Applied Energy 291 (2021) 116798

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Applied Energy
journal homepage: www.elsevier.com/locate/apenergy

Usage impact on data center electricity needs: A system dynamic
forecasting modelMartijn Koot, Fons Wijnhoven *University of Twente, the Netherlands

H I G H L I G H T S• A system dynamic model for policy makers and researchers to simulate data center energy scenarios.
• A user behavior perspective on data center energy needs.
• User behavior increases data center energy needs from 292 TWh in 2016 to 353 TWh in 2030.
• The end of Moore’s law and IoT combined cause data center energy needs going up to 1287 TWh in 2030.

A R T I C L E I N F O

A B S T R A C T

Keywords:
Data centers
Data center electricity needs
System dynamic forecasting
Moore’s law
Internet of Things

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% in2016 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 ofthe 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 theexisting 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.

1. Introduction
The increasing usage of consumer and business applications is
associated with more computational tasks and higher storage demands
by data centers, resulting into higher data center electricity consump­tion. Masanet et al. [1] conclude that despite a massive growth of data
storage (25-fold with only 3-fold increase in energy), IP traffic (10-foldgrowth with only a marginal increase in energy used), and data centercompute instances (6.5-fold with 25% energy usage increase), the totalenergy consumption of data centers increased only 6% between 2010
and 2018 (from 194TWh to 205TWh). This remarkable result is nearly
fully explained by the large energy efficiency gains of data processing

and data center infrastructures (mainly cooling and uninterrupted
power supplies (UPS), but their predictions exclude data center externalenergy needs for “user-to-data center” and “data center-to-data-center”
IP traffic.
The speedy growth of data center usage may result in a growth ofenergy needs of data centers when energy efficiency gains do notcontinue as well as they did in the past. Moore’s law –which predicts a
25% annual energy decline per processing unit (or as often said a per­formance doubling each 2 years)– is expected to stop having an influ­
ence by 2021 and 2023 [2]. Given this trend, one may wonder if Internet
users will be confronted with a scarcity of internet energy resources in
the future [3]. Data center energy needs predictions for the next decade,* Corresponding author at: University of Twente, Faculty of Behavioural, Management, and Social Sciences, Drienerlolaan 5, 7552 NB Enschede, the Netherlands.
E-mail address: a.b.j.m.wijnhoven@utwente.nl (F. Wijnhoven).
https://doi.org/10.1016/j.apenergy.2021.116798
Received 12 December 2020; Received in revised form 26 February 2021; Accepted 5 March 2021
Available online 25 March 2021
0306-2619/© 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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Applied Energy 291 (2021) 116798

however, lack consensus as some authors suggest a stable energy need ofabout 200 TWh in 2030 and others forecast about 10 times as much [4].
This article, therefore, aims at answering the research question: Howwill future data center energy demand evolve as a consequence of its
growing usage?
An extensive review of data center energy consumption models isfrom Dayarathna et al [5]. They identify studies that focus on hardwarecentric and software-centric power models. The hardware-centric
approach starts from the digital circuit level and moves on to describe
higher-level energy consumption models at the hardware component
level, server level, data center level, and system-of-systems level. The
software-centric approach describes power models developed for oper­
ating systems, virtual machines, and software applications. This meansthat these models do not analyze the usage side of data center energy
consumption. In our view, this is comparable to understanding the en­ergy efficiency of a car as-a-machine versus understanding the driver’s
behavior that causes an even extremely efficient machine to consume
much. Increasing consumer demand also will need more machines doing
the processing. Our study thus delivers an important usage perspective
to previous data center modelling work.
With usage here we mean both industrial and consumers requests for
IT storage, networking, and processing. These requests enter data cen­ters to a large part via “user-to-data center” IP traffic, which next causes
storage, processing, and networking activities by data centers. The usage
demand growth has an exponential nature because of mutual growth
reinforcement effects between capabilities being offered, like hardware,storage and communication speed, and opportunities of using these for
new applications and software. Such reinforcements between techno­
logical innovations have been observed frequently as explanations forthe exponential growth of technological markets [6] and internet-based
services [7]. Exponential growth patterns at a certain stage of industrial
maturity result in a balancing force because of the development of
resource scarcity and market saturation [8], which are typical system
dynamic model patterns [9]. A similar pattern of resource scarcity,
especially electric power, may slow down the exponential growth of thedata center market as well.
This article is structured as follows. First, previous research in thefield of data center energy consumption is presented. After that, thesystem dynamic methodology is explained by which we model andsimulate user driven data center energy consumption. This is followedby two results sections, one section about the design of the model andone section about simulations until 2030. We next discuss our resultswith outcomes of previous studies and present a list of approaches forcoping with potential problems in the future.

phase has evolved from 2007 until 2012. Following their estimates, theyearly growth of communication networks, personal computers, anddata centers is 10%, 5%, and 4% respectively, and thus is higher than thegrowth of worldwide electricity consumption in the same time frame(3%). The relative share of these three ICT products and services in the
total worldwide electricity consumption has increased from about 3.9%
in 2007 to 4.6% in 2012.
For analyzing the volume of greenhouse gas emissions (GHGE),
Belkhir and Elmeligi [3] estimated the energy needed for ICT component
manufacturing, i.e., production energy (PE), which is a fixed single timeemission per device produced, and use phase energy (UPE) costs, which
is an annual recurring volume. A third parameter they use for estimating
GHGE is the useful life (UL) of devices, which if shorter causes a more
frequent rebuy and therefore more production energy consumption.
Belkhir and Elmeligi [3] determined the UL and the installed base ofeach device. The combined data resulted in estimations that the ICT
GHGE relative contribution could grow from roughly 1–1.6% in 2007 to
exceed 14% of the 2016-level worldwide GHGE by 2040, accounting for
more than half of the current relative contribution of the whole trans­
portation sector. Their study also highlights the contribution of smart
phones and shows that by 2020, the footprint of smart phones alone
would surpass the individual contribution of desktops, laptops and dis­
plays. Finally, they offer actionable recommendations for mitigating the
ICT explosive GHGE footprint through a combination of renewable en­ergy use, tax policies, managerial actions, and alternative businessmodels.
The growing data center electricity demand is also discussed byMorley et al [12], who focused on determining the daily peak demandfor data and therefore the peak of the electricity demand of data centers.
These demand peaks are especially caused by the large volume of datatransfer for video streaming and video interaction, i.e., user-to-data
center IP traffic. Their study calculates that containing the overall
growth in energy demand across digital infrastructures depends on morethan technological efficiency alone; it also requires limiting the growthin traffic to at least keep in step with efficiency improvements, a balance
which has not so far been the case.
Much of this discussion has also been described and analyzed in a2015 paper of Andrae and Edler [13]. For the electricity usage of thetotal communication technology sector, they predicted an absolute risefrom 2,000 TWh in 2010 to 8,000 TWh in 2030. Data centers’ energy
usage alone would consequently grow from 200 TWh in 2016 to 2,967
TWh in 2030. However, their predictions have high uncertainty andhave been revised in 2019 to 974 TWh for data centers in 2030 [14].
Additionally, Andrae [15] expects 3,234 TWh in 2030 for the fullInternet (i.e., fixed and mobile Internet, devices and WIFI). Andrae andEdler state that for the worst-case scenario, the different communication
technology components —i.e., use and production of consumer devices,
communication networks, and data centers— could use as much as 51%
of global electricity in 2030 if not enough improvement in electricity
efficiency of wireless access networks and fixed access networks/data
centers is realized. In the worst-case scenario, communication technol­
ogy electricity usage could contribute up to 23% of the globally released
GHGE in 2030. These insights have been much debated by experts fromthe data center industry, who point at the efficiency gains by Moore’s
law and improvements in data center infrastructure of about 20%
annually [1], but the gains from Moore’s law flatten according to Shalf[2] and Markov [16], who predict Moore’s law to be fully out of order by
2021, a view shared by Shahidi [17] later.
Clearly, there is much diversity on these forecasts. Hintemann and
Hinterholzer [4] state that in the “best case” the energy consumption ofdata centers can remain constant, but if the current developments
continue, the energy consumption of data centers will double by 2030
compared to 2019.
The diversity of these predictions is large and calls for a transparent
and public model that researchers and policy makers can use to workwith their own assumptions. Additionally, such a model should not stick

