AI and Energy: Insights from IEA's New Observatory
The International Energy Agency (IEA) has generated considerable interest among technology executives with its launch of the Energy and AI Observatory.
This cutting-edge initiative aims to bridge the gap between technological advancements in AI and their implications on the energy sector by providing up-to-date data and thorough analysis.
The IEA says: “There has been a step change in the capabilities of AI, driven by falling computation costs, a surge in data availability and technical breakthroughs.
“There is no AI without energy; at the same time, AI has the potential to transform the energy sector.”
Energy for AI and the need for data
Addressing the evolving needs of the AI landscape, the IEA notes that “new and fast-moving field of AI requires a new approach to gathering data and information”.
The observatory's mission is to deliver the most recent data, offering a 'comprehensive view of the implications of AI on energy demand (energy for AI) and of AI applications for efficiency, innovation, resilience and competitiveness in the energy sector (AI for energy)”.
The IEA, in collaboration with partners from the energy and tech industries, developed and sustains this observatory.
The observatory features global data centre capacity and power use maps, accompanied by various case studies that demonstrate AI application models.
Despite the crucial role that data centre electricity consumption plays in understanding AI's impact on energy demand, the IEA points out the lack of "comprehensive global statistics on the electricity consumption of data centres."
It adds: "The IEA has developed a global model that enables it to provide estimates of data centre electricity consumption by region and across time."
Challenges with data centres
Acknowledging the rise of data centres essential for AI development, the IEA states that investments in these centres have surged, with large-scale clusters appearing across North America, Europe and Asia Pacific.
It adds: “AI is making data centres larger and more power-intensive, raising the importance of the availability of electricity generation capacity and grids in the locational decision-making of data centres.
“However, the existing infrastructure, policy frameworks and talent pools that enabled the top markets to flourish have created momentum that continues to draw new data centre development.”
IEA says that, as a result, there is a critical need for grid operators and policymakers to “understand how the data centre pipeline is evolving”.
Algorithms, processes and examples
The observatory includes 19 case studies spanning buildings, industry, power supply and transport.
These studies showcase how AI enhances efficiency, cost-effectiveness and competitiveness while promoting innovation and resilience in the energy sector.
Among these studies is Hitachi Energy's exploration of AI-driven forecasting for improved energy pricing accuracy, utilising advanced machine learning algorithms.
Across six teams, a spectrum of methods and algorithms were examined, resulting in algorithms like the Nostradamus AI that attain high accuracy levels, surpassing industry norms for real-time energy price forecasting.
The processes and algorithms developed achieved 87% accuracy for CAISO locational marginal prices, 93% accuracy for MISO locational marginal prices and 86% accuracy for MISO ancillary service prices, compared to the industry expectation of 75% accuracy for predicting hourly, real-time, wholesale energy prices in US markets.
These modelling framework enhancements and algorithms have been built into a forecasting software solution called Nostradamus AI, enabling users to generate forecasts without any data science training.
Tech's role
IBM and Google are notable contributors to the observatory's case studies.
IBM’s Chief Sustainability Officer, Christina Shim, expresses enthusiasm about IBM's role.
“I’m delighted that IBM contributed by sharing our work on the Electricity Access Forecasting AI model.
“The model was co-developed by IBM and UNDP and built on IBM watsonx, IBM Cloud and an open-source machine learning library.
“It projects electricity access through 2030 across 102 Global South countries by evaluating drivers like population density, existing grid and off-grid infrastructure, urbanisation rates, terrain elevation and night-time satellite imagery, augmented with land use data from IBM Environmental Intelligence.”
Google CSO Kate Brandt, Sustainability Magazine’s 2025 number one woman in sustainability, said: “The International Energy Agency (IEA)’s new, first-of-its-kind Energy and AI Observatory creates a single, comprehensive reference, gathering crucial data and providing a global, informed vision on the impact of AI on the energy sector.
“Delighted that two of Google’s AI-powered solutions are featured.”
They are:
MethaneSAT
Kate says: “Monitoring methane emissions at scale has been a major challenge in identifying and reducing their sources.
“Google’s partnering with the Environmental Defense Fund on a new satellite, MethaneSAT, which can detect methane emissions from oil and gas production more accurately and precisely than ever before.”
Tapestry
Kate says: “X‘s moonshot for the electric grid is using AI-powered tools to help partners like CEN, Chile’s national grid operator, make grid planning smarter, faster and easier to help achieve its ambitious goal of carbon neutrality by 2050.
“I'm excited to see the many applications of how AI is being used today and new additions to the observatory over time.”
