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Currently, the power consumption of AI is enormous
AI models need to perform extremely complex calculations. Experts predict that the energy required to train large language models such as ChatGPT could spiral out of control in the coming years. A study by Goldman Sachs already shows that a query to ChatGPT consumes ten times more energy than a Google search.
A report by the International Energy Agency (IEA) identifies data centers, AI and the cryptocurrency sector as key drivers of the increase in global energy consumption. Furthermore, AI’s energy usage could double by 2026. With an estimated electricity consumption of 460 terawatt hours (TWh) in 2022, the total electricity consumption of data centers could rise to over 1000 TWh by 2026. This is roughly equivalent to Japan’s electricity consumption in one year.
Consuming vs. optimizing energy sources
It is very likely that the problem of AI’s high energy consumption will soon be solved – by AI itself. AI’s strength lies in analyzing and evaluating huge amounts of data and optimizing complex systems based on these evaluations. This is why AI is already being used to make power grids more efficient, reduce energy consumption in buildings, and optimize the production of renewable energy. Specialized AI-powered energy management tools such as LADEgreen, etalytics and Greenflash are already on the market.
How AI can make electricity grids more efficient
1. Load forecasting and demand management
- AI models can analyze historical consumption data, weather forecasts and other factors to accurately predict energy demand.
- With the help of AI, grid operators can motivate consumers to adjust their energy consumption at certain times in order to reduce peak loads and relieve the grid.
2. Optimization of energy generation
- AI helps to better predict and integrate the irregular feed-in from renewable sources such as solar and wind power.
- AI can decide when to ramp up or ramp down which power plants to maximize the efficiency of energy generation.
3. Grid monitoring and maintenance
- AI can analyze sensors and smart meter data to detect potential faults or malfunctions at an early stage.
- Instead of regular maintenance intervals, AI control allows maintenance operations to be carried out exactly when they are needed, reducing downtime and costs.
4. Real-time grid control
- Through optimal load balancing, special AI systems can control the energy flow in the grid in real time to minimize losses and ensure grid stability.
- In the event of a grid problem, AI can implement automatic response strategies to rectify the fault as quickly as possible.
5. Optimization of energy storage systems
- AI algorithms can decide when to store energy or draw energy from batteries to improve grid stability.
- Through real-time monitoring and analysis, AI can optimally coordinate different storage systems (e.g. batteries and pumped storage power plants).
6. Decentralized energy management
- Through smart grids, AI enables communication between different grid participants (e.g. between households, companies and power plants) and optimizes distribution in local networks. One example of this is the ION Power Grid project from Salzburg, Austria. It combines modern technologies such as decentralized control, real-time data analysis and artificial intelligence to optimize energy flows.
- With the help of AI, local producers and consumers can trade energy directly without the need for central grid operators to intervene.
Example 1: AI systems in wind power
One of the pioneers is the Danish company Vestas, a leading manufacturer of wind turbines. Vestas uses AI to analyze weather and location data and optimize the positioning of new wind farms.
This maximizes energy yield while reducing costs and environmental impact. In existing plants, AI enables predictive maintenance. This technology detects problems at an early stage before they affect operation.
Example 2: Smart cities and buildings
In Singapore, the government is working with AI-supported systems to reduce energy consumption throughout the city. Smart buildings, equipped with AI-controlled sensors, automatically adjust lighting, heating and cooling to the number of people present and the weather conditions. This results in a significant reduction in energy consumption of up to 30 percent.
Example 3: AI management of power grids
Germany’s Siemens cooperation is using AI technology to make power grids more efficient. The system analyses data from the network in real time and can predict bottlenecks or overloads. This prevents energy from being wasted. At the same time, AI ensures that renewable energies can be better integrated into the grid by using special algorithms to compensate for fluctuations in renewable energy generation.
Sustainable AI could soon no longer be a vision of the future
Numerous technology companies are also working on reducing the energy consumption of their AI systems. Approaches such as “Green AI” aim to design AI algorithms in such a way that they require less computing power. One example of this is the company DeepMind, whose AI system is designed to optimize the energy consumption of Google data centers. By analyzing and fine-tuning the cooling system, energy consumption has been reduced by 40 percent.
Another innovative approach is the use of AI in combination with edge computing. Edge computing means moving away from energy-intensive cloud computing. Instead, this technology relies on decentralized data processing on site. This not only reduces energy consumption, but also the delay in data transmission between sender and receiver.
A promising tightrope walk
The high energy consumption of AI systems is a challenge that we need to address. However, the potential for AI to help solve global energy problems is extremely high. To master this balancing act, we need close cooperation between research, industry and politics. Only through sustainable innovation and responsible use can AI become a driving force for a greener future.
