Unlocking grid capacity in Australia

AI-enabled optimisation, supported by real-time data, can optimise generation capacity on the grid and behind the meter – increasing supply from generators.

Across commercial, residential and industrial sectors, AI-enabled optimisation can improve electricity efficiency by using real-time data to make more responsive and better-coordinated decisions – reducing demand from consumers.

AI has long played a role in supporting supply-side and demand-side optimisation in energy systems, typically through rule-based automation, static scheduling and other conventional control methods. The shift now underway is toward more data-driven AI applications, enabled by growing access to real-time operational data from sensors, control systems and market platforms. These approaches can be better suited to many energy systems because their performance is shaped by numerous interacting variables, nonlinear behaviour, and operating conditions that change over time, making them difficult to optimise through fixed rules or simplified analytical models alone. By learning from historical and real-time data, AI can identify patterns across these complex interactions, anticipate changing conditions, and continuously improve decision-making.

Cloud computing is central to the growth and deployment of AI. It provides the scalable, flexible and high-performance computing environment that AI systems require. Unlike traditional on-site or fixed hosted infrastructure, cloud platforms allow users to draw on large volumes of processing power, data storage and specialised hardware, such as GPUs, on demand. This is particularly important for AI, where workloads are often computationally intensive, data-heavy and highly variable over time. By enabling rapid access to advanced compute resources and allowing capacity to expand or contract as needed, cloud computing makes AI development and deployment more practical, efficient and economically accessible.

On the supply side, as more generation comes from variable and decentralised sources, the challenge is not just producing electricity, but coordinating it, predicting it, and using it at the right time. In that context, AI can help improve the usable output of wind and solar, reduce avoidable curtailment, and better orchestrate distributed energy resources such as rooftop solar, batteries and electric vehicles. Its value lies less in creating new generation than in supporting a distributed system that relies more on variable renewable generation to operate in a more coordinated and flexible way.

The strongest opportunities for demand are in sectors where electricity use is both large and difficult to optimise through fixed rules or manual intervention. In buildings and homes, AI can continuously adjust loads such as heating, cooling and appliances in response to changing conditions, reducing waste without materially affecting comfort. In mining and manufacturing, the opportunity differs: AI can improve the performance of energy-intensive processes whose efficiency depends on many interacting  variables, making small operational improvements that can translate into meaningful energy savings at scale. Across these settings, the common value of AI is not simply automation, but more adaptive and data-driven control of complex electricity use.

Market adoption and policy settings will play a material role in determining whether AI-enabled energy saving solutions are achieved at scale, and whether their full potential is realised in practice. A consistent requirement for viability is access to timely, high-quality operational data, because optimisation and automated control perform best when they can respond to changing conditions in near real time. Uptake will also be shaped by the readiness of existing infrastructure, the cost and disruption of retrofits, and the organisational capability needed to operate more data-driven systems. Where energy-saving solutions intersect with worker or consumer safety, including in mining and food production, deployment will need to align with relevant safety standards, balancing efficiency gains against clear assurance, oversight, and risk controls.