Why AI is set to double power datacenter energy use by the end of the decade

Global power datacenter energy use could double by 2030, with forecasts from the Electric Power Research Institute and Boston Consulting Group projecting up to 260 TWh in growth. The International Energy Agency and Goldman Sachs also expect rapid increases, driven by ai and cloud services. This rising demand places significant impact on the power grid and business costs. The urgency grows as data center operations expand. What drives this surge, and how can leaders respond? NBYOSUN stands out as a leader in intelligent power solutions for the evolving power datacenter landscape.

AI and Power Datacenter Growth

AI Workloads vs. Traditional Computing

Generative ai has transformed the landscape of data center operations. Traditional computing tasks, such as web hosting or email, require modest computing power and energy. In contrast, ai workloads demand much higher power density and specialized hardware. The difference becomes clear when comparing energy use and infrastructure needs.

Note: A single ChatGPT query can use nearly ten times more electricity than a standard Google search.

The following table highlights key differences between ai workloads and traditional computing:

Generative ai models, such as GPT-4, require massive computing power for both training and inference. This leads to exponential growth in energy consumption. Data center operators now face a 25% annual growth rate in power demand for ai compute, compared to 12–15% for traditional workloads. By 2027, ai workloads could account for 27% of total data center power use, up from 14% in 2023.

Hardware Demands in AI Data Centers

Generative ai drives a surge in demand for advanced hardware. Ai data centers must support thousands of high-performance GPUs or TPUs, which consume much more power than standard CPUs. These facilities operate at 80–90% utilization, far higher than non-ai data centers. The hardware generates significant heat, so advanced cooling systems—sometimes using liquid or immersion cooling—are essential.

• Hyperscale data centers have nearly doubled capacity in four years, now operating almost 1,000 sites worldwide.
• The largest tech companies plan to spend over $200 billion on data center expansion in 2024, with much of this focused on generative ai.
• Nvidia sold $66 billion in GPUs to data centers in the past year, reflecting the scale of ai compute demand.
• Cooling systems can use up to 40% of a data center’s total energy, especially in ai data centers.

Generative ai models require continuous data movement and storage, increasing the need for robust power distribution and monitoring. Ai data centers often run at full capacity around the clock, which raises the risk of hardware failure. For example, Meta reported a 9% annualized GPU failure rate during Llama 3 training. Operators must monitor power, temperature, and device health closely to prevent downtime.

The rapid growth of generative ai means data centers now use about 1–1.5% of global electricity. This figure could exceed 5% by the end of the decade, making efficient power management and infrastructure upgrades critical for the future.

Data Center Power Demand Projections

Global Forecasts and Key Drivers

Recent projections from leading organizations show a dramatic rise in data center power demand by 2030. The International Energy Agency (IEA) expects global data center power demand to increase between 35% and 100%, adding 120 to 390 terawatt-hours (TWh) of electricity demand. Goldman Sachs Research estimates a 160% increase in data center power demand by 2030 compared to current levels. Semi Analysis predicts global data center demand could triple, reaching about 1,500 TWh by 2030. This would represent 4.5% of total global power demand, up from 1.5% today.

The U.S. Federal Energy Regulatory Commission (FERC) projects U.S. data center load will grow by up to two-thirds by 2030, adding 21 to 35 gigawatts (GW) of new demand. The Electric Power Research Institute (EPRI) highlights that data centers could consume more than 9% of U.S. electricity by 2030, more than doubling current levels. These forecasts reflect both the rapid expansion of hyperscale data centers and the increasing power density required for generative ai workloads.

Note: The IEA reports that a single ChatGPT query uses nearly ten times the electricity of a standard Google search, showing the significant impact of generative ai on energy use.

The following table summarizes key projections for data center demand and power growth:

Asia Pacific and North America currently lead in data center capacity, with North America expected to see the largest growth. The rising power demand from data centers will require significant grid investments and infrastructure upgrades. The growing use of generative ai models, which need high-performance GPUs and advanced cooling, drives much of this ai-driven demand growth.

AI’s Share in Data Center Demand

Generative ai is transforming the landscape of data center power consumption. As of 2024, global data centers use about 1.5% of global electricity, or roughly 415 TWh each year. The U.S. accounts for 45% of this load. The IEA forecasts that global data center electricity demand will more than double to over 945 TWh by 2030.

• AI workloads are highly power-intensive. A single ai data center can consume as much electricity as 100,000 homes.
• Hyperscale ai data center campuses may use 20 times that amount, acting as industrial-scale energy consumers.
• AI and cloud workloads are expected to drive nearly half of all U.S. electricity demand growth this decade.

Data center electricity consumption in the U.S. doubled from about 2% in 2020/21 to 4.4% in 2023. EPRI projects that data centers could consume more than 9% of U.S. electricity by 2030. Goldman Sachs forecasts a 165% increase in global data center power demand, driven by ai energy demand. Expert Jonathan Koomey confirms that data center energy consumption will likely double within a few years, with total capacity more than doubling by 2030.

