Energy systems are becoming more complex: wind and solar output varies with the weather, electric vehicles and heat pumps change demand patterns, and millions of rooftop panels and batteries feed electricity back into the grid. Managing all this requires predicting and coordinating vast amounts of data — a natural task for AI. At the same time, AI’s own electricity use is a growing concern.

Forecasting renewables

Wind and solar output depends on weather. Better forecasts help grid operators plan reserves and help producers sell energy at better prices. In 2019, Google and DeepMind reported that machine learning forecasts of wind farm output 36 hours ahead allowed them to commit power in advance, which they said increased the value of the wind energy by roughly 20% compared with no time-based commitments. AI weather models have since improved forecasts further.

Demand forecasting

Utilities forecast electricity demand by hour and location, accounting for weather, holidays, economic activity and growing loads like electric vehicles. Accurate forecasts reduce costs and the need for expensive backup generation.

Grid management

  • Balancing: coordinating supply, storage and flexible demand in real time.
  • Congestion management: predicting and avoiding overloaded lines.
  • Fault detection: locating faults and outages faster from sensor data.
  • Integrating distributed energy: managing rooftop solar, batteries and EV charging as virtual power plants.

Predictive maintenance

Wind turbines, transformers, power lines and plants are expensive and critical. Sensor-based AI predicts failures, and drones with computer vision inspect lines and turbine blades, reducing outages and dangerous manual inspections.

Wildfire and climate risk

Utilities use AI with satellite and camera data to identify vegetation near lines and detect wildfires early — an important issue where power lines have caused deadly fires.

Energy efficiency

AI optimizes heating, cooling and lighting in buildings and industrial processes. A well-known example is Google’s use of DeepMind’s machine learning to reduce the energy used for cooling its data centers.

Exploration and materials research

AI supports research into battery materials, solar cells, catalysts and fusion plasma control, potentially accelerating clean energy technology.

AI’s own energy demand

The International Energy Agency’s 2025 report Energy and AI projected that electricity consumption by data centers could roughly double by 2030, with AI as the main driver, while also highlighting AI’s potential to optimize energy systems. Where and how new data centers are powered — and whether AI-enabled efficiencies offset their demand — is a central question for energy policy.

Energy trading and markets

Electricity markets clear prices every hour or even every few minutes. Traders, utilities and battery operators use machine learning to forecast prices, decide when to buy, sell or store power, and bid flexible assets into markets. As more batteries connect to grids, automated trading based on AI forecasts becomes a significant part of how storage earns revenue — and of how grids absorb variable renewables.

Security and reliability concerns

Power grids are critical infrastructure. Introducing AI raises questions about:

  • cybersecurity, since more connected devices and software expand the attack surface;
  • reliability, as operators must understand and trust AI recommendations in emergencies;
  • explainability for regulators and system operators;
  • concentration risk if many operators rely on the same vendors or models.

Grid operators therefore typically introduce AI as decision support with human operators in control, and test extensively before automating critical functions.

What to watch next

  • Data center power demand and new generation built to supply it.
  • AI-based weather models adopted by grid operators.
  • Virtual power plants coordinating home batteries and EVs.
  • Regulatory approaches to AI in critical energy infrastructure.

Key takeaways

  • AI forecasts renewable output and demand, helping integrate wind and solar.
  • DeepMind reported about 20% more value from wind energy using ML forecasts in 2019.
  • Grid operators use AI for balancing, congestion, faults and distributed energy.
  • Predictive maintenance and drone inspections reduce outages and risks.
  • The IEA projects data center electricity use could roughly double by 2030, driven by AI.

Frequently asked questions

Can AI make the grid more reliable?

Better forecasting, fault detection and maintenance can improve reliability, provided systems are secure and well validated.

Does AI use a lot of energy?

Data centers’ electricity use is growing quickly, with AI a major driver. Its share of global electricity remains modest but is significant in some regions.

What is a virtual power plant?

A network of distributed resources — batteries, solar panels, EV chargers, flexible loads — coordinated by software to act like a power plant.