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Artificial intelligence is being applied to solve energy and climate challenges in three fundamental ways: by making energy systems smarter and more efficient, by accelerating the discovery and deployment of clean technologies, and by enabling better decision-making across the energy transition. AI can detect patterns in data and use historic knowledge to accurately predict future outcomes, making it invaluable for monitoring the environment and helping governments, businesses and individuals make more planet-friendly choices. However, the technology presents a paradox—while AI offers powerful tools for decarbonization, AI-fuelled data centre growth raises concerns that it might fuel climate change, even as AI applications in the energy sector could help reduce emissions by unlocking new optimisations and efficiencies.

Key Points

- AI can enhance the stability and efficiency of renewable energy integration into power grids by forecasting supply and demand more accurately and by managing distributed energy resources such as electric vehicles and energy storage systems.

- AI can facilitate detection of methane emissions in oil and gas operations through better identification using satellite monitoring systems, allowing repairs to happen sooner.

- Machine learning algorithms are being used to optimize CO2 separation processes in real-time, and AI-driven materials design is playing a key role in the discovery of advanced materials such as sorbents, membranes, and catalysts for CO2 capture.

- AI reduces building energy and emissions in design, construction, equipment, occupancy, and control/operation, and by accelerating high-efficiency and net-zero buildings, AI could cut energy and emissions by 40-90% by 2050 combined with adequate policies.

- The net impact of AI on emissions will depend on how AI applications are rolled out, what incentives and business cases arise, and how regulatory frameworks respond to the evolving AI landscape.

Understanding AI's Role in Energy Systems

AI is fundamentally changing how energy systems operate by enabling real-time optimization that was previously impossible. Traditional energy grids were designed around predictable demand patterns and centralized power generation. Today's grids must balance intermittent renewable sources with fluctuating demand while integrating millions of new devices—from electric vehicles to smart thermostats. The power grid must maintain an exact balance between power input and output at every moment, but demand has uncertainty since power companies don't ask customers to pre-register energy use ahead of time, and on the supply side there is variation in costs and fuel availability, which has become an even bigger issue because of the integration of time-varying renewable sources like solar and wind where weather uncertainty impacts power availability.

AI addresses these challenges by processing vast amounts of data from sensors, weather stations, and historical patterns to make predictions and optimize operations in real time. By leveraging massive datasets and advanced machine learning algorithms, AI-powered forecasting allows utilities to predict energy demand with unprecedented precision, enabling more efficient planning and a more resilient power system. This capability extends beyond simple forecasting to active grid management, where AI can coordinate distributed resources and adjust loads dynamically.

How It Works

1. Grid Forecasting and Demand Prediction

AI can use a combination of historical and real-time data to make more precise predictions about how much renewable energy will be available at a certain time, which could lead to a cleaner power grid by allowing better handling and utilization of these resources. Machine learning models analyze weather patterns, historical consumption data, time-of-day effects, and occupancy patterns to forecast both energy supply from renewables and demand from consumers. This enables grid operators to plan generation and storage deployment more effectively.

2. Real-Time Grid Optimization and Flexibility

AI can help tackle the complex optimization problems that power grid operators must solve to balance supply and demand in a way that also reduces costs, determining which power generators should produce power, how much they should produce, and when they should produce it, as well as when batteries should be charged and discharged—optimization problems so computationally expensive that operators use approximations to solve them in a feasible amount of time. AI can solve these problems more efficiently, enabling faster response to changing conditions.

3. Carbon Capture and Materials Discovery

AI models learn from datasets of hypothetical metal-organic frameworks and suggest new molecular linkers predicted to have high capacity for capturing carbon from the air, with models assembling 120,000 possible structures.

AI-optimized Temperature Swing Adsorption cycles have achieved up to 50% reductions in heat requirements and intelligent process controls have enhanced capture efficiency by 20% while reducing energy use by 15%.

4. Building and Industrial Energy Efficiency

IoT sensors and real-time monitoring track energy usage, while AI predicts demand and automates HVAC and lighting adjustments to reduce waste.

By monitoring energy consumption, AI develops customized predictive models to predict consumption patterns based on several variables such as time of day, weather, asset type, occupancy, usage and other relevant factors. This enables buildings and industrial facilities to operate at peak efficiency.

5. Predictive Maintenance

AI systems analyze vast amounts of information energy companies collect from sensors installed throughout the grid, creating a predictive model that forecasts wear and tear over time, and ultimately the software could recommend when to repair or replace parts before any problems occur.

Why It Matters

The energy transition requires solving problems at unprecedented scale and speed. Across the bulk power system, large centralized power generators are being replaced with smaller, distributed, and variable generators like solar photovoltaic and wind turbines, creating a need for optimizing and fast-tracking the deployment of new grid infrastructure and developing tools to better forecast and manage intermittent, non-dispatchable power plants. AI provides the computational capability to manage this complexity in real time.

However, the stakes are high because AI's own energy consumption is growing rapidly. In 2024, global data center electricity consumption was approximately 415 terrawatt hours, representing about 1.5% of the world's total electricity use, and this figure has been growing at a compound annual growth rate of 12% since 2017, a rate more than four times faster than that of total global electricity consumption.

The only technologies that can scale fast enough to meet rising AI energy needs will be renewables and grid-scale storage such as large battery systems, and this reality will push utilities, investors, and governments to accelerate clean energy projects, making AI's appetite an inadvertent catalyst for the energy transition.

Related Terms

• Smart Grid: An electricity network that uses digital communication and automation to improve efficiency, reliability, and sustainability by integrating distributed energy resources and enabling two-way information flow.

• Demand Response: The ability to adjust electricity consumption in response to grid conditions or price signals, often coordinated by AI systems to balance supply and demand.

• Distributed Energy Resources (DERs): Smaller, decentralized power generation and storage systems like rooftop solar, batteries, and electric vehicles that AI can coordinate to support grid operations.

• Direct Air Capture (DAC): A technology that removes CO2 directly from the atmosphere, with AI optimizing the energy-intensive processes involved in capture and regeneration.

• Microgrid: A localized energy system that can operate independently or connected to the main grid, with AI managing the integration of multiple energy sources and loads.

Frequently Asked Questions

Can AI really help reduce energy consumption?

Yes, but with important caveats. The emissions reductions due to AI from just three key sectors—power, food, and mobility—would more than offset the estimated increase in emissions from all of AI's activities, showing how the case for using AI for the climate transition is not only strong but imperative. However, there is currently no momentum that could ensure the widespread adoption of these AI applications, and their aggregate impact could be marginal if necessary enabling conditions are not created, with barriers including constraints on access to data, the absence of digital infrastructure and skills, regulatory and security restrictions, and social or cultural obstacles.

What is the biggest challenge with AI and energy?

The fundamental challenge is that bringing even one AI data center online can add the same continuous electricity load as powering a small city, and coal and gas plants cannot be built or ramped up quickly enough to meet these rising needs, with slow transmission expansion continuing to limit how fast new power can reach demand. This creates a timing mismatch between AI's energy demands and the deployment of clean energy infrastructure.

How can AI data centers themselves become more sustainable?

Software can observe data centre workloads, identify which tasks are non-critical, and adjust computing activity to shape electricity demand, including ramping down by a defined number of megawatts, holding reductions for an agreed period, and ramping back up gradually to avoid "snapback" that can destabilise the grid. Additionally, direct air capture powered by waste heat from data centers could remove some 50-1,000 megatonnes of CO2 annually and could generate up to $100 billion USD annually in economic value while contributing significantly to net-zero goals.

Last updated: April 26, 2026. For the latest energy news and analysis, visit stakeandpaper.com.