Generative ai models require continuous training and inference, which increases both power demand and electricity consumption. The share of ai workloads in total data center demand is rising quickly. In 2023, ai workloads accounted for 14% of data center power demand. By 2027, this share is expected to reach 27%. Traditional workloads will shrink as a share, while generative ai and cloud computing continue to expand.

The impact of this shift is clear: data center operators must plan for much higher power density, greater electricity use, and new infrastructure challenges. The rapid growth in ai-driven demand growth will shape the future of the power datacenter industry.

Challenges for AI Data Centers

Grid and Infrastructure Strain

AI data centers face major challenges as they scale up to meet rising power demand. The exponential growth in AI workloads requires much more energy and forces operators to redesign infrastructure. Traditional grids struggle to keep up because AI servers use five to ten times more power than standard servers. This surge in electricity demand puts stress on local grids, especially during peak hours. Research from the International Energy Agency and Lawrence Berkeley National Laboratory shows that data centers now drive significant increases in electricity consumption, sometimes causing local grid constraints and higher costs for other customers.

The following table summarizes key challenges and industry responses:

Operators must also manage complex systems with many components, such as generators, renewables, and batteries. Real-time monitoring and control systems help reduce failure points and keep operations reliable. In regions like Northern Virginia, rapid data center growth has pushed grid capacity to its limits, forcing new investments and policy changes.

Note: Demand flexibility, such as reducing power use during peak hours, can help ease grid strain and lower electricity costs.

Environmental and Regulatory Pressures

AI data centers also face growing environmental and regulatory pressures. Governments now require operators to track and report energy consumption, water use, and renewable energy share. In the European Union, any data center with at least 500kW IT power demand must report annual metrics like Power Usage Effectiveness (PUE), Water Usage Effectiveness (WUE), and Renewable Energy Factor (REF). The average global PUE in 2023 reached 1.58, showing the need for better energy efficiency.

• Operators must monitor electricity consumption at the power distribution unit level.
• Regulations may restrict AI training during peak energy hours to protect grid stability.
• Policy proposals include binding targets for energy efficiency and renewable energy use.

Environmental risks, such as extreme weather, threaten physical infrastructure and increase the need for robust data center cooling systems. Cybersecurity concerns add another layer of complexity, as operators must protect both power and data systems. Sustainability goals push operators to integrate more renewables and reduce carbon emissions, even as electricity use rises.

Operators must balance the need for more AI compute with the responsibility to minimize environmental impact and comply with strict regulations.

Solutions for Sustainable Power Datacenter Operations

Smart PDU Technology from NBYOSUN

NBYOSUN leads the way in delivering intelligent power solutions for ai data centers. Their smart PDUs provide advanced features that help operators optimize energy use and support sustainability goals. These devices offer real-time power usage monitoring, allowing precise tracking of voltage, current, power factor, and energy consumption at both the inlet and outlet levels. High-efficiency transformers in these PDUs improve energy efficiency by 2% to 3%, which results in significant cost savings over time.

Smart PDUs from NBYOSUN enable load balancing, distributing power evenly across devices. This minimizes energy waste and reduces the risk of overloads. Operators can manage power remotely, which enhances operational efficiency and reduces downtime risks. The PDUs support scalability and integrate seamlessly with existing infrastructure, making them ideal for the evolving needs of ai-driven data centers. Metered PDUs provide granular data, such as voltage, current, active power, apparent power, energy, and power factor. This level of detail helps identify inefficiencies and optimize resource allocation.

Smart PDUs can reduce energy usage by up to 20%, lowering both operational costs and the carbon footprint of ai operations.

Energy Monitoring with 42U IEC 36 C13 6 C19 3phase Monitored IP PDU

The 42U IEC 36 C13 6 C19 3phase Monitored IP PDU from NBYOSUN stands out as a robust solution for high-density, ai data centers. This device delivers real-time monitoring of critical electrical parameters, including current, wattage, voltage, and frequency. Operators benefit from outlet-level monitoring, remote reboot capabilities, and threshold alerts that prevent overloads and equipment failures.

• 30% reduction in downtime incidents due to real-time monitoring and alerts.
• Annual cost savings of approximately $50,000 by preventing outages and optimizing power usage.
• Environmental monitoring features include temperature, humidity, and smoke detection sensors.
• Remote monitoring and control via LAN, WAN, or Internet.
• Integration with Data Center Infrastructure Management (DCIM) systems for centralized power management.
• Improved power distribution balance across racks, reducing hotspots and enhancing cooling efficiency.
• Optimization of maintenance schedules by analyzing power consumption trends to detect failing components early.
• Support for scalability and future-proofing through cascading and remote management.
• Contribution to lowering Power Usage Effectiveness (PUE), improving overall data center energy efficiency.

The PDU’s scalability through cascading connections supports growing data center power demands. Integration with DCIM systems enables centralized and efficient power management. These features make the 42U IEC 36 C13 6 C19 3phase Monitored IP PDU a key component in supporting the transition to more energy-efficient data center technologies.