2. Literature review
We studied a variety of future estimations of data center usage
behavior, data center energy use, and electricity production capacity via
a systematic literature search in Scopus, Web of Science and Google
Scholar and present them in their sequence of publication.
Hinton et al. [10] state that in 2011, the ICT sector is responsible forabout 5% of the total electric power consumption in developed econo­mies. 1% of the total electric power consumption was caused by theInternet, but this percentage was expected to rapidly increase under the
influence of increasing data access rates. This may become an untenable
development, because the expected 10% efficiency gains will notcompensate for the predicted 40% traffic growth [10]. Consequently,
they express an urgent need for reducing data center power consump­tion when workload is low, further improving the energy efficiency ofcore routers, and the deployment of more energy-efficient accessnetwork technologies.
Van Heddeghem et al. [11] analyzed the electricity consumption of
ICT worldwide. They estimated that global electricity demand is
growing slower than the electricity consumed by digital devices andinfrastructure. They assess how ICT electricity consumption in the use
2

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Applied Energy 291 (2021) 116798

at being at a top-down level but needs to open up the black box of data
centers and allow predictions for different data center components anddifferent usage categories of data centers.

traffic expressed in Exabytes (EB) that correspond with different user
applications. The IP traffic stock is annually increased by a growth factor
and causes a certain level of electricity need not only for its own energy
but also for the server workload and required storage. Additionally, thisprocessing, traffic and storage produces heat as a by-product and thus
needs cooling. Besides of this need for cooling, a data center’s infra­
structure has energy needs for its building, light, security, and otherbuilding-associated energy needs. Moore’s law is supposed to directlyreduce the energy needs of servers and this variable is presented as aconverter variable in our model, i.e., we define the level of Moore’s law
influence differently for different years between 2016 and the end of thesimulation. We next can determine the share of electricity consumptionof the data centers per year by comparing the energy needs of the data
centers with the total global electricity production capacity, which we
believe to be growing each year with a certain percentage. In system
dynamic terms, we may see a growth of energy consumption of data
centers by increased server loads, traffic, and storage, but we may also
see balancing mechanisms like Moore’s law and infrastructure efficiency
gains.
Clearly, such a top-down model is simplistic as more external factors
may influence data center energy needs and different components of adata center will use different levels of energy. Consequently, in the next
sections we present a more bottom-up model for data center energy
needs. Such a bottom-up approach will model energy needs per data
center component (i.e., server, network, storage, and infrastructure) andsummarize these results for a data center total. Per component also othervariables than IP traffic may cause its energy need. Besides of further
elaborating on the structure of such a model, values for variables, stocks
and flows will have to be found from reliable information sources, like[1]. To implement our focus on application behavior, we added Cisco
Systems [27] data on workloads per application, and we added datafrom Aslan et al. [28]. to cover data on IP traffic between data centersand between data centers and users. Very likely, these values will be
open for debate and thus a point prediction will be much under debate.
Consequently, as common in system dynamic studies [9], the model will
be used to calculate-through multiple usage growth scenarios. This
article thus will further continue with the design of our model in Section
4, the simulations in Section 5, and finally a discussion and conclusions
Sections 6 and 7.

3. Methodology
Our goal is to develop an energy forecasting model for global data
centers, with a focus on data center usage impact. In contrast to naturalscience predictions, forecasting human behavior and usage of data
centers is difficult as diverse economic, cultural, and technological de­velopments may change people’s preferences [18]. The strategic man­agement discipline, which also tries to understand longer term humanbehavior, therefore has developed so-called scenario development
methods that are used to produce multiple competing but plausible
scenarios of the future [19,20]. System dynamic models are thinking
machines for answering “what if” questions about possible futures thatare hard to create and experiment with in a non-virtual way [21,22] andaim at representing the nonlinear behavior of complex systems over timeusing variables that influence the flows (i.e., the volume changes) ofstocks [23,24]. Non-linear systems have no proportionality and nosimple causality between the magnitude of responses and the strength oftheir stimuli: small changes can have striking effects, whereas great
stimuli will not always lead to drastic changes. This way of viewing andmodeling is appropriate for many systems when multiple influences
have reinforcing and balancing effects, which we assume in data center
energy forecasting.
There are many tools for system dynamics modeling, but throughout
this article we will work with Insight Maker, which is a free-ware, Web
2.0-based, multi-user, general-purpose, online modeling, and simulation
environment. For introductions and illustrations of Insight Maker, thereader may be interested in [25,26] and the free interactive Insight
Maker tutorial http://beyondconnectingthedots.com/.
System dynamic modeler Insight Maker presents stocks, like mate­rials, customers, or money, graphically by rectangles. Flows are pre­sented by bolded solid lines with arrows that give the direction of the‘material’ flows. Variables are graphically portrayed by ovals; they canbe dynamically calculated values that change over time or they can beconstants, e.g., IP growth rates. Links are graphically shown by dashedlines with arrows that show the transfer of information between the
different primitives in the model. Thus, we can express data center en­
ergy system dynamics in a general top-down way by the Insight Maker
language as we do in Fig. 1. In our data center usage impact model, we
see a stock volume of application behavior as initiated by users as thestart for all internet traffic and data center activities.
In the top-down model of Fig. 1, we start with a certain stock of IP

