Artificial intelligence (AI) is not only changing technology. It is also significantly changing the global energy sector. According to the latest report from the International Energy Agency (IEA), the rapid growth of AI, especially in data centers, has led to a significant increase in power demand. At the same time, AI also provides more efficient, sustainable and resilient opportunities for the energy sector. This shift is expected to dramatically change the way we generate, consume and manage electricity.
The demand for electricity from artificial intelligence continues to grow
One of the most direct effects of AI on global power consumption is the growth of data centers. These facilities provide the computing power needed to run AI models and are already the main consumers of electricity. As AI technology becomes more powerful and broader, the need for computing power and the energy needed to support it will increase significantly. According to the report, data centers are expected to consume more than 945 TWH by 2030, more than double the 2024 level. This increase is driven primarily by the growth of AI models that require high-performance computing, especially 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 mainly due to the fact that AI relies on GPU and professional hardware for acceleration servers (acceleration servers). The energy-intensive nature of artificial intelligence will play a key role in determining the future of power consumption.
Regional changes in the impact of artificial intelligence energy
The power consumption range from data centers is evenly distributed worldwide. The United States, China and Europe account for the largest share of global data center power demand. In the United States, data centers are expected to contribute to nearly half of the country’s electricity demand growth by 2030. Meanwhile, while their demand growth remains low compared to developed countries, emerging economies such as Southeast Asia and India are experiencing rapid data center development.
This concentration of data centers poses a unique challenge to the grid, especially in areas where infrastructure is already under pressure. High energy demands in these centers can cause grid congestion and connect to the grid. For example, data center projects in the United States face long wait times due to limited grid capacity, and this problem may worsen without proper planning.
Strategies to meet the growing energy demand of AI
The IEA report proposes several strategies to meet the growing electricity demand for AI while ensuring grid reliability. One key strategy is diversification of energy. While renewable energy will play a central role in meeting the increased demand for data centers, other sources such as natural gas, nuclear energy and emerging technologies will also promote small modular reactors (SMR).
Renewable energy is expected to provide half of global data center demand growth by 2035 due to economic competitiveness and faster development timelines. However, balancing the intermittent nature of renewable energy with the ongoing demand for data centers will require strong energy storage solutions and flexible grid management. In addition, AI itself can play a role in improving energy efficiency, helping to optimize power plant operations and improving grid management.
The role of AI in optimizing energy
AI is also a powerful tool for optimizing energy systems. It can improve energy production, reduce operating 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 by 2035, widespread adoption of full adoption could save up to $110 billion in the power sector. The IEA report also highlights several key applications of how AI can improve demand and supply efficiency in the energy sector:
- Forecasting supply and demand: AI enhances the ability to predict renewable energy availability, which is crucial for integrating variable sources into the grid. For example, with an accurate 36-hour forecast, Google’s neural network-based AI boosted the financial value of wind energy by 20%. This allows utilities to better balance supply and demand, thereby reducing their reliance on fossil fuel backup.
- Predictive maintenance: AI monitors energy infrastructure, such as power cords and turbines, to predict failures that lead to failures. E.On uses machine learning to reduce by up to 30% in medium voltage cables, while ENEL reduces by 15% through sensor-based AI systems.
- Grid management: AI processes data from sensors and smart meters to optimize power flow, especially at the allocation level. This ensures stable, efficient grid operation, even if the number of grid-connected devices continues to grow.
- Requirement response: AI can better predict electricity prices and dynamic pricing models, thereby encouraging consumers to shift usage to off-peak hours. This reduces grid strain and reduces costs for utilities and consumers.
- Consumer Services: AI enhances the customer experience with applications and chatbots, thus 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 increasing the efficiency of energy-intensive processes such as power generation and transmission. As the energy sector becomes more digital, AI will play a key role in balancing supply and demand.
Challenge and direction to move forward
While there is great hope for integrating AI into the energy sector, uncertainty remains. The speed of AI adoption, advances in AI hardware efficiency, and the ability of the energy sector to meet growing demand are all factors that may affect future power consumption. The IEA report outlines several situations, with the most optimistic forecasts showing that demand surges beyond current expectations.
To ensure that AI growth does not exceed the capacity of the energy sector, countries will need to focus on enhancing grid infrastructure, promoting flexible data center operations, and ensuring that energy production can meet the evolving needs of AI. Cooperation between the energy and technology sectors and strategic policy plans are critical to managing risks and leveraging AI’s potential in the energy sector.
Bottom line
AI is significantly changing the global power sector. While its increasing energy demand for data centers presents challenges, it also provides opportunities for the energy sector to grow and improve efficiency. By using AI to enhance energy use and diversify energy, we can meet the growing demand for power in AI in a sustainable way. The energy sector must adapt quickly to support the rapid growth of AI while using AI to improve energy systems. Over the next decade, we can expect significant changes in the way electricity is driven by the intersection of AI and the digital economy.
