“Driven by the dual pressures of escalating computational demands and urgent sustainability imperatives, the data centre industry is experiencing a renaissance,” says Julien Deconinck, Managing Director at DAI Magister. The potential of AI to enhance data centre sustainability has been overshadowed by the undeniable environmental impact of this technology. But is it as deadly a double-sword as we think?
Data centres, expected to account for 6% of the world’s carbon footprint by 2030, are undergoing a period of transformation, driven by the rise of AI and the pressing need to combat climate change. With such rapid growth comes unforeseen environmental impacts, highlighting the significance of the application of AI technologies in optimising energy use.
It is undeniable that the data-intensive workloads generated by AI will see power consumption soar to unprecedented levels. However, the technology itself can help develop the next generation of data centres that are both high-capacity and more sustainable.
According to Julien Deconinck, Managing Director at DAI Magister, environmental concerns are driving the development of innovative AI solutions that optimise energy usage in data centres, while reducing operating costs.
“Over the next five years, the amount of data generated will surpass the total produced in the past decade, necessitating a significant expansion of storage capacity in data centres worldwide,” said Deconinck. “Another key factor contributing to this rising energy demand is the escalating computational power required for AI training, which is doubling every six months.
“Tech giants, recognising the scale of the problem and their significant contribution to it, are racing to mitigate the environmental impact of their operations. These companies face mounting pressure to reduce their carbon footprint and meet neutrality targets.
“Most data centres aim to operate in a ‘steady state’, striving to maintain consistent and predictable energy consumption over time to manage costs and ensure reliable performance. As a result, they’re dependent on the local electricity grid, where outputs can fluctuate significantly. AI-driven solutions offer enormous potential to address these challenges by optimising energy usage and predicting and managing demand more effectively.
“Integrating renewable energy sources like solar and wind into the grid can improve data centre sustainability, but this presents challenges due to their variable availability. AI addresses this by forecasting renewable energy availability using weather data and predictive analytics. This enables data centres to shift non-critical workloads to peak renewable energy production periods, maximising the use of clean energy and reducing reliance on fossil fuels.
“When assessing the efficiency of a facility, the Power Usage Effectiveness (PUE) measure serves as a crucial metric for indicating output. By monitoring and adjusting operational parameters in real-time, AI sensors autonomously adjust power supply voltages, reducing consumption without compromising performance.
“AI algorithms analysing usage patterns and optimising workload distribution further reduce this energy waste associated with inadequate server management and inconsistent allocation. The optimisation of computing resources in data centres minimises the need for, and use of, excess capacity, both lowering operating costs and maximising performance capabilities.”
AI can also pre-empt system issues that can lead to breakdowns or long-term disruption.
“AI sensors are facilitating predictive maintenance by analysing real-time data to detect anomalies or deviations in consumption patterns. Once identified, AI systems alert the issue to operators, preventing the activation of energy-intensive emergency cooling systems.
“Integration of AI sensors is further beneficial in thermal modelling, enabling dynamic adjustments to systems, accounting for high-intensity computing tasks and external temperature fluctuations by predicting potential hotspots within the facility, based on data collected.
“Together, AI and green technologies are set to revolutionise data centre operations by allowing them to manage larger capacities while reducing their carbon footprint,” said Deconinck. “This not only supports sustainability objectives but also safeguards the transition to low-carbon, high-capacity data centres as the demand for data storage and processing continues to surge brought about by the rise of AI.”
How do you envision AI-driven solutions addressing the challenge of fluctuating outputs from local electricity grids in data centres?
AI-driven solutions offer enormous potential to address fluctuating output challenges by optimising energy usage and predicting and managing demand more effectively. Integrating renewable energy sources like solar and wind into the grid presents challenges due to their variable availability. AI addresses this by forecasting renewable energy supply using weather data and predictive analytics. This enables data centres to shift workloads to peak renewable energy production periods, maximising the use of clean energy and reducing reliance on fossil fuels.
Google is pioneering the use of AI for demand response through its in-house carbon-intelligent computing platform. The platform, which functions like an intelligent task manager for its data centres, leverages real-time data on renewable energy availability and forecasts to shift computing workloads and prioritise using clean energy sources when carbon-free energy is available on the grid.
How do you see AI and other green technologies scaling to meet both storage demands and sustainability goals?
Fluctuating workloads in data centres account for a large proportion of energy waste, causing some servers to become overworked while others remain underutilised. AI algorithms can analyse usage patterns and distribute workloads in real-time, ensuring optimal utilisation of computing resources. This approach reduces energy consumption, improves performance and lowers operational costs by minimising the need for excess capacity.
AI-driven thermal modelling offers transformational improvements, scaling to meet storage demands by enabling real-time, dynamic adjustments to cooling systems. Traditional cooling systems in data centres predominantly rely on static settings that are unable to adapt to real-time conditions, often operating at full capacity, regardless of actual needs. By analysing sensor data, AI creates Digital Twins – virtual models of the data centre environment. These models consider upcoming high-intensity computing tasks, expected external temperature fluctuations and planned maintenance. They simulate various scenarios, predicting temperature changes and potential hotspots within the facility before they occur, allowing for precise and efficient cooling management. By applying DeepMind’s Machine Learning to its data centres, Google reduced the energy used for cooling by up to 40%.
How can data centre operators justify investments in AI sensors for predictive maintenance?
AI enables data centres to conduct predictive maintenance by analysing sensor data to identify patterns that signal potential equipment failures. This early recognition enables proactive maintenance when and where malfunctions will likely occur, preventing the activation of energy-intensive emergency cooling systems and reducing reliance on power-hungry backup systems.
Furthermore, the technology enhances energy anomaly detection in data centres by monitoring real-time data from various sensors and comparing it to established baselines of energy consumption patterns. When deviations are detected, indicating potential issues such as malfunctioning equipment or irregular cooling patterns, AI systems alert operators for swift resolution. This proactive approach prevents prolonged periods of inefficient energy use and ensures optimal equipment operation.
What emerging AI technologies or innovations will be pivotal in further transforming data centres over the next decade?
Workload optimisation, Power Usage Effectiveness (PUE) optimisation, predictive maintenance, anomaly detection and demand response innovations supporting data centres will prove pivotal over the next decade to make data centres more sustainable and efficient. Companies, ranging from innovative start-ups to established players like Google, are leveraging AI and Machine Learning technologies to bring to the market these new technologies.
