DATACENTERS data center
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A power and cooling analysis, also referred to as a thermal assessment, measures the relative temperatures in specific areas as well as the capacity of the cooling systems to handle specific ambient temperatures. A power and cooling analysis can help identify hot spots, over-cooled areas that can handle greater power use density, the breakpoint of equipment loading, the effectiveness of a raised-floor strategy, and optimal equipment positioning (such as AC units) to balance temperatures across the data center. Power cooling density is a measure of how much square footage the center can cool at maximum capacity. The cooling of data centers is the second largest power consumer after servers, with cooling taking about 7% to 30% of energy usage (depending on efficiency), compared to an average of 60% of energy used by servers.
==== Energy efficiency analysis ====
An energy efficiency analysis measures the energy use of data center IT and facilities equipment. A typical energy efficiency analysis measures factors such as a data center's Power Use Effectiveness (PUE) against industry standards, identifies mechanical and electrical sources of inefficiency, and identifies air-management metrics. However, the limitation of most current metrics and approaches is they do not include IT in the analysis. Case studies have shown that by addressing energy efficiency holistically in a data center, major efficiencies can be achieved that are not possible otherwise.
==== Computational fluid dynamics (CFD) analysis ====
Computational fluid dynamics (CFD) analysis uses sophisticated tools and techniques to understand the unique thermal conditions present in each data center—predicting the temperature, airflow, and pressure behavior of a data center to assess performance and energy consumption using numerical modeling. By predicting the effects of these environmental conditions, CFD analysis can be used to predict the impact of high-density racks mixed with low-density racks and the onward impact on cooling resources, poor infrastructure management practices, and AC failure or AC shutdown for scheduled maintenance.
==== Thermal zone mapping ====
Thermal zone mapping uses sensors and computer modeling to create a three-dimensional image of the hot and cool zones in a data center. This information can help identify optimal positioning of data center equipment. For example, critical servers might be placed in a cool zone that is serviced by redundant AC units.
==== Green data centers ====
Data centers use a lot of power, consumed via two main usages: the power required to run the equipment and the power required to cool the equipment. Power efficiency reduces the first category.
Cooling cost reduction through natural means includes location decisions. When the focus is avoiding good fiber connectivity, power grid connections, and people concentrations to manage the equipment, a data center can be miles away from users. Mass data centers like Google or Facebook do not need to be near population centers. Arctic locations that can use outside air, which provides cooling, are becoming more popular. Countries with favorable conditions, such as Canada, Finland, Sweden, Norway, and Switzerland are trying to attract cloud computing data centers.
Singapore lifted a three-year ban on new data centers in April 2022. A major data center hub for the Asia-Pacific region, Singapore lifted its moratorium on new data center projects in 2022, granting four new projects, but rejecting more than 16 data center applications from over 20 received. Singapore's new data centers will meet very strict green technology criteria, including "Water Usage Effectiveness (WUE) of 2.0/MWh, Power Usage Effectiveness (PUE) of less than 1.3, and have a "Platinum certification under Singapore's BCA-IMDA Green Mark for New Data Centre" criteria that clearly addressed decarbonization and use of hydrogen cells or solar panels.
==== Energy reuse ====
It is very difficult to reuse the heat that comes from air-cooled data centers. For this reason, data center infrastructures are more often equipped with heat pumps.
== Power infrastructure ==
The rapid growth of artificial intelligence (AI) and high-performance computing workloads has increased attention on data center power infrastructure, particularly the relationship between electrical reliability, deployment timelines, energy costs, and decarbonization objectives. Industry publications have described phased approaches to infrastructure development that enable operators to deploy power capacity rapidly while maintaining pathways toward lower-carbon energy systems as technologies, fuel availability, grid capacity, and economics evolve.
Recent industry discussion has also emphasized "speed-to-power" as a significant consideration in data center development, reflecting the growing importance of securing electrical capacity and resilient power systems within increasingly compressed project timelines.
== Regulation and policy responses ==