4. Design of a system dynamic model for data center energy
forecasting
For developing a bottom-up model, we further decompose the highlevel data center model of Fig. 1, and further describe each of its

Fig. 1. A conceptual model for Internet traffic, energy needs, and CO2 emissions.
3M. Koot and F. Wijnhoven

Applied Energy 291 (2021) 116798

components, i.e., application behavior, servers’ application workload,
networking, storage, and infrastructure. At the end of this section, we
integrate the resulting sub-models into one integrated model for fore­
casting the global data centers’ electricity consumption (Fig. 2).

traditional data centers in the industry. Traditional data centers have 2.4
workloads per server disk and the efficiency growth rate of workloads
per server is 6.9%. Cloud data centers had 199.4 million of workloads in2016 and a growth rate of 22%. The number of workloads per server disk
are 8.8 and efficiency growth rate is 8.5%. This would mean that clouddata centers are more efficient in their server services than traditional
data centers. The number of workloads also differs between applications(see Table 2). Shehabi et al. [29] found different industry electricity
consumption parameters for different server ranges, where traditional
data centers, –i.e., those datacenters run internally by an organizationand mostly with a size of less than 2,000ft2 in size – on average had
severs that consume 229.84 W per server in 2016, which increased by
3.6% per year since then. Non hyperscale cloud data centers, which havea size of between 2,000 and 20,000ft2 mostly run by specialist data
center services providers, have servers that use on average 302.75 W per
server in 2016 and became 1.9% more efficient per year since then. For
the very large scale hyperscale data centers, these number are respec­tively 253.49 and − 0.4%.

4.1 Application behavior parameters
Application behavior consists of activities like the use of data pro­cessing servers that cause workloads, the storage of data, and datatransfers within and between data centers. Indirectly, workloads cause
needs for cooling and other infrastructure services. Following Cisco
Systems [27], we identify 8 types of application behavior. The first 4 areconsumer-oriented behaviors consisting of search, social networking,video streaming and several other consumer apps. The last four are(smart) industry behaviors, consisting of cloud-ERP and business ap­
plications, databases, analytics and IoT, collaboration software andcomputations. Table 1 gives a selection of the Cisco Systems data fordata center application workload, storage, and networking from 2016
until 2021 for consumer and business usage.
The annual growth rates (CAGR’s) for application workloads, stor­age, and networking are 18.6%, 31.2% and 24.7% respectively. These
parameters will be further detailed per data center component in thefollowing subsections.

4.3 Networking parameters
The total data center data traffic combines the traffic between data
centers and users, data centers and data centers, and within data centers.
The first two is named IP traffic. According to Cisco Systems [27], mo­bile traffic will seven-fold between 2017 and 2022, assuming that there
will be 1.5 mobile devices per capita. By 2022, 5G will generate 2.6
more traffic than a 4G connection on average [27]. Using these insights,
Cisco predicts a compound annual IP traffic growth (CAGR) of 22%,starting with 1,153 EB for 2016. Besides, Masanet et al. [1] base theelectricity consumption of data center networking on the number of data
center networking ports used. However, there is more involved in
networking energy usage of data centers than port traffic power (seeTable 3).
Aslan et al. [28] searched for a value for the electricity consumptionof IP traffic that takes both the time used and the data volume. They
conclude an electricity consumption of 0.06 TWh/EB that also decreases
by half every 2 years [28]. When taking the electricity consumption of
Aslan et al. and the total IP traffic according to Cisco Systems, we
calculate (998 + 679) EB with 0.06 being the TWh/EB factor [28],4.2 Servers’ application workload parameters
Server electricity needs are caused by the volume of workloads thatdata centers process and the total number of servers needed for theseprocessing workloads. Our assumption here is that a server disk runs atfull capacity, but we adjust the watt used per server following [29] tocompensate for idle time. To calculate the electricity consumption, thevalue of electricity consumption of a server disk is needed. The relation
between the total volume of server power consumed depends on the
total number of workloads, which is increasing as a consequence of more
intensive use of data processing servers and the productivity of servers
which enables more workload to be processed by the same unit of server.
Cisco Systems [27] identified 42.1 million of workloads for tradi­tional data centers with a negative growth rate of 5% on the total
amount of workloads, which is consistent with the shift away from

Fig. 2. Simulation model for global data center electricity consumption.
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Applied Energy 291 (2021) 116798

Table 15-year CAGR for datacenter workload, storage and networking.
Application workload in millions

2016

2017

2018

2019

2020

2021

5-year CAGR

Consumer total
Business total
Total
Application storage in EB
Consumer total
Business total
Total
Application network in EB
Consumer total
Business total
Total

58
184
242
2016
143
520
663
2016
4,501
2,319
6,820

77
226
303
2017
193
697
890
2017
6,156
2,931
9,087

98
274
372
2018
265
915
1,180
2018
8,052
3,505
11,557

115
318
433
2019
342
1,156
1,498
2019
10,0544,070
14,124

133
362
495
2020
455
1,516
1,971
2020
12,401
4,716
17,117

152
416
568
2021
591
1,982
2,573
2021
15,107
5,449
20,556

21.3%
17.7%
18.6%
5-year CAGR
32.8%
30.7%
31.2%
5-year CAGR
27.4%
18.6%
24.7%

Source [27].
Table 2Server parameters.

Table 3
Networking parameters.

Class

Sub-class

Year
2016

5-yearCAGR

SourceClass

Sub-class

Application workload(millions)SearchSocial Networking
Video Streaming
Other consumer
apps
ERP & business
apps
Database,analytics, IoT
Collaboration
Compute
Total
Traditional
Cloud
Hyperscale
Total
Traditional
Cloud
Hyperscale
Traditional
Cloud
Hyperscale
Traditional
Cloud
Hyperscale

10
12
18
18

14.9%
25.9%
23.6%
18.5%

[27]

Application traffic in
EB

57

18.6%

33

21.4%

48
46
242
42.1
103.9
95.5
242
17%
43%
40%
2.4
8.8
8.8
230
303
253

14.4%
17.0%
18.6%
− 4.8%
12.9%
29.2%
18.6%
− 19.7%− 4.8%
8.9%
9.6%
8.4%
8.4%
3.6%
− 1.9%− 0.4%

Search776
Social Networking
931
Video Streaming
1,397
Other consumer
1397
apps
ERP & business
718
apps
Database, analytics,
416
IoT
Collaboration
605
Compute
580
Total
6,820
Data center to user
998
Data center to data
679
centerWithin data center
5,143
Total
6,820
Data center to user
0.06
Data center to data
0.06
centerWithin data center = port power.
Traditional
1.71
Cloud
2.58
Hyperscale
3.19
Average
4.53

DC type workload(millions)
DC type workload(frequency)
Server productivity
Average server power[27]

Network traffic in EB[27]

Traffic energy rate in
TWh/EB[27]

Average port power
in W/port[29]

Ports per server

resulting in 100.62 TWh used for IP traffic towards data centers for2016. Cisco estimates an average growth rate of 25.2% for data-to-usertraffic and 32.7% annual growth rates for data center-to-data center
traffic.