Efficiency and Renewable Integration

The transition to sustainable power solutions in ai data centers requires a multi-faceted approach. Operators increasingly rely on renewable energy sources, such as solar and wind, to meet rising power demands. Renewable Energy Certificates (RECs) help companies align with corporate sustainability goals, even when on-site renewable generation is not possible. RECs also increase market demand for renewables, which incentivizes further clean energy investments.

Advanced cooling systems and energy-efficient infrastructure upgrades play a crucial role in reducing operational energy consumption. Digital twin technology acts as a virtual replica of the data center, allowing managers to simulate and optimize cooling strategies and resource allocation. This technology helps prevent energy waste caused by rising power densities from ai workloads and supports the integration of renewable sources by identifying the most effective configurations.

Operators use demand flexibility and grid-aware scheduling to align ai workloads with renewable energy availability. This reduces energy waste and supports the transition to carbon-free energy sources. Policy frameworks, including grid integration requirements, efficiency targets, transparency mandates, and financial incentives, ensure that ai data centers operate efficiently and sustainably. Collaboration between industry and regulators establishes best practices and avoids inefficient infrastructure investments.

Hitachi Energy’s modular data centers, for example, incorporate renewable energy sources and aim for net-zero operations by 2030. Colocation services further optimize resource sharing and energy efficiency, reducing carbon footprints compared to on-premises data centers.

The success of DeepSeek, an ai model optimized for energy efficiency, demonstrates the potential for software and system-level improvements to reduce energy consumption. As ai data centers are projected to consume nearly 9% of total U.S. electricity by 2030, the need for sustainable solutions and the transition to carbon-free energy sources has never been more urgent.

Future of Data Center Demand

Innovation and Industry Collaboration

The future of data center demand will depend on how the industry innovates and works together. Companies now invest billions to meet growing power needs while reducing environmental impact. For example:

• The International Energy Agency projects data center electricity use will rise from 200 TWh in 2022 to 260 TWh by 2026.
• McKinsey expects this number to reach 600 TWh by 2030, which could be 11-12% of all power consumption.
• Google, Microsoft, and Amazon have committed large investments to build data centers powered by renewable sources.
• Google aims for 24/7 carbon-free energy in all its data centers by 2030.
• Microsoft works with local providers to supply 100% renewable energy, including in New Zealand.
• Amazon plans to invest $148 billion over 15 years in sustainable data center infrastructure.

Industry partnerships drive innovation. The table below highlights recent collaborations:

These efforts show that investment and collaboration are essential for meeting future data center demand.

Balancing Growth and Sustainability

Operators must balance rapid growth in data center demand with sustainability. Data centers in the US emitted 105 million tons of CO2 last year, tripling since 2018. About 56% of their electricity comes from fossil fuels. Most data centers operate in regions with high carbon intensity, even as companies buy renewable energy.

• AI data centers are expected to use 90 TWh each year by 2026, nearly doubling global power needs to 96 GW.
• New AI chips use less than one-tenth the energy of older models, improving efficiency.
• Companies optimize workloads, shift processing to edge devices, and right-size AI models to save energy.
• Some countries, like Ireland, have paused new data center construction to manage grid strain. Singapore has raised operating temperatures to cut cooling energy.

Sustainable strategies include using recycled water, aiming for water-positive operations, and exploring new energy sources like hydrogen and geothermal. Xendee’s approach, which combines distributed energy resources and small modular reactors, has cut operational costs by up to 80% in some locations and reduced emissions by 24%. This shows that local solutions can help balance innovation and sustainability.

The transition to renewable energy, smarter chips, and better management will shape the next decade. Industry leaders must continue to invest, collaborate, and innovate to meet rising demand while protecting the environment.

AI-driven growth will double power datacenter energy use by 2030. Rapid expansion in server infrastructure and rising power density drive this trend. Operators face challenges from higher energy costs and the need for sustainable solutions. The table below shows key trends shaping the future:

NBYOSUN’s intelligent solutions help manage rising power datacenter demand. Industry, technology, and policy must work together to ensure a sustainable future.

FAQ

What makes AI data centers consume more energy than traditional ones?

AI data centers use high-performance GPUs and advanced cooling systems. These components require much more electricity than standard servers. For example, a single AI server can use up to 10 kW, while a traditional server uses about 1 kW.

How do NBYOSUN smart PDUs help reduce energy waste?

NBYOSUN smart PDUs provide real-time monitoring and remote management. Operators can track power usage at each outlet. This helps identify inefficiencies and prevent overloads. Studies show smart PDUs can cut energy waste by up to 20%.

Why is real-time energy monitoring important for data centers?

Real-time monitoring allows operators to detect abnormal power usage quickly. This helps prevent equipment failures and downtime. For instance, the 42U IEC 36 C13 6 C19 3phase Monitored IP PDU offers alerts for overloads, which can reduce downtime incidents by 30%.

Can data centers use renewable energy to meet rising AI power demands?

Many data centers now use solar, wind, or purchase Renewable Energy Certificates (RECs). Google and Microsoft aim for 100% renewable energy in their data centers by 2030. Renewable integration supports sustainability goals and helps reduce carbon emissions.

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