legitimate privacy questions. When your inverter reports production data, that information reveals something about your home — when you’re consuming power, how much you’re generating, and by inference, when you’re home.

Most inverter data used for forecasting is aggregated and anonymised, but the underlying data streams exist and could theoretically be misused. This is worth being aware of, though the practical risk is low.

Where this is heading

In the next few years, I expect solar forecasting to become so accurate that grid operators can treat distributed solar as dispatchable resource rather than an intermittent one. Not because the sun becomes predictable, but because the combination of forecasting and distributed battery storage makes the overall system controllable.

That’s a profound shift. It means rooftop solar transitions from being a grid management headache to being a grid management asset. And that benefits everyone — solar owners, non-solar households, and the energy system as a whole.

The AI applications in energy aren’t as flashy as chatbots or image generators, but they might end up being more consequential for daily life. Better forecasting means a more stable, cheaper, cleaner grid. And that’s something worth getting excited about.