Year
2016

5-yearCAGR

sources

20.7%
32.3%
29.9%
24.5%

[27]

19.6%
22.3%
15.3%
17.9%
24.7%
25.2%
32.7%
23.4%
24.7%
− 28.5%− 28.5%− 5.9%− 9.5%− 6.5%
1.3%

[27]

[28]

[30]
[30]

Table 4Storage parameters.

4.4 Storage parameters
Data center storage demand in exabytes is determined by multi­
plying the number of exabytes per workload with the application
workloads requested and dividing this by the capacity per driver (no idle
capacity assumed). Masanet et al. [1] give that different applications
have different storage volume needs, where search has the lowest stor­
age need of 23 EB with an annual growth rate of 26.7% until 2021, andsocial networking and video streaming with the highest growing storage
needs of 35.6% and 37% annually until 2021 (Table 4). Regarding an
estimate of actual energy costs of storage, the type of storage medium isimportant. Shehabi et al. [29] mention a 40–50% division of HDD and
SSD drivers in data centers. The energy consumption of SSD drives re­duces each year by about 2.3% and for HDD it reduces by 5.3% and SSD
start with an average of 6.0 W per drive in 2016 and HDD starts with anaverage of 8.1 W per driver. The number of SSD increases with 9.5%
annually and the number of HDD decreases by 2.9% annually.

Class

Sub-class

Year
2016

5-yearCAGR

SourceApplication
storage in EB

SearchSocial Networking
Video Streaming
Other consumer apps
ERP & business apps
Database, analytics, IoT
Collaboration
ComputeTotal
Solid state drives (SSD)
Hard disk drives (HDD)
Operational energy as afraction of storage energy
Solid state drives (SSD)(=100%-HDD%)
Hard disk drives (HDD)
Solid state drives (SSD)
Hard disk drives (HDD)

23
29
48
43
148
128
90
154
663
1.38
3.78
28.5%

26.7%
37.0%
35.6%
29.4%
31.5%
30.6%
33.3%
28.2%
31.2%
35.3%
27.0%
− 3.6%

[27]

19%

9.5%

[30]

81%
6.0
8.1

− 2.9%− 2.3%− 5.3%[30]

Driver capacity in
TB/drive
Internal storage
proportion
Driver capacity
frequency
Driver power in
W

5

[30]
[30]

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4.5. Infrastructure parameters5.2. Data center energy forecast – Baseline model

Infrastructure energy needs consist of all data center energy needs
that are not directly caused by server processing, storage, or network
activities. Some of the main infrastructure components are cooling,light, security and building heating. The most common measure for data
center infrastructure energy performance is power usage effectiveness(PUE) measured by the total amount of energy used by a data center
divided by the energy used by its IT equipment [31]. Data centers differ
highly per PUE, as given in Table 5. Much infrastructure energy costs
reductions are gained by the generation of economies of scale, but there
is no proof that this will continue in the same speed as for 2010–2018,
and PUE’s of 1.2 for hyperscale data centers cannot go better as 1.0,
which would indicate zero infrastructure energy consumption.

We simulate the data centers’ energy consumption for the period
2016–2030 with the model of Fig. 2 and assuming that future techno­
logical and behavioral trends are maintained. Fig. 3 gives the annual
data center energy consumption (in TWh) for the baseline model.
The majority of the applications’ workloads are processed byhyperscale servers, increasing their relative share from less than 50% in2016 to over 80% in 2030.The total workload starts with 242 million in2016 and ends with 2,760 million in 2030. For traditional, cloud andhyperscale, the starting values for 2016 electricity consumption arerespectively 35.48, 25.21, 29.25 TWh and move to 9.18, 32.29, and177.59 TWh in 2030.
The data centers’ networking is represented by IP traffic and will
become larger over time [27], starting with 5,143 EB in 2016 andgrowing to 97,640 EB in 2030. The data centers’ external IP traffic will
also accelerate growing from 998 EB for data center-user traffic and 679
EB data center-data center traffic in 2016 to 23,210 EB and 35,650 EB in
2030 respectively. Because of the declining energy costs of the data
centers’ external IP traffic (about 28.5% per year) and the increased
energy efficiency of server ports (about 7.3% annually), the high traffic
growths do not contribute much to the energy needs of data centers. The
different data centers however do have different total energy costs
internally due to the global workload allocations, for hyperscale we
predict about 5.02 TWh in 2030, for traditional and cloud respectively
0.10 and 0.48 TWh.
The increase in data storage demand is for traditional, cloud andhyperscale data centers respectively from 118.93, 235.63, and 309.14
EB in 2016 to 368.47, 5,023.40 and 24,840.67 EB in 2030. This sharp
increase in storage demand does not result into a rising electricity de­mand for storage devices, mainly due to the implementation of moreenergy efficient SSD devices. Consequently, the storage-related elec­
tricity consumption drops from 18.33 TWh in 2016 to 15.23 TWh in
2030. Because of the global workload allocations, hyperscale data cen­ters consume the largest part of the storage related energy consumption.
Hyperscale data centers will consume 12.72 TWh in 2030, while tradi­tional and cloud data centers only consume 0.13 and 2.39 TWh
respectively.
The data centers’ total energy predictions thus differ for traditional,
cloud and hyperscale data centers and goes from 83.73, 53.98, and48.06 TWh in 2016 to 17.16, 50.71, and 220.32 TWh in 2030. The global
data centers’ energy consumption thus will grow with approximately
12% in between 2016 and 2030, if (and only if) today’s technologicaland behavioral trends remain unchanged for the upcoming decade.

