Artificial intelligence (AI) is more than just converting technology. It has also changed the global energy sector significantly. According to a new report from the International Energy Agency (IEA), the rapid growth of AI, particularly in data centers, has caused a significant increase in electricity demand. At the same time, AI provides opportunities for the energy sector to become more efficient, sustainable and resilient. This shift is expected to significantly translate the way power is generated, consumed and managed.
Increased power demand for AI
One of the most pressing impacts of AI on global electricity consumption is data center growth. These facilities, which provide the computing power needed to run AI models, are already the leading consumers of electricity. As AI technology becomes more powerful and widespread, the demand for computing power and the energy needed to support it is expected to increase significantly. According to the report, data center power consumption is projected to exceed 945 TWH by 2030. It’s more than twice the level you’ll see in 2024. This increase is driven primarily by the increased demand for AI models that require high-performance computing, particularly those using accelerated servers.
Currently, data centers consume around 1.5% of the world’s electricity. However, the global share of electricity demand is expected to increase significantly over the next decade. This is mainly because AI relies on special hardware such as GPUs and acceleration servers. The energy-intensive nature of AI plays a key role in determining the future of electricity consumption.
Regional variation in the impact of AI energy
Power consumption from data centers is not evenly distributed worldwide. The US, China and Europe account for the largest share of global data center electricity demand. In the US, data centers are expected to contribute nearly half of the country’s increased electricity demand by 2030. Meanwhile, emerging economies such as Southeast Asia and India have experienced rapid data centre development, but demand growth is low compared to developed countries.
This concentration of data centers poses unique challenges to the electrical network, especially in areas where infrastructure is already strained. The high energy demands of these centers can lead to congestion of the grid and delayed connections to the grid. For example, US data center projects face long latency due to limited grid capacity and can get worse without proper planning.
Strategies to meet the growing energy demands of AI
The IEA report suggests several strategies to meet the increased power demand for AI while ensuring grid reliability. One important strategy is diversifying energy sources. Renewable energy plays a central role in meeting the growing demand from data centers, but other sources such as natural gas, nuclear and emerging technologies such as small modular reactors (SMRs) also contribute.
Renewables are expected to supply nearly half of global growth in data center demand by 2035 due to their economic competitiveness and fast development timelines. However, robust energy storage solutions and flexible grid management are required to balance the intermittent nature of renewable energy with constant demand from data centers. Furthermore, AI itself plays a role in improving energy efficiency, helping to optimize power plant operations and improve grid management.
The role of AI in energy sector optimization
AI is also a powerful tool for optimizing energy systems. It can enhance energy production, reduce operational costs, and improve the integration of renewable energy into existing grids. Using AI for real-time monitoring, predictive maintenance and grid optimization allows energy companies to increase efficiency and reduce emissions. The IEA estimates that widespread adoption of AI will save up to $110 billion a year in the electricity sector by 2035. The IEA report also highlights some key applications of how AI can improve supply and demand efficiency in the energy sector.
- Supply and Demand Forecast: AI increases the ability to predict the availability of renewable energy, essential for integrating variable sources into the grid. For example, Google’s neural network-based AI increased the financial value of wind power by 20% with accurate 36-hour forecasts. This allows the utility to improve the balance between supply and demand and reduce its reliance on fossil fuel backups.
- Predictive Maintenance: AI monitors energy infrastructures such as power lines and turbines and predicts failures before they stop. E. reduced outages by up to 30% using machine learning for medium voltage cables, while ENEL achieved a 15% reduction in sensor-based AI systems.
- Grid Management: AI processes data from sensors and smart meters, especially at distribution levels, to optimize power flow. This ensures stable and efficient grid operation, even when the number of grid-connected devices continues to increase.
- Demand response: AI allows for improved forecasting of electricity prices and dynamic pricing models, encouraging consumers to shift usage to off-peak hours. This reduces grid distortion and reduces costs for both utility and consumers.
- Consumer Services: AI enhances customer experiences through apps and chatbots, improving billing and energy management. Companies such as Octopus Energy and Oracle Utilities are key examples of this innovation.
Furthermore, AI can help reduce energy consumption by improving the efficiency of energy-intensive processes such as power generation and transfer. As the energy sector becomes more digital, AI will play a key role in balancing supply and demand.
Challenges and progress
While AI integration into the energy sector is highly promising, uncertainty still exists. The speed of AI adoption, advances in AI hardware efficiency, and the ability of the energy sector to meet increasing demand are all factors that could impact future power consumption. The IEA report outlines several scenarios, with the most optimistic forecast showing a surge in demand exceeding 45%, exceeding current expectations.
To ensure that AI growth does not exceed the capabilities of the energy sector, countries need to focus on strengthening their grid infrastructure, promoting flexible data center operations, and meeting the evolving needs of AI. Collaboration with the energy and technology sector, along with strategic policy planning, is essential to managing risk and harnessing the possibilities of AI in the energy sector.
Conclusion
AI is significantly changing the global electricity sector. While increasing demand for energy in data centers creates challenges, it also provides opportunities for the energy sector to evolve and improve efficiency. By using AI to enhance energy use and diversifying energy sources, we can meet the growing power needs of AI in a sustainable way. The energy sector needs to quickly adapt to support the rapid growth of AI while using AI to improve its energy systems. Over the next decade, we can expect a major change in how electricity is generated, distributed and consumed, driven by the intersection of AI and the digital economy.
