Schneider Autonomously Cools Data Centers with AI, Significantly Reducing Power Consumption and Costs

Which company is this a case study of?

Schneider Electric is a global leader in energy management and industrial automation, headquartered in France. The company provides digital solutions that promote energy efficiency and sustainability for homes, buildings, data centers, infrastructure, and various industries. Especially in recent years, Schneider Electric has integrated the use of AI into its entire technology stack, accelerating improvements in operational performance and energy efficiency.

The challenge they wanted to solve

With the widespread adoption of generative AI, data center power consumption is surging. High-density servers, in particular, which process AI workloads, generate a large amount of heat, making cooling a significant challenge. Traditional cooling systems relied on fixed settings or manual human adjustments, unable to respond in real-time to server load fluctuations or external weather conditions, leading to wasted energy and increased operational costs. Amid growing demands for sustainability, it became urgent to reduce energy consumption and CO2 emissions while maintaining stable data center operations.

How AI was used

Schneider Electric leveraged AI to transform building energy management from a traditional 'reactive' approach to an 'autonomous' one. The AI system analyzes diverse information in real-time, such as server load, indoor temperature and humidity, historical operational data, and even weather forecasts. Based on this analysis, machine learning models predict future heat loads and autonomously optimize the operation of cooling equipment. This makes it possible to constantly maintain optimal cooling conditions with minimal energy, without human intervention.

Implementation effects and key takeaways

• ▸Autonomous optimization by AI successfully reduced building operating costs by up to 40%.
• ▸Improved energy efficiency and reduced CO2 emissions contribute to achieving corporate sustainability goals.
• ▸The key point of this case study is that it goes beyond mere data visualization or prediction; AI makes decisions itself and achieves 'autonomous control' by regulating physical equipment (cooling devices).
• ▸For successful AI adoption, clear business needs and the availability of appropriate data for building prediction models are crucial.

What Japanese companies can learn

This approach is applicable not only to data centers but also to any building that consumes large amounts of energy, such as manufacturing plants, large commercial facilities, and hospitals. By combining sensor data with AI while utilizing existing equipment, there is potential to significantly improve energy efficiency. Starting small in a specific area or with specific equipment and then expanding the scope while measuring effects would be a realistic option for many Japanese companies.

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