DeepMind AI slashes cooling costs at Google's data centers

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Google's DeepMind machine learning system has helped cut the cooling costs of the company's data centers

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Deep learning AI has been put to work in intelligent drones, sequencing genomes, learning the tactics of the ancient Chinese board game Go, and even keeping cats off the lawn. Now, Google has set its DeepMind system loose on its massive data centers, and drastically cut the cost of cooling these facilities in the process.

Running Gmail, YouTube, and the all-knowing Google Search guzzles a tremendous amount of power, and while Google has invested heavily in making its servers, cooling systems and energy sources as efficient and green as possible, there's always room for improvement. Especially when the industrial-scale cooling systems are difficult to run efficiently, given the complex interactions that occur between equipment, environment and staff in a data center.

To account for all those factors that a human operator or traditional formula-based engineering might miss, the team put DeepMind to work on the problem, and the result was a drastic reduction in power consumption for the center's cooling systems.

The efficiency was measured by the ratio of the IT department's energy usage compared to that of the entire building – a metric known as Power Usage Effectiveness (PUE). DeepMind networks were fed existing data, including temperature, power and pump speeds, and then trained to focus on the average future PUE, while other systems analyzed data to predict how factors like the temperature and pressure would change over the next hour, and adjust the cooling systems accordingly.

With the PUE plotted out, DeepMind's effectiveness is pretty clear: when the machine learning controls were turned on, the site saw a consistent 40 percent reduction in power used for cooling, a 15 percent reduction in total PUE (after inefficiencies in other departments were accounted for), and a new record for the lowest PUE the center had ever achieved.

Google plans to expand the system more broadly across its own facilities, as well as share the nitty-gritty of how it achieved the energy savings to help other data centers and industrial system operators reduce their energy consumption and environmental footprint.

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