4.6 Total data center energy consumption calculation
The total data center energy usage is a sum of its sub-model outcomes
and their interaction, reinforcements, and balancing effects. We calcu­
late the total energy costs with the complete model of Fig. 2. The users’
behavior implicates that the data centers’ electricity needs arise fromthe number of workloads demanded by customers and business practi­
tioners. Therefore, we first calculate the storage capacity and corre­
sponding IP traffic required to process one workload only. Second, we
aggregate all these workloads to determine the impact in terms of server,
storage, and network activities. Finally, the PUE values from Table 5 areadded to conclude the total data centers’ electricity needs with the in­frastructures’ share. The share of data center electricity consumption is
calculated by dividing the predicted datacenter electricity needs foreach scenario with the product of the actual electricity production vol­ume for 2016 of 25,000TWh and the annual 2.5% growth factor ofelectricity production. Electricity production volumes and growth rates
are taken from [32].
5. Simulations
The simulation model in Fig. 2 is used for two objectives. First, wepredict how the data center electricity demand evolves if both today’s
technological and behavioral developments remain, i.e., the baselinemodel. Second, the model allows us to run a variety of scenarios andsensitivity analyzes. In Section 5.1, we first elaborate on the model’s
input parameters. The results of the baseline model are presented in
Section 5.2, while a sensitivity analysis regarding the user’s application
behavior is discussed in Section 5.3. Finally, we propose two alternative
scenarios referring to Moore’s law and the rise of the Industrial IoT in
Section 5.4.

5.3. Sensitivity analysis of the baseline model
A major contribution of our simulation model is the association of
the data centers’ electricity consumption with the users’ application
behavior. A comparison of multiple Cisco Systems forecasts indicatesthat estimations of users’ demand for data center servers depend on bothtechnological and behavioral trends [34,34–37]. The observed sto­
chasticity of the user’s application behavior makes it hard to give areliable estimate of the data center electricity consumption in 2030 (seeTable 7). However, we can derive the sample variance and standard
deviation for the model’s CAGR values by comparing Cisco Systems
behavioral forecasts over several years [34,34–37]. This procedure en­ables us to simulate the baseline model with a randomized set of growthvalues for the users’ workloads, IP traffic, and storage activities per
application.
The growth projections in Table 7 allows us to randomly generate afuture scenario by slightly altering the users’ application CAGR values.
For each application, we add a random error value to the application’s
average CAGR values. The error is normally distributed with μ = 0%,and either σ = 3.163%, σ = 2.091%, or σ = 1.469% for the application’s
workloads, IP traffic, or storage activities respectively. The result of a

5.1. Simulation settings
For simulating the future energy consumption of data centers, we use
a time window until 2030, because the electricity suppliers’ demand
response time is between 5 and 8 years [33]. Additionally, we expect
much of energy scarcity issues to show up after 2025, as will be shown
later in the simulations. For these simulations, we present the input
values for all variables of our model in Tables 2–5. Each variable isassociated with an initial value originating from 2016, and a 5-year
CAGR value.
Table 5
Infrastructure parameters.
Average PUE value

2016

5-year CAGR

sourceTraditional
Cloud
Hyperscale

2.10
1.66
1.21

− 1.0%− 1.0%− 0.5%[27]

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Fig. 3. Energy consumption per end-user category predicted with the baseline model.
Table 7
Cisco Systems forecasts of the users’ application behavior [34,34–37].
Workload in millions

2010

2011

2012

2013

2014

2015

2016

2017

2018

2019

2020

2021

5-year CAGR

Forecast in 2011
Forecast in 2012
Forecast in 2013
Forecast in 2014
Forecast in 2015
Forecast in 2016
Forecast in 2018
IP traffic/ workload in EB
Forecast in 2011
Forecast in 2012
Forecast in 2013
Forecast in 2014
Forecast in 2015
Forecast in 2016
Forecast in 2018
Storage/workload in EB
Forecast in 2016
Forecast in 2018

57.5
–
–
–
–
–
–
2010
19.8
–
–
–
–
–
–
2010
–
–

70.2
71.1
–
–
–
–
–
2011
23.5
24.7
–
–
–
–
–
2011
–
–

85.8
86.6
83
–
–
–
–
2012
26.1
29.5
30.9
–
–
–
–
2012
–
–

107.4
108
99.3
108.3
–
–
–
2013
27.5
30.1
33.6
28.3
–
–
–
2013
–
–

131.2
131.6
119.5
125.2
129.5
–
–
2014
28.5
31.3
35.3
30.6
26.6
–
–
2014
–
–

154.8
155.1
140.4
143.6
161.8
175
–
2015
30.7
33.8
37.1
32.9
27.3
26.7
–
2015
2.2
–

–
180.6
163.2
163.4
194.2
225
242
2016
–
36.8
39.1
35.5
28.9
29.0
28.2
2016
2.4
2.7

–
–
188.2
184.8
232.7
290
303
2017
–
–
41.1
38.3
30.0
29.7
30.0
2017
2.7
2.9

–
–
–
211.5
276.6
355
372
2018
–
–
–
40.5
31.0
30.3
31.1
2018
3.0
3.2

–
–
–
–
319.7
410
433
2019
–
–
–
–
32.4
31.5
32.6
2019
3.4
3.5

–
–
–
–
–
465
495
2020
–
–
–
–
–
33.0
34.6
2020
4.0
4.0

–
–
–
–
–
–
568
2021
–
–
–
–
–
–
36.2
2021
–
4.5

21.9%
20.5%
14.5%
14.3%
19.8%
21.6%
18.6%
5-year CAGR
9.1%
8.3%
4.8%
7.5%
4.0%
4.3%
5.1%
5-year CAGR
12.7%
10.6%

Note: Users’ application behavior in million workloads, storage demand per workload EB, and corresponding IP traffic per workload EB.

Monte Carlo simulation with 10,000 replications is given in Fig. 4. The
sensitivity analysis results into a 95% confidence interval of [267, 422]
TWh for the global data centers’ electricity consumption. Our pro­jections are similar to the estimations made by [3] while the forecasts
from [38] are more fluctuating. The discrepancy between the forecast
results is mainly explained by the assumptions made regarding thetechnological developments. Section 5.4 presents alternative scenarios.

Table 8 gives the differences in data center electricity demand for thebaseline model and the two scenarios, including the nonlinear trends ofdata centers’ share of the available electricity. We will elaborate onthese results in the following subsections. Note that the simulation
model is published online for other researchers to use the model forother scenarios as well (e.g., the introduction of 5G networks, the rise ofedge and/or fog computing, and the implementation of smaller public
service providers,).

5.4. Scenarios

5.4.1. Impact of Moore’s law
As stated earlier, Moore’s law is expected to stop having an influence
between 2021 and 2023 [2]. We will adjust the server productivity
CAGR values to slow down the annual energy decline per same pro­cessing unit. Therefore, the possible end of Moore’s law is modeled as adeclining trend from 2016 (25% efficiency increase) until 2023 (zero)
following [2]. The impact of ending Moore’s law is given in Fig. 5,
including a recalculating of data center energy needs. In 2016, servers

The main power of our model is to run scenarios and validate several“what-if” hypotheses. In this section, we discuss two alternative sce­narios for the baseline model:• Scenario 1: End of Moore’s law (Section 5.4.1);• Scenario 2: The rise of the Industrial IoT (Section 5.4.2).

