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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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Cites
Rogers, E. M., Singhal, A., & Quinlan, M. M. (2019) · Routledge eBooks · doi:10.4324/9780203710753-35 · Cited by 37,670
Five disruptive technology directions for 5G
Boccardi, F., Heath, R. W., Lozano, A., Marzetta, T. L., & Popovski, P. (2014) · IEEE Communications Magazine · doi:10.1109/mcom.2014.6736746 · Cited by 3,698
Scenarios: The art of strategic conversation
Schlange, L. E. (1997) · Futures · doi:10.1016/s0016-3287(97)90139-7 · Cited by 1,929
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
Valente, T. W. (2010) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195301014.003.0010 · Cited by 1,143
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
All models are wrong: reflections on becoming a systems scientist
Sterman, J. D. (2002) · System Dynamics Review · doi:10.1002/sdr.261 · Cited by 1,021
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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
Zhu, H., Zhang, D., Goh, H. H., Wang, S., Ahmad, T., Mao, D., Liu, T., Zhao, H., & Wu, T. (2023) · Sustainable Cities and Society · doi:10.1016/j.scs.2022.104322 · Cited by 164
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Freese, T., Elzinga, N., Heinemann, M., Lerch, M. M., & Feringa, B. L. (2024) · RSC Sustainability · doi:10.1039/d4su00056k · Cited by 135
Zhao, N. B., Zhang, H., Yang, X., Yan, J., & You, F. (2023) · Advances in Applied Energy · doi:10.1016/j.adapen.2023.100125 · Cited by 131
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Istrate, I.-R., Tulus, V., Grass, R. N., Vanbever, L., Stark, W. J., & Guillén‐Gosálbez, G. (2024) · Nature Communications · doi:10.1038/s41467-024-47621-w · Cited by 136
Two-phase spray cooling for high ambient temperature data centers: Evaluation of system performance
Kandasamy, R., Ho, J. Y., Liu, P., Wong, T. N., Toh, K. C., & Chua, S. J. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.117816 · Cited by 115
Li, W., Qian, T., Zhang, Y., Shen, Y., Wu, C., & Tang, W. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120787 · Cited by 106
Edge AI for Internet of Energy: Challenges and perspectives
Himeur, Y., Sayed, A. N., Alsalemi, A., Bensaali, F., & Amira, A. (2023) · Internet of Things · doi:10.1016/j.iot.2023.101035 · Cited by 107
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Hnayno, M., Chehade, A., Klaba, H., Polidori, G., & Maalouf, C. (2023) · Case Studies in Thermal Engineering · doi:10.1016/j.csite.2023.102925 · Cited by 78
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Liang, X. P., Goh, H. H., Kurniawan, T. A., Zhang, D., Dai, W., Liu, H., Liu, J., & Goh, K. C. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.133297 · Cited by 69
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hao, Y., Zhou, H., Tian, T., Zhang, W., Zhou, X., Shen, Q., Wu, T., & Li, J. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.125489 · Cited by 65
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Choi, Y. J., Park, B. R., Hyun, J. Y., & Moon, J. W. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.108704 · Cited by 60
Castro, V., Georgiou, M., Jackson, T., Hodgkinson, I. R., Jackson, L. M., & Lockwood, S. (2024) · Energy Policy · doi:10.1016/j.enpol.2024.114404 · Cited by 46
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Liu, M., Liu, H., & Lee, C. (2023) · Energy · doi:10.1016/j.energy.2023.129655 · Cited by 58
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Chen, X., Jiang, S., Chen, Y., Lei, Y. M., Zhang, D., Zhang, M., Gou, H., & Shen, B. (2022) · Energy · doi:10.1016/j.energy.2022.123820 · Cited by 57
Chen, X., Zhang, M., Jiang, S., Gou, H., Pang, Z., Yang, R. H., & Shen, B. (2022) · Energy · doi:10.1016/j.energy.2022.125622 · Cited by 56
Wang, K., Ye, L., Yang, S., Deng, Z., Song, J., Li, Z., & Zhao, Y. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.120414 · Cited by 56
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Chen, X., Jiang, S., Chen, Y., Zou, Z.-C., Shen, B., Lei, Y. M., Zhang, D., Zhang, M., & Gou, H. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.118602 · Cited by 53
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Cho, J., Park, B., & Jang, S. (2022) · Energy · doi:10.1016/j.energy.2022.124787 · Cited by 51
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Kim, J., Choi, H., Lee, S., & Lee, H. (2025) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2025.125476 · Cited by 46
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Aghasi, A., Jamshidi, K., Bohlooli, A., & Javadi, B. (2023) · Computer Networks · doi:10.1016/j.comnet.2023.109624 · Cited by 42
Toorajipour, R., Oghazi, P., & Palmié, M. (2024) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2024.102804 · Cited by 42
Safari, A., Sorouri, H., Rahimi, A., & Oshnoei, A. (2025) · Electronics · doi:10.3390/electronics14112214 · Cited by 42
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Bank, A. D. (2023) · doi:10.22617/tcs230473-2 · Cited by 42
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Ji, H., Chen, S., Yu, H., Li, P., Yan, J., Song, J., & Wang, C. (2022) · Energy · doi:10.1016/j.energy.2022.123599 · Cited by 41
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Jin, H., Guo, J., Tang, L., & Du, P. (2023) · Energy · doi:10.1016/j.energy.2023.129435 · Cited by 40
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Taheri, S. D. M., Nadarajah, S., & Trivella, A. (2024) · European Journal of Operational Research · doi:10.1016/j.ejor.2024.08.002 · Cited by 22
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Xu, R.-Y., Wang, K.-L., & Miao, Z. (2024) · Energy · doi:10.1016/j.energy.2024.133875 · Cited by 22
Ni, D., & Yu, F. (2023) · Thermal Science and Engineering Progress · doi:10.1016/j.tsep.2023.102268 · Cited by 21
Gotsch, M., Gandenberger, C., Serafimov, L., & Miemiec, M. (2022) · Corporate Social Responsibility and Environmental Management · doi:10.1002/csr.2441 · Cited by 21
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Sermsuk, M., Sukjai, Y., Wiboonrat, M., & Kiatkittipong, K. (2021) · Energies · doi:10.3390/en14196269 · Cited by 19
Kontani, R., & Tanaka, K. (2024) · Urban Climate · doi:10.1016/j.uclim.2024.101843 · Cited by 19
Xiao, Q., Li, T., Jia, H., Mu, Y., Jin, Y., Ji, Q., Pu, T., Blaabjerg, F., & Guerrero, J. M. (2024) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2024.3478844 · Cited by 19
Xu, L., Dabuo, F. T., Madzikanda, B., & Appiah-Twum, F. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.144141 · Cited by 18
Hu, H., Chen, F., Ye, G., Mo, T., Li, Y., Zhang, X., Ye, H., Hou, Z., & Zhang, B. (2026) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2026.116942 · Cited by 18
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Qin, S., Klaaßen, L., Gallersdörfer, U., Stoll, C., & Zhang, D. (2021) · doi:10.46855/energy-proceedings-8232 · Cited by 18
Hanna, B. N., Jaoude, A. A., Guaita, N., Talbot, P., & Lohse, C. S. (2024) · Energies · doi:10.3390/en17205073 · Cited by 17
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Yu, Z., Li, Z., & Ma, L. (2023) · Sustainability · doi:10.3390/su15097154 · Cited by 16
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Lee, J., & Kim, J. (2025) · Energy · doi:10.1016/j.energy.2025.138437 · Cited by 15
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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