Research, white papers, reports, and presentations on data centers, energy, and modern infrastructure from around the world.
Academic Paper·DeepMind·2022
A technical paper by DeepMind, Google, and Trane on controlling commercial-building chiller plants using reinforcement learning (RL). The authors describe BCOOLER, a system that generates safe recommendations for setpoints and equipment configurations based on BMS data, energy-use forecasts, and operating constraints. In real-world A/B experiments at two sites, the system reduced energy consumption by 9% and 13%, respectively, compared with Trane’s heuristic Sequence of Operations (SOO).
Reinforcement LearningAI-based HVAC controlChiller plantsData center cooling
Details →White Paper·Siemens Gas and Power·2019
This Siemens Gas and Power white paper examines on-site generation of electricity and cooling for data centers as an alternative to reliance on a constrained or unreliable external grid. Its main focus is on modular gas-turbine solutions, including open-cycle and combined-cycle configurations, combined heat and power (CHP) with absorption cooling, redundancy, and operational lifecycle considerations. The paper also outlines approaches to planning, EPC delivery, service, and decarbonization through biofuels and hydrogen.
Data centersOn-site generationGas turbinesCombined Cycle
Details →Technical Report·Uptime Institute, LLC·2018
The Uptime Institute Standard provides an objective basis for comparing data center site infrastructure topology in terms of functionality, capacity, and expected operational availability. The document defines four Tier levels (I–IV), their required outcomes and validating tests, and explains the practical implications of electrical, mechanical, and distribution-system choices. The assessment applies to the site as a whole, rather than to individual subsystems or IT architecture across multiple sites.
Tier StandardUptime Institutedata center topologyfault tolerance
Details →White Paper·ASHRAE·2021
An ASHRAE technical white paper on the factors accelerating the transition of data centers from predominantly air-based to liquid cooling. The document links rising CPU, GPU, FPGA, and memory power levels to the limitations of air-based systems in terms of airflow, temperature, noise, rack density, and personnel safety. It examines ASHRAE water classes, direct liquid cooling and immersion cooling, and the implications for PUE, TCO, power delivery, heat reuse, and deployment in enterprise, HPC, and colocation data centers.
Liquid coolingDirect Liquid CoolingImmersion coolingData center cooling
Details →Analytical Paper·Deloitte
A Deloitte analysis of the growth in data center infrastructure for AI workloads and its implications for the U.S. power system. It uses DC Byte and Wood Mackenzie data, estimates of AI data center penetration, and projections through 2035 to assess capacity. The focus is on interconnection and generation shortfalls, the impact on utility capital expenditures, and potential sources of and approaches to accelerating power supply for large AI sites.
AI data centersdata center power supplygrid interconnectionbehind-the-meter generation
Details →Government Report·Lawrence Berkeley National Laboratory·2016
This Lawrence Berkeley National Laboratory study, supported by the U.S. Department of Energy’s Federal Energy Management Program, estimates energy use by U.S. data centers from 2000 to 2020. The model covers servers, storage systems, networking equipment, and facility infrastructure, using data on the installed base of IT equipment and PUE by facility type. The report compares the current trend with a counterfactual scenario in which 2010 efficiency levels are maintained, and examines the potential of improved management, best practices, and workload migration to hyperscale data centers.
Data center energy consumptionPUEHyperscale data centersEnergy efficiency
Details →Government Report·International Energy Agency (IEA)
A special World Energy Outlook report by the International Energy Agency examining the relationship between AI development and energy. It analyses growth in data-centre electricity consumption and capital expenditure, the physical constraints on scaling AI infrastructure, power-supply options, and implications for energy systems, prices and emissions. It also considers AI’s impact on energy and industrial efficiency, as well as demand scenarios through 2030.
AI data centersData center power supplyGrid interconnectionLiquid cooling
Details →White Paper·NVIDIA
This NVIDIA white paper provides a framework for building single-tenant enterprise AI factories. It describes a starting reference architecture and an ecosystem approach that brings together accelerated computing, high-performance networking, storage systems, and AI software. The guide is intended for partners and enterprise teams responsible for infrastructure design, procurement, and operations.
AI factoriesAI infrastructuredata centersGPU computing
Details →