The rapid spread of artificial intelligence is creating notable new electricity demand, while also offering tools to operate power networks more efficiently and manage increasingly complex energy systems. Feldmájer Benjámin, Schneider Electric’s Central and Eastern Europe head for sustainability and infrastructure, made these points at a business event in Budapest.
Growing compute needs and broader electrification impacts
As machine-learning and other AI systems proliferate, they require ever more and larger computing capacity, which in turn drives increased electricity consumption in data centres. Beyond data centres, the energy system is also strained by advances in electric mobility, electrification of industrial processes, wider adoption of heat pumps and expanded local generation. Together, these developments place new demands on grids.
"Artificial intelligence is indeed an energy-intensive technology, so its expansion poses a serious challenge to the energy system. At the same time, it can enable major advances in energy efficiency, forecasting, operations and grid management," said Feldmájer Benjámin.
Physical grid expansion alone is insufficient
The growing share of renewable generation and more decentralized production are fundamentally changing how power systems operate. Instead of a largely centralised generation model, more weather-dependent and distributed producers connect to the grid. Electricity flows therefore become multi-directional and coordinating generation and consumption far more complex. For this reason, simply increasing physical infrastructure capacity is no longer enough: the system needs real-time data, intelligent networks, automated control and remotely controllable endpoints so operators and users can react quickly to changes in consumption, generation or network load.
The role of AI-driven solutions
AI-based solutions can analyse large volumes of operational and energy data to forecast demand, optimise usage, detect anomalies and support predictive maintenance. Automated grid control can speed up fault detection and resolution while improving supply security and asset utilisation.
Three interdependent technological shifts
According to Feldmájer, the new energy era requires parallel progress in electrification, automation and digitalisation. Electrification enables replacing fossil-fuel-based processes with electric alternatives; automation allows systems to adapt rapidly and effectively to changing conditions; and digitalisation connects devices, makes operations visible and creates the data foundation for intelligent decision-making.
Data and architecture: EcoStruxure
Effective energy management depends on linking consumption, operational and device-level data. Schneider Electric’s open and interoperable EcoStruxure architecture connects these data layers from connected equipment through control systems to analytical applications, making the operation of buildings, industrial sites, data centres and infrastructure more transparent and optimisable.
Local generation, storage and community solutions
In addition to intelligent management, local generation and storage, microgrids and energy communities can play important roles. They can reduce load on central grids, increase utilisation of locally produced renewable energy and add flexibility to consumption patterns. With AI support, expected production, demand and price signals can be continuously balanced to optimise energy flows.
"If we combine artificial intelligence with appropriate data, digital infrastructure and actuable intelligent devices, the entire energy system can become more efficient, flexible and sustainable," Feldmájer concluded.
