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 accessAbstract
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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![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.](/paper-figures/12d11c591db54a5787f5e397fdcfbb9d/usage-impact-on-data-center-electricity-needs-a-system-dynamic-forecasting-model-figure-4.png)
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“The energy consumption of data centres alone will rise from 200 TWh in 2016 to 2967 TWh in 2030.”
From Energy efficiency in cloud computing data centers: a survey on software technologies (2023) · cites this paper as [2]
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Rogers, E. M., Singhal, A., & Quinlan, M. M. (2019) · Routledge eBooks · doi:10.4324/9780203710753-35 · Cited by 37,670
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Recalibrating global data center energy-use estimates
Masanet, E., Shehabi, A., Lei, N., Smith, S., & Koomey, J. (2020) · Science · doi:10.1126/science.aba3758 · Cited by 1,545
On Global Electricity Usage of Communication Technology: Trends to 2030
Andrae, A., & Edler, T. (2015) · Challenges · doi:10.3390/challe6010117 · Cited by 1,334
Data Center Energy Consumption Modeling: A Survey
Dayarathna, M., Wen, Y., & Fan, R. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2481183 · Cited by 1,165
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A survey of blockchain from the perspectives of applications, challenges, and opportunities
Monrat, A. A., Schelen, O., & Andersson, K. (2019) · IEEE Access · doi:10.1109/access.2019.2936094 · Cited by 1,077
United States Data Center Energy Usage Report
Lawrence Berkeley National Laboratory (LBNL), B., Shehabi, A., USDOE, Smith, S., Sartor, D., Brown, R., Herrlin, M., Koomey, J., Masanet, E., Horner, N., et al. (2016) · doi:10.2172/1372902 · Cited by 1,040
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Sorted by citations
Energy efficiency in cloud computing data centers: a survey on software technologies
Katal, A., Dahiya, S., & Choudhury, T. (2023) · Cluster Computing · doi:10.1007/s10586-022-03713-0 · Cited by 516
“The energy consumption of data centres alone will rise from 200 TWh in 2016 to 2967 TWh in 2030 [2].”
BackgroundThe emerging driving force of energy consumption in China: Does digital economy development matter?
Xue, Y., Tang, C., Wu, H., Liu, J., & Hao, Y. (2022) · Energy Policy · doi:10.1016/j.enpol.2022.112997 · Cited by 402
Liquid cooling of data centers: A necessity facing challenges
Azarifar, M., Arık, M., & Chang, J.-Y. (2024) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2024.123112 · Cited by 231
Du, J., Shen, Z., Song, M., & Zhang, L. L. (2023) · Energy Economics · doi:10.1016/j.eneco.2023.106572 · Cited by 222
Exploring the sustainability challenges facing digitalization and internet data centers
Kez, D. A., Foley, A. M., Laverty, D., Rio, D. D. F. D., & Sovacool, B. K. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.133633 · Cited by 202
Metadata
- Authors
- Koot, M., & Wijnhoven, F.
- Affiliations
- University of Twente (NL)
- Corresponding author
- Wijnhoven, Fons
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2021-06
- Volume
- 291
- Pages
- 116798
- ISSN
- 0306-2619 (ISSN-L)
- 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
- hybrid
- doi.org
- 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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