Artificial intelligence (AI) is not only the transformation of technology; This also changes a significantly global energy sector. According to the latest report From the International Energy Agency (IEA), rapid growth of AI, especially in data centers, causes a significant increase in the demand for electricity. At the same time, AI also offers more efficient, balanced and resistant possibilities. This change is expected to significantly transform the way of generating, consuming and electricity management.
Growing demand for electricity AI
One of the most direct effects of AI is global electricity consumption, is the increase in data centers. These objects that provide computing power needed to run AI models are already the main consumers of electricity. Because AI technologies become stronger and common, it is expected that the demand for computing power – and the energy required to support it – will increase significantly. According to reportIt is anticipated that the consumption of electricity by data centers will exceed 945 TWh by 2030, more than twice as high as levels observed in 2024. This increase mainly results from the growing demand for AI models, which require high -performance calculations, especially those using accelerated servers.
Currently, data centers consume about 1.5% of global electricity. However, it is expected that their participation in the global demand for electricity will increase significantly in the next decade. This is primarily due to AI's rely on specialized equipment, such as GPU and accelerated servers. Energo -saving AI plays a key role in determining the future of electricity consumption.
Regional changes in influence on AI energy
Electricity consumption from data centers is not evenly distributed around the world. The United States, China and Europe are the largest share in the global demand for electricity in the data center. In the United States, data centers are expected to contribute to almost half of the increase in demand for electricity by 2030. Meanwhile, emerging economies, such as South -East Asia and India, experience the rapid development of data centers, although their increase in demand remains lower compared to developed countries.
This concentration of data centers is unique challenges for the electrical network, especially in regions where the infrastructure is already burdened. The high demand for the energy of these centers can lead to congestion and delays in conjunction with the mesh. For example, data centers in the United States have faced long waiting times due to a limited capacity of the network, a problem that can deteriorate without proper planning.
Strategies that meet the growing energy requirements AI
The IEA report suggests several strategies that meet the growing demand for AI electricity, ensuring reliability of the network. One of the key strategies is the diversification of energy sources. While renewable energy will play a key role in satisfying increased demand on the part of data centers, other sources, such as natural gas, nuclear energy and emerging technologies, such as small modular reactors (SMR).
Renewable energy sources are expected to ensure almost half of the global increase in demand on the data center by 2035, due to their economic competitiveness and faster development dates. However, the balance of the intermittent nature of renewable energy at the constant demand of data centers will require solid solutions for energy storage and flexible network management. In addition, artificial intelligence itself can play a role in increasing energy efficiency, helping optimizing power plant surgery and improve mesh management.
The role of AI in the optimization of the energy sector
AI is also a powerful tool for optimizing energy systems. It can improve energy production, reduce operating costs and improve renewable energy integration to existing networks. By using AI for real -time monitoring, predictive maintenance and network optimization, energy companies can increase efficiency and reduce emissions. IEA estimates that universal AI adoption may save up to $ 110 billion a year in the electricity sector by 2035. The IEA report also emphasizes several key applications AI may improve the efficiency of demand and supply in the energy sector:
- Forecasting supply and demand: AI increases the ability to predict renewable energy, which is necessary to integrate variable sources with a net. For example, Google AI based on a neural network increased the financial value of wind energy by 20% through exact 36-hour forecasts. This allows tools to better balance the supply and demand, reducing relying on backups of fossil fuels.
- Predictive maintenance: AI monitors energy infrastructure, such as energy lines and turbines, to predict errors before they would lead to a break. ETERNITY Reduced failures by up to 30% using machine learning for medium voltage cables, and Enel reached a 15% reduction thanks to AI systems based on sensors.
- Mesh management: AI processes data from intelligent sensors and measures to optimize power flow, especially at the distribution level. This provides stable and efficient network operations, even when the number of devices connected to the network is constantly growing.
- Answer at the request: AI allows for better forecasting of electricity prices and dynamic price models, encouraging consumers to change use on time outside the peak. This reduces the mesh deformation and reduces costs for both the media and consumers.
- Consumer services: AI increases customer experience through applications and chatbots, improving energy settlements and management. Companies such as Octopus Energy and Oracle Utilities are leading examples of this innovation.
In addition, AI can help reduce energy consumption by improving the efficiency of energy -saving processes, such as energy production and transmission. As the energy sector becomes more digitized, and will play a key role in the balance of supply and demand.
Challenges and ahead
While the integration of artificial intelligence with the energy sector has a great promise, there are still uncertainty. The speed of acceptance of AI, progress in AI equipment efficiency and the ability of energy sectors to satisfy growing demand are factors that can affect future electricity consumption. The IEA report presents several scenarios, with the most optimistic projection indicating an increase in demand by over 45% outside of current expectations.
To make sure that AI growth does not overtake the capacity of the energy sector, countries will have to focus on improving the infrastructure of the network, promoting flexible data center surgery and ensuring that energy production can meet the developing AI needs. Cooperation between energy and technology sectors, along with strategic policy planning, will be necessary to manage risk and use AI potential in the energy sector.
Lower line
AI significantly changes the global electricity sector. While his growing energy demand in data centers creates challenges, he also offers the possibilities of the energy sector of evolution and improvement in performance. Using artificial intelligence to increase energy consumption and diversify energy sources, we can in a balanced way to meet the growing needs of AI power. The energy sector must quickly adapt to rapid AI growth when using AI to improve energy systems. Over the next decade, we can expect serious changes in the way of generating, distributing and consumption of electricity, driven by AI intersection and digital economy.
