Australia’s net zero emissions target by 2050 is driving a shift to renewable energy amid rising demand, especially from data centres, which could consume eight per cent of national electricity by 2030.
While artificial intelligence (AI) contributes to this demand, it also offers solutions – enhancing grid efficiency, forecasting and real-time responsiveness. AI at the edge of the grid enables faster, localised decision-making, helping utilities manage energy more effectively.
Companies like Itron are already deploying smart technologies to optimise infrastructure, making AI a key enabler of a smarter, more sustainable energy future.
Australia has passed into law its target to achieve net zero greenhouse gas emissions by 2050. To achieve this target, the country is undertaking a transition in its energy sector through significant adoption of grid connected renewable energy combined with residential roof top solar.
This is in parallel with overall energy demand increases that underpin continued economic development and living standards for all.
A key contributor to increased energy demand are data centres. The International Energy Agency (IEA) predicts that due to soaring AI and cloud growth, data centres could be drawing as much power as the entire industrial sectors by 2030.
In Australia alone, data centres already consume five per cent of the nation’s electricity. That could rise to eight per cent over the next five years as Australia’s data centre industry, which is among the top five worldwide in built-out capacity, continues to boom.
AI’s dual role in Australia’s energy landscape
Herein lies a paradox: AI is both fuelling the demand for more energy, while simultaneously offering solutions to help manage it.
The energy transition from fossil fuelled base load generation to renewables that enable a net zero emissions future requires Australia to utilise the most of the infrastructure it already has. Energy efficiency – maximising every unit of energy generated and consumed – is becoming just as critical as building new capacity.
Here, AI can play a transformative role.
By enabling smarter forecasting, improving energy load balancing, optimising the performance of distributed energy resources and enhancing grid responsiveness in real time, AI can help extract more value from existing infrastructure.
In short, the very technology contributing to these energy challenges could also be key to solving them.
How AI and machine learning transform the grid at the edge
Utilities recognise the potential applications of AI.
According to the Itron Resourcefulness Insight report, predictive maintenance (42 per cent), energy savings (40 per cent), and demand forecasting (37 per cent) are leading their list of AI or machine learning (ML) priorities.
Modern utilities need real-time visibility, control at scale, and the kind of flexible, creative solutions that can pivot with the future.
These are the very capabilities that AI and ML powered intelligence at the low-voltage (LV) network edge provide. Having AI at the edge, right at the source of signals, reduces latency so high-frequency data can be acted on instantly.
To date, Itron has delivered over 13 million distributed intelligence-enabled endpoints globally with innovations:
- Meter bypass predicts if a meter has been bypassed using voltage, current and distributed intelligence (DI) data
- Electric vehicle (EV) detection identifies EV usage patterns through ML
- EV/solar awareness categorises when EVs and solar are in use by analysing load data
- Active temperature monitoring detects abnormal temperature conditions using machine learning and data from surrounding meters
- Anomaly detection flags irregularities and sends alerts for further investigation
Building a smarter grid, one node at a time
In the end, AI and ML is only as effective as the data it’s given, and that data needs to be accurate, timely and relevant.
Therefore, optimising energy using AI and ML at scale requires:
- Location awareness: LV sensors need to be able to work together to make sense of what’s happening on the grid, much like how computer vision processes visual information
- Low latency communication links: Fast, direct communication between sensors is what enables a quick and coordinated response.
- Hyperlocal control activity: Edge DERMS (distributed energy resource management system) handle local issues directly in real-time while still keeping central systems in the know
This means that instead of sending everything back to a central brain (taking up valuable time), each local node can think for itself and talk to its neighbours. It’s a faster, smarter, and far more resilient way to manage the grid, especially when things get unpredictable.
The energy landscape in Australia is changing fast. In the next article, Itron digs further into the growing role of AI in addressing future energy demands and how consumer energy resources, such as EVs, and the grid will need to “talk” to each other to ensure smarter energy management and realise a greener future.
To learn more, visit aunz.itron.com