7

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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.

base in order to process the increasing number of workloads. The lack ofprocessing efficiency gains results in a total server energy consumptionof 491 TWh in 2030, while only 219 TWh is required if Moore’s law
continues existing during the upcoming decade. Therefore, the server
category represents 74.66% (or 68.27%) from the global data centers’
electricity demand in case Moore’s law ends (or not). Doubling theserver’s energy consumption will inflate the data centers’ total energy
need up to 658 TWh in 2030 (see Fig. 5), which will also increment thedata centers’ share in the global energy consumption from 1.15% up to
1.86% in 2030.
The median electricity forecast in Fig. 5 relies on the assumption thatthe server’s productivity improvements will start declining in between
2016 and 2023, as indicated by [2]. Most scientists predict that Moore’s

Table 8Comparison of the data center electricity forecasts per scenario.
Electricity need

Baseline model

Scenario 1

Scenario 2

Electricity need 2016
Electricity need 2030
Relative differenceElectricity share 2016
Electricity share 2030
Relative difference

286.42 TWh
320.87 TWh+12.03%1.15%
0.91%− 20.72%286.42 TWh
658.03TWh+129.74%
1.15%
1.86%
+62.59%286.42 TWh
364.00 TWh+27.08%1.15%
1.03%–10.06%

are the largest energy consumers in data centers (approximately
31.40%), but the end of Moore’s law will increase the server’s electricity
demand further. Data centers will have to expend the installed server

Fig. 5. Data center electricity needs effects of ending Moore’s law.
8

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cause the global server electricity consumption to grow with 13.82% till
2030. More servers also require more internal traffic and larger sup­porting infrastructures, which results into an additional electricity
consumption of 1 TWh (+12.39%) and 9 TWh (+19.02%) respectively.
The increased use of IoT devices will also inflate the storage related
energy demand for 2030 (+13.03%) because business related applica­
tions require more database capacity in comparison with user applica­
tions. The baseline scenario predicts that storage devices will consumeless electricity due to energy efficient storage devices, but the rise of theIndustrial IoT will cause the storage related electricity consumption to
go up again, mainly due to the increased demand for storage capacity intotal. Therefore, we can conclude that the Industrial IoT will result in an
exponential growth of data center electricity consumption if businessapplications still rely on cloud or hyperscale-based architectures. These
growth projections will accumulate towards a global data center energy
consumption of 364 TWh in 2030 (see Fig. 6), while the data centers’
share in the global energy consumption will drop from 1.15% down to
1.03% in 2030.
The benefits from the Industrial IoT seem to be very promising [39],but it remains unknown when business practitioners will implement therequired platforms to support their daily operations at full scale. For
example, [43] revealed that number of IoT devices grew with a CAGR of
49% between 2012 and 2015, while [41] predicts a lower CAGR value(+19%) for the number of machine to machine connections. We will
embrace this uncertainty into our model by randomly generating a
growth factor for the CAGR value associated with the Industrial IoT. A
uniform distribution is used for the generation of a yearly growth per­centage, ranging from today’s CAGR value (+21%) up to a maximum
CAGR value found in literature (+49%). This procedure results into a
95% confidence interval of [322, 489] TWh for the global data centers’
electricity consumption, if the growth expectations for Industrial IoT
applications are randomly distributed. It appears that the IoT’s addi­
tional energy demand is quite modest in comparison with the 95%
growth projections of the baseline model (see Fig. 4, or the discussion in
Section 5.3). However, the inclusion of all uncertainty associated withthe users’ application behavior will significantly stretch up the gap in
between the lower- and upper bounds, resulting into a 95% confidence
interval of [290, 577] TWh. Therefore, the rise of the Industrial IoT
could significantly speed up the global data centers’ electricity needs for2030, especially if businesses require more server and storage capacity

law will end quite soon due to technical limitations, but it remains un­
certain when the end of Moore’s law will take place. Therefore, we
perform a Monte Carlo simulation with 10,000 replications where theending of Moore’s law is generated randomly. This means that the firstyear of the server’s productivity decline is uniformly distributed with U(2016, 2030), while the point of zero efficiency gains is uniformly
distributed with U(“Year start decline”, 2030). The server’s productivity
will drop linearly once the decline is initiated. This procedure results
into a 95% confidence interval of [326, 713] TWh for the global data
centers’ electricity consumption, if and only if the end of Moore’s law is
randomly distributed. The inclusion of the uncertainty associated withthe users’ application behavior will slightly alter both lower- and upperbounds (see Section 5.3), resulting into a 95% confidence interval of[307, 776] TWh. This means that the end of Moore’s law has a signifi­
cant worse impact on the global data centers’ electricity forecast incomparison with the alternative scenarios for the users’ application
behavior.
5.4.2. Rise of the Industrial IoT
In 2015, only 15 billion of the world’s 1.5 trillion physical objects
were connected to the internet [39]. During the upcoming decade, we
expect to see a sharp rise in the number of connected devices, due toIndustry 4.0 IoT developments [40]. The number of devices connectedto the internet, i.e., IoT, grew with 333% between 2012 and 2015 [39],which is equivalent to a CAGR value of 49.33%. This high growth per­centage is consistent with recent predictions of IoT growth. For example
IDC estimates data generated from connected IoT devices will grow from
18.3 ZB in 2019 to 73.1 ZB by 2025, which is about a 400% increase in 5
year and a CAGR of about 32% (https://www.idc.com/getdoc.jsp?containerId = prAP46737220). In the baseline model, we mentioned a
CAGR for workloads of databases, analytics, and IoT devices altogetherof + 21.4% (see Table 2 from [27]). We adjust the CAGR values fordatabase, analytics, and IoT application workloads to simulate the rise of
the Industrial IoT by slowly and linearly increasing this growth per­centage up to the 31.91% in 2030, which represents the growth factor of
the IoT’s installed base given by [39].
The workloads released by physical objects have to be processed bydata centers due to the cloud-based architectures installed, resulting into
a total of 249 TWh consumed by the installed server base in 2030. In
comparison with the baseline scenario, the Industrial IoT paradigm will

Fig. 6. Data center energy needs effects of the Industrial IoT.
9

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Applied Energy 291 (2021) 116798

for each IoT device installed.

