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Cited by 419Green IT and SustainabilityEnergy Efficiency in ComputingCloud Computing and Resource Management

The DOI matches its Crossref record; 56 of the 57 references it cites were found in a registry or an index.

Usage impact on data center electricity needs: A system dynamic forecasting model

Open access
Koot, Martijn, Wijnhoven, Fons·2021·Applied Energy
DOI 10.1016/j.apenergy.2021.116798
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Abstract

This article presents a forecasting model of data center electricity needs based on understanding usage growth and we conclude that this growth is not fully compensated by efficiency gains of data center technological innovations. We predict a combined growth of data center electricity needs of 286 TWh in 2016 until about 321 TWh in 2030, if all currently known growth factors remain the same. We next run simulations for the end of Moore’s law and the growth of industrial Internet of Things (IoT). The end of Moore’s law results in about 658 TWh for 2030 and an increase of the share of global data center electricity consumption from about 1.15% in 2016 to 1.86% in 2030. A rise of the Industrial IoT may result into total energy consumption of about 364 TWh (about 1.03%) in 2030. Moore’s law and IoT combined cause data center energy needs going up to 752 TWh in 2030, and about 2.13% of global electricity available. Our sensitivity analysis reveals that the future impact of the data centers’ electricity consumption is vulnerable to behavioral usage trends, since the 95% confidence interval of [343, 1031] TWh is relatively wide. Our forecasts, however, exclude the energy needs of mobile devices, edge and fog computing. We offer a system dynamic model and simulation input data selected from the existing literature for replicating this study and applying alternative parameters to it. We further suggest multiple research directions on usage impact on data center energy consumption.

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Crossref · 10.1016/j.apenergy.2021.116798

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Image 1
CC-BY · From the PDF
Fig. 1 A conceptual model for Internet traffic, energy needs, and CO2 emissions.
Fig. 1 A conceptual model for Internet traffic, energy needs, and CO2 emissions.CC-BY · From the PDF
Fig. 2 Simulation model for global data center electricity consumption.
Fig. 2 Simulation model for global data center electricity consumption.CC-BY · From the PDF
Fig. 3 Energy consumption per end-user category predicted with the baseline model.
Fig. 3 Energy consumption per end-user category predicted with the baseline model.CC-BY · From the PDF
Fig. 4 A sensitivity analysis of the baseline model, including 10,000 replications. Note: Including 75%, 95%, and 99% confidence intervals for the randomized user profiles. Three benchmark values from other researchers [1,38] are added for comparison.
Fig. 4 A sensitivity analysis of the baseline model, including 10,000 replications. Note: Including 75%, 95%, and 99% confidence intervals for the randomized user profiles. Three benchmark values from other researchers [1,38] are added for comparison.CC-BY · From the PDF
Fig. 5 Data center electricity needs effects of ending Moore ' s law.
Fig. 5 Data center electricity needs effects of ending Moore ' s law.CC-BY · From the PDF
Fig. 6 Data center energy needs effects of the Industrial IoT.
Fig. 6 Data center energy needs effects of the Industrial IoT.CC-BY · From the PDF
Fig. 7 Effect of both Industrial IoT and Moore ' s law on data center electricity consumption.
Fig. 7 Effect of both Industrial IoT and Moore ' s law on data center electricity consumption.CC-BY · From the PDF

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Metadata

Authors
Koot, M., & Wijnhoven, F.
Affiliations
University of Twente (NL)
Corresponding author
Wijnhoven, Fons
ORCID
Venue
Applied Energy
Publisher
Elsevier BV
Published
2021-06
Volume
291
Pages
116798
ISSN
0306-2619 (ISSN-L)
DOI
10.1016/j.apenergy.2021.116798
OpenAlex
W3136117146
Cited by
403
References
57
Citations by year
2026: 79 · 2025: 99 · 2024: 86 · 2023: 71 · 2022: 46 · 2021: 22
FWCI
16.55
Type
journal article
Language
en
Open access
Licence
Locations
  • Applied Energy (publishedVersion, cc-by)
  • University of Twente Research Information (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • University of Twente Research Information (submittedVersion, cc-by)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Green IT and Sustainability
  • Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
  • Physical Sciences › Computer Science › Information Systems › Cloud Computing and Resource Management
Keywords
energy forecasting, Moore's law, Industrial Internet of Things, data center efficiency, system dynamics modeling, energy consumption projections

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