Here's How U.S. Data Centers are Going Green Using AI

Here's How U.S. Data Centers are Going Green Using AI
Here's How U.S. Data Centers are Going Green Using AI

Across the globe and in the U.S., companies of all sizes are under increasing pressure from investors and other key stakeholders to minimize the negative impact their operations have on the environment. The data center industry is no exception. 
Data centers consume a vast amount of electricity, accounting for roughly 3% of all electricity generated on the planet. To compare, data centers contribute approximately 2% of global greenhouse emissions, which is the same as the aviation sector. This number is only set to increase as the world embraces the Fourth Industrial Revolution and the Internet of Things, leading to a greater number of objects connected to the Internet and relying on data centers. Some U.S. researchers estimate that this could go up to 14% by 2040, which is around the same proportion of emissions from the U.S. today.
IDC predicts that global data usage will increase from 33 zettabytes in 2018 to 175 zettabytes by 2025–an increase of 430%. Furthermore, the more powerful the data center is, the more cooling it requires. These cooling systems also consume a significant amount of energy, leading some operators to move data centers to colder climates while other operators find less energy-intensive cooling techniques. (Microsoft explored submerging a data center underwater at one point.)
As demand on data centers increases, it’s clear the industry needs to design facilities for maximum energy efficiency and minimal environmental impact. The operation of a “green” data center must also consider the need for its IT technology to use less energy than is needed to cool it. 

How can AI be harnessed to better sustainability? 

A data center powered by artificial intelligence (AI) is nothing new. For some time now, data center operators have been aware of the significant operational benefits of deploying AI in their facilities, and across North America more broadly the AI market was valued at $20.88 billion in 2019 and growing, according to Maximize Market Research. But Gartner estimates that the value of AI is so impactful, it’s possible more than 30% of data centers that fail to sufficiently prepare for AI will no longer be operationally or economically viable by 2020.
AI allows data centers to operate autonomously by automating the routine tasks involved in the maintenance and monitoring of these centers. A common solution is predictive analytics in the form of machine learning. AI can identify anomalies in processes or equipment, such as performance issues or a deterioration in an asset’s health, well in advance of it becoming an active threat. Sophisticated AI can even identify the probable root cause of the problem and recommend a course of action to best remedy and optimize a given situation. Issues can be identified and corrected quickly and accurately before they have an impact on operations. 
AI can also significantly reduce costs by minimizing downtime and increasing output. According to Mike Guilfoyle, vice president at ARC Advisory Group. “I’ve seen a multitude of wide-ranging results, from 50% maintenance work reduction and 5% increased reliability for elevators to $31.5 million in maintenance spend over 3 years for utility wind turbines.”
With the price of storage and computing power plummeting, reducing operational and energy costs can turn a break-even data center into a profitable operation. Indeed, Gartner predicts that data centers that fail to deploy AI effectively will become economically and operationally defunct.

Beyond productivity and profitability

Discussions around the benefits of using AI in data centers often focus on how it can increase productivity and profitability, but neglect to talk about how AI can make a data center more sustainable. In 2020, data centers in the U.S. alone will consume approximately 73 thousand megawatts of electric power.
Using historical data collected from smart sensors such as data output, temperature and humidity levels, AI can train deep neural networks to optimize the performance of a data center and make it more energy efficient. Moreover, prognostic AI can forecast future events such as surges in demand or temperature changes and adapt the system variables accordingly. This prevents the data center from going beyond its operating constraints while also ensuring it operates as efficiently as possible.
Less energy-intensive data centers require less cooling, further reducing total energy usage. AI is already being deployed by some of the biggest players in the industry. In the U.S., Google used DeepMind machine learning in its data centers to directly control the cooling systems. This move resulted in a notable 40% drop in energy consumption. Not only does the implementation of AI constitute a significant cost saving, but more importantly it dramatically reduces harmful emissions and the carbon footprint of companies who rely on data centers. A recent PwC study found that deploying AI across business operations could reduce global greenhouse gas emissions by as much as 4% by 2030–the equivalent to the combined 2030 annual emissions of Australia, Canada and Japan.

Where should data centers start?

To maximize the value from AI, businesses with deployed data centers should first look at their IT and control infrastructures. If they are collecting data from their control systems and/or energy management systems, then they are excellent candidates to benefit from AI and reduce overall energy consumption. As additional sensors are incorporated into their infrastructures, AI can provide increased value and sophistication to achieve a higher efficiency.

  • A fundamental–and recommended–first step in AI implementation is using the technology to detect equipment that waste electricity. AI quickly identifies underperforming assets and assets with maintenance issues, both of which result in excessive power consumption. Across a large data center, the wasted electricity from these types of assets can quickly add up to a significant cost and a significant impact on the environment.

  • Cooling is essential for data centers, especially since temperature hot spots can occur when least expected. As these situations worsen, computer equipment can start to fail, strange anomalies appear, and energy is wasted. Further, as equipment heats up, it can become less efficient, requiring more energy to run. This can result in a bit of an efficiency degradation spiral. Since hot spots often worsen gradually, they can go undetected for quite some time, resulting in potentially serious issues. Automated monitoring with AI analytics is a “best practices” method of early detection of hot spots, resulting in increased operational efficiency and overall energy savings.  New cognitive capabilities are also being developed and enhanced to support this type of discovery. For example, AI-driven vision can pinpoint problems through thermographic detection of hot-spot anomalies, allowing machine vision to replace traditional sensors with improved detection capabilities in certain situations.

  • Enhanced AI capabilities are continually developing and evolving to minimize energy consumption, minimize downtime, and maximize efficiency. Some of the newer areas where AI is helping has to do with balancing load across servers as well as within a given server across multiple CPUs in order to minimize overall heat generation and, thus, power consumption. Power distribution at a data center can also be optimized through AI. Associated anomalies detected through aberrations of multi-variate patterns as well as an overall analysis of situational awareness related to power delivery is an area that is continuing to evolve. Additionally, sensors are becoming more prolific, providing AI with additional “raw material” to perform further analysis and provide more sophisticated insight in order that data centers can continue to expand while using less energy.

There is no doubt that sustainability has become a top priority for business executives, investors and governments alike–in fact, many large American companies are making big commitments. For example, Facebook has said it will have 100% of its data centers running on green energy by the end of 2020, and Microsoft is shifting to 100% supply of renewable energy for its data centers by 2025. Data centers are, and will continue to be, an integral part of the data-driven economy we live in; therefore, finding ways to reduce their contribution to carbon emissions is critical. This is where AI can play a major role. By optimizing operations and increasing energy efficiency, AI can ensure that data centers become more sustainable as the world continues its ascent towards a greener global economy.

About The Author

With more than 30 years of experience in the industrial software sector, Jim Chappell is currently head of Artificial Intelligence (AI) and Advanced Analytics across all AVEVA business units, products and markets. Prior to his current position, he led the Asset Performance Management (APM) suite of software products and related engineering/analytics services for Schneider Electric.
AVEVA is a global leader in engineering and industrial software driving digital transformation across the entire asset and operations life cycle of capital-intensive industries. The company’s engineering, planning and operations, asset performance, and monitoring and control solutions deliver proven results to over 16,000 customers across the globe. Its customers are supported by the largest industrial software ecosystem, including 4,200 partners and 5,700 certified developers. AVEVA is headquartered in Cambridge, UK, with over 4,400 employees at 80 locations in over 40 countries.

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