practical planning problem with as yet little academic interest [29,44].
A second assumption was our limitation to server power predictions
with only average server power values. Although we follow Shehabi
et al. [29] in this by calculating the average sum product of all server
types’ wattages per data center type, server power usage may be highly
different over a day. Working with usage peaks thus will be more real­istic and is an important subject for further research. Morley et al. [12]argue for a focus on peak load estimations, which are stronger predictorof energy capacity needs than the annual volumes we discussed in here.
In contracting with electricity suppliers, data centers have to negotiate
electricity for their peak moments to remain fully operational and thus
in fact needs much more than the average. Economies of scale and vir­tualization are important developments that reduce the total electricity
demand of data centers.
A third assumption is our simplification on IoT and related to that 5 g
networks. IoT is expected to further increase the usage of the Internet inan yet unpredictable way [45,46]. 5 g could result in less data travel
distances and may reduce the load for data centers by edge computing,but at the same time will require data processing in more decentralized
units with less efficient electricity usage. We hypothesize that data
centers will remain their central position in IT infrastructures because of
their ability to exploit economies of scale (especially cooling, UPS andprocessing virtualization) [1].
A fourth assumption is our limitation to data centers. IDC [47] pre­dicts the size of the global datasphere – i.e., the total volume of digitalcontent stored on core, edge and endpoint computing locations anddevices – to grow from 33 Zettabyte (ZB) in 2018 to 175 ZB in 2025.
Compared to data center storage volume, which is about 1.180 ZB in
2018, 31 ZB in 2030 (CGAR of 31.31%), information storage andpossible related traffic seems to be just a small part of the total size ofdata stored, processed, and shared between all the possible electronic
device we have nowadays.

5.4.3. Combined effects of Moore’s law and IoT
The combined effects of an end to Moore’s law and an increase of IoT
applications for our model and using our expected behavioral usage
trends is given in Fig. 7. Both scenarios will significantly increase thenumber of servers required to process all workloads. In 2030, 74% of all
the global data centers’ electricity is only consumed by servers. The
infrastructure category is also responsible for a large part of the totalenergy consumption (17%), while the impact of all network and storage
categories is quite negligible (9% only). Our sensitivity analysis reveals
that the future impact of the data centers’ electricity consumption isvulnerable to both technological and behavioral developments, since the
95% confidence interval of [343, 1031] TWh for all three scenarios isrelatively wide. The end of Moore’s law is the main cause for the
exponential growth projections, while the uncertainty in the behavioral
usage trends explains the discrepancies found in the literature (e.g., thebenchmark values reported by [2,39]).
6. Discussion
Our study had to work with a number of assumptions and simplifi­
cations, which may be studied in further.
One assumption is our focus on global data as a relevant approach forany prediction. In reality, data centers tend to become more concen­
trated in very large cloud centers, named hyperscale centers, which will
do most of the data center services now and especially in the future.
Because hyperscale centers have very low PUE’s, this may be an
economically good development but also implies that energy con­sumption of all data centers is highly local, especially in highly indus­
trialized geographical areas. This may make the percentage of electricity
consumption in these areas very high and resulting in large energy de­livery problems. For example, in the Amsterdam region, data centers’
growing demand for electricity has become problematic ([42;43]
Accessed: 26.01.2020.) Any percentage of electricity capacity that data
centers claim make the share smaller for other increasing electrificationsof life, like traffic and heating. The difference in time horizons for the
rapidly developing data center industry and the less agile response ofelectricity distribution channels (requiring large and complex infra­
structure changes that may take about 8–10 years) is a challenging

7. Conclusions and further considerations
Our research question “How will future data center energy demand
evolve as a consequence of its growing usage?” now is answered by asimulation model and a set of simulation parameters found from theliterature. We expect a combined growth of data center electricity needsof 286 TWh in 2016 up to 321 TWh in 2030, if today’s technological andFig. 7. Effect of both Industrial IoT and Moore’s law on data center electricity consumption.
10M. Koot and F. Wijnhoven

Applied Energy 291 (2021) 116798

behavioral trends remain the same. The end of Moore’s law results in a
total of 658 TWh for 2030, and an increase of the global data centers’­share electricity consumption from 1.15% in 2016 to 1.86% in 2030.
The rise of Industrial IoT applications may consume a total of 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. However, looking into the future is difficult andmust be accompanied with uncertainty levels, and thus we performed
Monte Carlo simulations with 10,000 replications for all the baseline,
Moore’s law, IoT, and combined scenarios. The outcomes for the aver­ages, lower and upper bounds of these scenarios is given in Table 9.
Our sensitivity analysis in Table 9 reveals that the future impact ofthe data centers’ electricity consumption is vulnerable to behavioral
usage trends, since the 95% confidence interval of [343, 1031] TWh isrelatively wide. We conclude that the global data centers’ future energy
demands are kept reasonably constant due to technological innovations,even when both consumers and business workloads seem to growexponentially during the upcoming decade. However, the end of
Moore’s law will most likely cause an exponential growth of the data
centers’ electricity consumption, while the uncertainty in both techno­
logical and behavioral developments explains the discrepancies found intoday’s literature (e.g., the benchmark values reported by [2,39]).
We further discuss the academic contributions of this article and next
the challenges for the future from a data center supply and a data center
usage perspective.

Data center utilization in their model is only one part of the energy costs
of use, and as addition to ecological effects they extend their impact
analysis on greenhouse gas emissions (GHGE) instead of CO2 alone.
Their analysis predicts a growth of GHGE effects of communication
technologies from a bit more as about 1 to 1.6% in 2007 to about 14% of
the 2016 vol in 2040 in 33 years in 14 years for data center CO2 emis­
sions according to our prediction. These analyses may be hard tocompare because CO2 is only one part of all GHGE and Belhir andEmelgidi also include the manufacturing and usage of smaller devices,like smart phones, in their analysis. Although both analyses indicate
alarming GHGE effects, we kept this out of our analysis because thecalculation and simulation of GHGE implications of electricity produc­tion requires an intensive and complex additional study on a subject thatis highly influenced by new energy generation technologies.
Going more in depth on user behavior, we further describe the de­mand and supply sides of data center services in the following sections.
7.2. Data center supply perspective implications
Data centers are highly technological and innovative organizations.
Especially regarding their electricity consumption, they have high in­centives to innovate as about 70% of their costs contain the electricity
bill(https://info.siteselectiongroup.com/blog/power-in-the-data-ce
nter-and-its-costs-across-the-united-states, accessed April 2, 2020).
This study has kept the technological side of data centers as a given but
important technological innovations may make this become irrelevant.
The following innovations have been mentioned in the literature may
impact our forecasts of the coming decade:7.1. Contribution to the literature
The goal of this article is to discuss the impact of user demand
growths for data center electricity needs. The simulation of this modelshows exponential growths of data center usage. This results in about
approximately 2 or 3 times more energy needs in 14 years. This insight isless than Andrae [14] and Belkhir and Elmeligi [3] who predicted about
2,000 TWh for 2030 but more than Borderstep who predict a bit lessthan 1,000 TWh for 2030. Whatever the differences in numbers, wedeveloped a system dynamic model on basis of existing literature thatdefined the stock, flows and variables that determine the outcome of
TWh from data center usage. This model is also publicly available via
this publication [Note: to remain anonymous, we publish the link after
acceptance of this article; Insight Maker file though is available] and canbe used by any researcher or decision maker with alternative parametersand settings to replicate our findings. Instead of comparing estimates,
our work goes into what is behind estimates by a transparent simulation
model based on state-of-the-art literature.
Two alternative approaches to estimating electricity consumption ofdata centers and communication technology result in comparable find­ings but have a different dependent variable, i.e., not only electricity
consumption but greenhouse gas estimations (GHGE) and a broader
scope, i.e., not only data centers but also other components of theDatasphere. For example Belkhir and Emelgidi [3] have focused on the
production and utilization of electronic devices. They state that the
production of devices is also highly influenced by their life cycle lengthand thus is responsible for additional energy usage and CO2 emissions,but the main part is the actual number of devices and its intensity of use.

