The applications of artificial intelligence are growing day by day. Using AI to reduce energy consumption is one of the key applications of this technology, and the addressable market in data centers around the world is significant.
We recently spoke to leading AI applications research firm, Nnaisense, executive vice president Ralf Haller to learn more.
What drives data centers to improve the efficiency of their energy consumption? Cost? Other?
Energy efficiency is now a central issue for data centers, which have often overlooked this issue. For the most part, we can attribute this to two things: cost and climate change.
In terms of costs, the use of data is reflected in the energy costs of running and maintaining data centers. Since most IP traffic passes through data centers, ordering shoes online or watching your favorite Netflix series causes these systems to consume energy. These energy bills are astronomical, especially if the data center is not optimized.
One of the established telecom companies in Switzerland, where Nnaisense is headquartered, is raising about $22.8 million in energy costs annually. Energy efficiency in this context equates to lower costs, and with the technology in place to make this possible, optimizing energy consumption is a real focus for data center operators.
Regarding climate change, the global mission to reach net zero has a major impact on data centers. Corporate images and the responsibility to help save our planet from irreversible damage are at stake. Because data centers use a huge amount of electricity to run and keep their equipment cool, they pose a significant challenge to meeting climate change goals. Precisely for this reason, a growing focus is on improving their energy efficiency.
How can AI help with this?
AI can optimize data centers as complete systems. Today, every HVAC device, which often does nothing at all, runs at full, half, or no speed to cool nearby IT loads with no “knowledge” of the entire data center system.
AI uses sensor input data, such as a rack or room-level temperature, barometric pressure data, environmental weather data, and IT consumption data, to predict any HVAC device’s optimal coolant flow settings. Most importantly, the data center is managed as a whole system with restrictions on each device.
Digital twin technology is a great use case for AI in data centers. A virtual clone of the entire operating system is created using real-time data, allowing the user to achieve optimization through experimentation, which is then applied to the actual data center.
AI installations can be found in data centers, mainly in North America and China. Europe is following suit, with more frequent reports of the successful use of AI in this domain. Europe may well set the trend in this area in the future.
As signatories to the Climate Neutral Data Center Pact Self-Regulatory Initiative Policy Pledge — part of the European Green Deal — dozens of top cloud service providers, from Google and Amazon to down, commit to powering their installations with completely carbon-free energy by the end of 2030.
Microsoft and Google Deepmind are two examples of successful AI implementations to optimize data efficiency. Another good example is a data center energy management solution that Huawei has installed in its Linyi Big Data Center.
According to the company, the AI-based solution alone reduced the UPS power consumption by 40%, “with system efficiency of 97% and module efficiency of 97.5%.” In addition, it implemented a dedicated cooling system that uses deep learning to correlate IT loads, environmental variables, and device capabilities.
AI is used to cool water in the cooling system to achieve maximum energy efficiency, and data collected from sensors “executes the instructions issued by the algorithms, including adjusting the amount of equipment operating; adjusting control loop target values.” such as speed, power, temperature, and differential pressure; and switching the cooling mode.”
AI technology to reduce the carbon footprint of data centers exists today. Huawei uses its AI-based cooling and energy efficiency systems for its internal operations. Still, data center companies worldwide can partner with AI technology organizations to achieve similar results. Regulators will likely demand that they do this anyway, and their customers – businesses and consumers alike – demandpresshat advice do you have to share?
To successfully implement AI-driven optimization technology, working with a highly experienced third-party AI team specialized in this field is essential. In-depth knowledge of AI implementation is an integral part of a successful project.
While AI can automate and optimize many industrial processes, including data centers, expert oversight is a fundamental prerequisite for navigating the installation process, solving problems as they arise, and streamlining maintenance. This is why only major tech companies have implemented AI in their operations to meet their optimization goals thus far.
