Artificial intelligence in data centers to achieve net-zero carbon

Today, regardless of industry or sector, we all face the same pressures and pain points: rising energy and fuel costs, rising raw material costs, and declining operations and profit margins. At the same time, stakeholders are under pressure to reduce carbon emissions and achieve sustainable development goals.

#Data centers face pressure from all sides, with environmental regulations and businesses demanding greener solutions.

As we all know, data centers are huge consumers of resources, and the demand for the computing power provided by data centers is growing at an alarming rate. As global concerns about climate change increase, incorporating sustainability into strategy is becoming an essential element of data center operations and data center PR.

But will the pressure to achieve the SDGs create additional problems?

Many data center providers already have environmental programs in place. However, a commitment to significantly reducing carbon emissions and a desire to demonstrate quick results often leads to carbon offsets.

Not all emissions can be avoided or replaced, and reducing carbon emissions can involve widespread organizational changes that require time and investment. Therefore, many organizations do go down this path but seize on new initiatives as solutions to improve sustainability. They focus on new discrete projects with easy-to-calculate returns, such as alternative technologies such as electric vehicles (EVs) or replacing lighting with LEDs or renewable energy.

What they often miss is achieving quick sustainability wins in the infrastructure they already acquire by improving energy efficiency throughout their operations.

Harness the power of AI to make better business decisions, faster

The good news is that artificial intelligence (AI)-based solutions can deliver rapid, sustainable growth in just six weeks , and can be easily scaled to unlock efficiency optimization across operations.

Recent advances in artificial intelligence can analyze massive data sets from assets in any industry without the need to deploy large teams of data scientists (whether wind turbines, photovoltaics, jet engines, shipping, oil and gas pumps, cooling or IT servers) ) and use pattern recognition to generate real-time insights. These insights provide the ability to make data-driven business decisions that comprehensively optimize operations to improve energy efficiency, reduce emissions and track progress toward sustainability goals.

Many environments don’t make good use of the data they already have, and hundreds of data feeds are thought to be needed to gather the necessary insights for optimization. In fact, just five data feeds are enough to make significant improvements.

The starting point is to create reliable of data baselines and other internal data sources (ERP, enterprise applications, cloud file storage).

The AI ​​then looks for the most efficient ways to operate equipment and assets, but is not limited by user-defined limits or parameters. By searching for and recommending the closest historical performance, AI can simulate better performance using Pareto front optimization that meets defined quality targets and process limits as well as recommended control set points, resulting in immediate reductions in energy costs and emission.

Reducing energy by optimizing cooling and reducing water use, controlling CPU P and C states to match workload efficiency, and predicting asset failures are just some of the benefits that AI can provide. By operating in closed or open loop, energy savings of 10 -40% can be achieved and costly downtime avoided.

On-premises, hosted and cloud providers and customers can all benefit from artificial intelligence. AI technology accelerates digital transformation, optimizes energy costs and yields, maximizes the renewable energy mix, reduces carbon emissions, and provides reporting on sustainability metrics to track real-time progress toward net zero. More accurate device-level tracking (even down to each individual core) ensures accuracy in billing and Scope 2 and 3 emissions reporting.

For example, QiO works with asset-intensive and energy-intensive industries to deliver AI-driven sustainability. The first rule of increasing sustainability is to figure out how to make better use of what you already have. We believe data is the key to doing more with less, and AI provides the insights needed to navigate to net zero.

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