1. Photonic computing is especially seen as contributing to very low
energy costs of data transmission and data processing [2]. Cyberoptic transmission is already common at many places but photonic
data processing is still met with controversy and doubts regarding its
applicability [48]. The possible contribution of photonic computing
thus needs further research.
2. Immersion computing allows all processing units to work in a basinof oil, resulting in low levels of heat production and the reduction ofcooling costs of data centers by about 90% [49]. This technology is
still in an experimental stage but could be fully operational within
the next 5–10 years. The contribution to energy costs however islimited to reducing infrastructure cooling costs that may be less than
20% of the total energy costs of a modern data center [1].
3. Quantum computing may expand our ability to solve combinatori­
cally complex problems in polynomial time, but it will not be muchgood for word processing or graphics rendering [2]. The technology
itself will probably become available in the mid of this decade, but its
application opportunities are restricted [50].
4. Instead of thinking solely about having all data stored and processedin central cloud data centers, much data processing is better done on
decentralized edge units in combination with 5 g communication[40]. This will reduce the energy costs of central data center servers
and networking but will reduce the opportunities of exploiting
economies of scale that are easier to realize via large data centers.
Edge computing is also less useful for data transmissions that will
have to go via the Internet core, like large scale video services andsocial media, but will be especially useful for regional smart industry
and transportation development [51]. Edge computing also has anenergy costs and it is not sure if edge processing is more efficient thandata center processing.
5. Heat nets. Instead of focusing on data centers as energy consumers,
they also can be seen as energy suppliers to local households andindustry. The heat produced by data centers can be sent through acity heating network, and when at the end heat has been given tohouses, the cooler liquid is used for cooling the data center again.
This may reduce the cooling energy costs to nearly zero for a data

Table 9Sensitivity analysis for all data center electricity forecasts.
Data centerelectricity
consumption(TWh)

Scenario 0:
Baseline

Scenario 1:
Moore’slaw

Scenario
2: IIoTAll
scenarios
combined

Number of
replications
95% Lower boundMedian
95% Upper bound

10,00010,00010,00010,000266.75334.26421.59326.22445.03712.63322.86382.00489.17343.25565.871031.27

11

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center, although it does not reduce the energy costs of storage,
processing and transmission of data [52,53].

interests or personal relationships that could have appeared to influence
the work reported in this paper.

7.3. Data center demand side perspective

Acknowledgement

Besides of more energy efficient data centers also user behavior
changes may contribute to handling the energy scarcity problem. The
key idea that motivates our selection of mitigation options is the historicawareness that each innovation after a stage of unlimited growth andexplored opportunities comes in a stage of large scale adoption, diffusion
and maturity (if successful) that generates scarcity of resources[8,54,55]. Given this scarcity concept, we can think of strategies that
disallow certain types of behavior, that encourage certain types of
behavior, and that allow certain types of consumption if compensating
something worse.
Some highly energy intensive data center usages have an unclear
contribution and could be considered to be disallowed. One of them isthe block chain, which although without a large diffusion yet, is energyintense in its code generation and verification networks [56–58]
Another, but more radical, option is the disallowance of electronic
advertising. Experiments with adblockers show that about 40% of theenergy consumption of mobile phones can be avoided with applying
adblockers [59]. The impact of adblockers on the profitability of internet
content however is large and may be destructive of content businessmodels [60]. Consequently, content owners need to develop attractive
alternative business models that may combine public sponsorship withpayments. The feasibility of these new business models however is stillin need of research.
A growing and high energy consumption industry is the gaming in­dustry, which also can be defined as a variant of interactive video, theprobably most IP traffic consuming variant of data center usage [12].
Some limitations to the growth of this industry may be needed.
For realizing more energy saving behavior, positive encouragements
may be useful. Such encouragements can be realized by creating users’
energy awareness and given positive feedbacks for efficient users.
Creating awareness can be realized by public campaigns, but alterna­
tively we also suggest a metering system that can give users an energy
usage report of what they were doing, possibly including a recom­
mender or gamification system to avoid high avoidable usage [61].
Positive feedback can also be created as feed forwards like a pricing
system that informs the user before actual consumption about the en­ergy and GHGE report or giving information about a monetary price. For
the pricing “solution” a price elasticity must be known, or a behavioral
impact prediction must be known before such a strategy would be rec­ommended for practice. For GHGE reporting and feedback, more
research is needed on the future ecological footprint that data center
usage causes.
As a follow up on euro commissioner Vestager’s call for “technology
with a purpose” [62], we believe that technology is great if it helps tomake life better. So, if data centers cause energy and ecological prob­lems, there would not be a problem if following the next two principles:

We appreciate the discussions and support received from mrs Zahra
van Egdom who helped us on developing ideas for this article.
Appendix A. Supplementary material
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.apenergy.2021.116798.
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1. The service gives a strong and demonstrably great contribution tosocial goods, like the generally well accepted United Nations
Challenges.
2. The service’s energy consumption by far should reduce energy needsof other activities (e.g., large improvements in traffic mileages or
production energy use avoided), resulting in energy neutral or en­
ergy reduction applications.
Both these two principles require research for developing a usefuldecision method.
Declaration of Competing Interest
The authors declare that they have no known competing financial
12M. Koot and F. Wijnhoven

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