Energy retailing in Australia has traditionally been a crude business: buy wholesale, add a margin, sell retail. The forecasting was basic — historical consumption data, seasonal patterns, and a healthy dose of conservative overpricing to manage risk.

That’s changing. The smarter retailers are now using AI and machine learning to predict demand at much finer resolution, and it’s starting to affect the products they offer consumers.

What’s happening behind the scenes

Retailers need to forecast demand to purchase wholesale electricity in advance. If they underestimate demand, they have to buy expensive spot market power to cover the shortfall. If they overestimate, they’ve bought power they don’t need.

Traditionally, retailers used statistical models based on weather forecasts, historical consumption, and seasonal trends. These models work reasonably well in aggregate but miss granular patterns — like the spike in consumption when a popular TV show starts, or the drop when schools break for holidays.

Machine learning models trained on millions of data points (smart meter readings, weather data, calendar events, even social media signals) can predict demand with much higher accuracy. The best models now predict next-day demand within 2-3% accuracy, compared to 5-8% for traditional methods.

Why it matters for consumers

Better demand forecasting benefits consumers in several ways:

Lower risk margins in pricing. When retailers can predict demand more accurately, they need smaller safety margins in their pricing. In theory, this should lead to lower retail prices. In practice, competitive pressure is needed to ensure the savings are passed through.

More dynamic pricing products. Amber Electric’s wholesale pass-through model only works because of sophisticated demand forecasting and risk management. Without AI, offering consumers real-time wholesale pricing would be too risky for the retailer.

Personalised tariffs. Some retailers are experimenting with tariff structures tailored to individual consumption patterns. If the retailer knows (through AI analysis of your smart meter data) that you’re a low-risk customer with consistent, predictable consumption, they can offer you a better rate.

The companies working on this technology include both the big retailers (AGL, Origin, EnergyAustralia all have data science teams) and specialist firms. AI consultants in Brisbane and other Australian cities are working with energy companies to implement these machine learning systems, bridging the gap between academic research and operational deployment.

The Amber Electric model

Amber deserves special mention because they’ve built their entire business around AI-driven energy management. Their platform:

1. Passes wholesale prices directly to consumers (with a small subscription fee)
2. Uses algorithms to forecast price movements and advise consumers when to use power
3. Automatically shifts battery charging and EV charging to low-price periods
4. Participates in demand response programs on behalf of customers

The result is that engaged Amber customers can save 25-40% compared to standard retail offers. The savings come from exploiting price patterns that are invisible on a flat retail tariff.

Smart meters are the foundation

All of this depends on smart meters, which record consumption in 15-30 minute intervals and transmit data to distributors and retailers. Victoria completed its smart meter rollout years ago. Other states are catching up but still have millions of old accumulation meters that only record total consumption.

If you still have an old meter and your retailer or distributor offers a free smart meter upgrade, take it. You can’t access time-of-use tariffs, real-time monitoring, or AI-driven energy products without one.

Privacy considerations

Detailed consumption data reveals a lot about your life — when you’re home, when you’re sleeping, when you’re cooking. Retailers using AI to analyse this data need to handle it responsibly.

Australian privacy law applies, and the Consumer Data Right (CDR) is being extended to energy, giving consumers more control over their data. But the practical reality is that most consumers click through terms and conditions without reading them.

If privacy concerns you, look for retailers who are transparent about how they use your data and give you meaningful control. Amber’s privacy policy is relatively clear on this front. Some of the bigger retailers are less forthcoming.

Where this is heading

In five years, I expect energy retail to look very different. AI-driven demand forecasting will enable:

• Real-time personalised pricing for every household
• Automated demand response where your appliances respond to price signals without your intervention
• Predictive maintenance alerts for your solar and battery systems
• Integrated energy-as-a-service platforms that manage your whole energy ecosystem

The boring world of electricity retail is becoming surprisingly sophisticated. And for consumers willing to engage with smarter products, the savings are real and growing.