Introduction—The need for more power has already taken off
The recent national cold snap has renewed concerns about power outages in areas where large data centers have been constructed. As we move deeper into the new year, the relationship between artificial intelligence (AI) and data centers will increasingly become a double-edged sword. But while AI is one of the primary drivers of skyrocketing energy demand, it is also the most effective tool for managing that consumption growth. With data center electricity use projected to exceed 600 – 1,000 TWh globally this year, (roughly equivalent to the electricity consumption of Japan) the industry is, of necessity, transitioning from passive power consumption to AI-driven energy management. This means that energy industry leaders, challenged to identify new ways to keep pace with continued rising demand for AI, will increasingly look to intelligent tools to boost power capacity in ways that are both economically viable and sustainable.
How is all that power used?
Data center power consumption can be broken down into three main areas: IT equipment: the actual servers doing the work (~60%), cooling: the fans and air conditioning that keep things from overheating (~30%), and power infrastructure: UPS systems, lighting, etc. (~10%). AI, though the proximate cause of the data center construction boom, is also emerging as the prime candidate for solving the problem of how better to tackle the emerging energy challenge. Use cases for how AI can help are far ranging.
AI-Driven Cooling Optimization
Cooling historically accounts for 30% – 40% of a data center’s total energy bill. AI has shifted this from a static process to a dynamic, predictive one.
- Neural Networks for Airflow: AI models analyze data from thousands of heat sensors to identify hotspots in real-time. Rather than cooling the entire room, AI models adjust individual fan speeds and floor tile placements to optimize the effectiveness of cooling infrastructure.
- Reinforcement Learning (RL): Google, Amazon, and other large data center operators use RL to learn the most efficient cooling settings. This has led to consistent reductions in cooling energy use and significant improvements in Power Usage Effectiveness (PUE), the ratio of total power consumed by a data center to the power used directly by the computers.
- Liquid Cooling Control: As high-density graphics processing units (GPUs)—required for AI training—move toward liquid cooling, AI manages the flow of coolants at the chip level, predicting thermal spikes before they occur to prevent “thermal throttling.”
Intelligent Workload Orchestration
AI functions as a controller for computer processing tasks, ensuring that servers are neither idling nor being overworked.
- Predictive Scheduling: AI predicts peaks in user demand and schedules non-urgent tasks (like model training) for times when electricity is cheaper or renewable energy is more abundant.
- Server Consolidation: Using machine learning, data centers can pack workloads onto fewer servers, allowing underutilized hardware to enter deep-sleep modes, reducing overall cluster energy consumption by as much as 15%.
ProactiveEnergy Management
Data centers are no longer just consumers from the power grid; they are becoming active stakeholders using AI to balance local energy supply.
- Renewable Integration: AI algorithms forecast wind and solar output with high precision. Microsoft’s Irish data centers, for instance, use AI to switch between the grid and on-site batteries or renewable sources, increasing green energy utilization by 25%.
- Carbon-Aware Computing: “Follow the sun” or “follow the wind” strategies use AI to physically route computing tasks to data centers in regions where the carbon intensity of the grid is lowest.
- Predictive Maintenance: AI-powered normal behavior modeling (NBM) detects equipment degradation (like a failing transformer or clogged cooling pipe) through anomaly detection, preventing the massive energy waste associated with faulty hardware.
Avathon Autonomy Platform for Power & Utilities
Even before the data center construction wave got going in earnest, AI was making its impact felt in the power and utility marketplace, both in traditional (fossil-fueled) generation and in renewables. That’s because data centers aren’t the only thing driving strong increases in demand. There’s also the not-insignificant matter of auto manufacturers transitioning to EVs, an industry expected to consume 2.5% of global power demand by 2030. For the purposes of this discussion, that experience means that the power industry is already well on its way to understanding how AI can help address this perfect storm of emerging demand.
Electricity providers today (both generators and transmission/distribution (T&D) companies) are using AI for a wide range of applications, all of which will play a role in the coming years to ensure that data center operators have the power they need to operate effectively and efficiently.
Predictive maintenance
AI-powered predictive maintenance models use normal behavior modeling (NBM) to monitor system/component performance, flag impending failures well in advance, and provide prescriptive action recommendations that reduce generation losses and prevent catastrophic asset failures. AI-assisted decision making and execution will, with growing experience, move to semi-autonomous, and eventually fully autonomous, operation, improving resolution responsiveness, delivering the uptime and availability critical to data center operations.
Spares planning and readiness
AI-enabled logistics solutions ensure the availability of spare parts in the right location at the right time, including, as well, planning and instructional support for repair personnel, optimizing the balance between availability (inventory and locations) and costs.
Grid optimization and loss reduction
AI-enabled real-time flow optimization assesses optimal paths for power, dynamically adjusting flow voltages to minimize line losses. AI models also analyze real-time customer consumption patterns, weather forecasts, and historical consumption to provide highly accurate load forecasts. This allows T&D operators to purchase power more precisely, avoid imbalances, minimize overloaded equipment, and achieve better integration of power from inconsistent renewable sources
Outage and storm response
AI improves the speed and efficiency of responding to outages or other grid disruptions. By analyzing smart meter and sensor data, faults are quickly located, and crews dispatched. Proactive models can also analyze weather patterns and equipment status to identify high-risk areas for tree/brush clearing and fire mitigation actions. Identifying physical threats like wildfires and ice/freeze damage enables utilities to proactively shut down power and dispatch crews to resolve issues.
Conclusion—AI is part of the challenge and part of the solution
The current massive growth phase of construction virtually ensures that local grids will continue to struggle to keep up with data center interconnections—meaning that AI-driven power management is no longer optional; it is a prerequisite for data center operating success. Ultimately, safe and effective power management will be a team effort involving the utility, grid operator, and data center management. Avathon’s Autonomy Platform delivers comprehensive predictive/prescriptive performance monitoring across the entire power generation and T&D asset base. Operators maximize the value of real-time data from their assets and gain visibility into current and emerging performance trends.
With global AI processing expected to consume as much electricity as a medium-sized country in the coming few years, the expected growth rates could pose severe challenges to data centers as they battle to meet growing customer needs. The energy management tools enabled by AI tools like Avathon Autonomy for Power & Utilities Operations ensure that the net result will be not only sustainable but will also effectively meet the processing needs of a world of AI users.
To learn more about how the Avathon Autonomy Platform helps utilities optimize their generation and T&D performance, visit our site.
