Artificial intelligence (AI) is not just transforming technology; it’s also significantly changing the global energy sector. According to the latest report from the International Energy Agency (IEA), AI’s rapid growth, particularly in data centers, is causing a significant rise in electricity demand. At the same time, AI also offers opportunities for the energy sector to become more efficient, sustainable, and resilient. This shift is expected to significantly transform the way we generate, consume, and manage electricity.
The Growing Electricity Demands of AI
One of the most immediate impacts AI is having on global electricity consumption is the growth of data centers. These facilities, which provide the computational power needed to run AI models, are already major consumers of electricity. As AI technologies become more powerful and widespread, the demand for computing power — and the energy required to support it — is expected to increase significantly. According to the report, the electricity consumption of data centers is projected to exceed 945 TWh by 2030, more than double the levels seen in 2024. This increase is mainly driven by the rising demand for AI models that require high-performance computing, particularly those using accelerated servers.
Currently, data centers consume about 1.5% of global electricity. However, their share of global electricity demand is expected to grow significantly over the next decade. This is primarily due to AI’s reliance on specialized hardware like GPUs and accelerated servers. The energy-intensive nature of AI will play a key role in determining the future of electricity consumption.
Regional Variations in AI’s Energy Impact
Electricity consumption from data centers is not evenly distributed worldwide. The United States, China, and Europe account for the largest share of global data center electricity demand. In the U.S., data centers are expected to contribute to nearly half of the country’s electricity demand growth by 2030. Meanwhile, emerging economies such as Southeast Asia and India are experiencing rapid data center development, though their demand growth remains lower compared to developed countries.
This concentration of data centers poses unique challenges for electricity grids, especially in regions where infrastructure is already under strain. The high energy demands of these centers can lead to grid congestion and delays in connecting to the grid. For instance, data center projects in the U.S. have faced long wait times due to limited grid capacity, a problem that could worsen without proper planning.
Strategies to Meet AI’s Growing Energy Demands
The IEA’s report suggests several strategies to meet the growing electricity demands of AI while ensuring grid reliability. One key strategy is diversifying energy sources. While renewable energy will play a central role in meeting the increased demand from data centers, other sources such as natural gas, nuclear power, and emerging technologies like small modular reactors (SMRs) will also contribute.
Renewables are expected to supply nearly half of the global growth in data center demand by 2035, due to their economic competitiveness and faster development timelines. However, balancing the intermittent nature of renewable energy with the constant demand from data centers will require robust energy storage solutions and flexible grid management. Additionally, AI itself can play a role in enhancing energy efficiency, helping to optimize power plant operations and improve grid management.
AI’s Role in Optimizing the Energy Sector
AI is also a powerful tool for optimizing energy systems. It can enhance energy production, lower operational costs, and improve the integration of renewable energy into existing grids. By using AI for real-time monitoring, predictive maintenance, and grid optimization, energy companies can increase efficiency and reduce emissions. The IEA estimates that widespread AI adoption could save up to $110 billion annually in the electricity sector by 2035. The IEA report also highlights several key applications of how AI can improve efficiency of demand and supply in the energy sector:
- Forecasting Supply and Demand: AI enhances the ability to predict renewable energy availability, which is essential for integrating variable sources into the grid. For example, Google’s neural network-based AI has increased the financial value of wind power by 20% through accurate 36-hour forecasts. This enables utilities to better balance supply and demand, reducing reliance on fossil fuel backups.
- Predictive Maintenance: AI monitors energy infrastructure, such as power lines and turbines, to predict faults before they lead to outages. E.ON reduced outages by up to 30% using machine learning for medium-voltage cables, and Enel achieved a 15% reduction with sensor-based AI systems.
- Grid Management: AI processes data from sensors and smart meters to optimize power flow, especially at the distribution level. This ensures stable and efficient grid operations, even as the number of grid-connected devices continues to grow.
- Demand Response: AI allows for better forecasting of electricity prices and dynamic pricing models, encouraging consumers to shift usage to off-peak times. This reduces grid strain and lowers costs for both utilities and consumers.
- Consumer Services: AI enhances customer experience through apps and chatbots, improving billing and energy management. Companies like Octopus Energy and Oracle Utilities are leading examples of this innovation.
Additionally, AI can help decrease energy consumption by improving the efficiency of energy-intensive processes, such as power generation and transmission. As the energy sector becomes more digitized, AI will play a crucial role in balancing supply and demand.
The Challenges and Way Forward
While the integration of AI into the energy sector holds great promise, uncertainties still exist. The speed of AI adoption, advancements in AI hardware efficiency, and the ability of energy sectors to meet increasing demand are all factors that could influence future electricity consumption. The IEA’s report outlines several scenarios, with the most optimistic projection indicating a demand surge of over 45% beyond current expectations.
To ensure that AI’s growth doesn’t outpace the energy sector’s capacity, countries will need to focus on enhancing grid infrastructure, promoting flexible data center operations, and ensuring that energy production can meet AI’s evolving needs. Collaboration between the energy and technology sectors, along with strategic policy planning, will be essential to manage risks and utilize AI’s potential in the energy sector.
The Bottom Line
AI is significantly changing the global electricity sector. While its increasing demand for energy in data centers creates challenges, it also offers the energy sector opportunities to evolve and improve efficiency. By using AI to enhance energy use and diversify energy sources, we can meet the growing power needs of AI in a sustainable way. The energy sector must quickly adapt to support AI’s rapid growth while using AI to improve energy systems. Over the next decade, we can expect major changes in how electricity is generated, distributed, and consumed, driven by the intersection of AI and the digital economy.
