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Energy and Buildings 47 (2012) 74–83

Contents lists available at SciVerse ScienceDirect

Energy and Buildings

j our na l ho me p age: www.elsev ier .com/ locate /enbui ld

ovel instrumentation for monitoring after-hours electricity consumption oflectrical equipment, and some potential savings from a switch-off campaign

eil Brown ∗, Richard Bull, Farhan Faruk, Tobore Ekwevugbenstitute of Energy and Sustainable Development, Queens Building, De Montfort University, The Gateway, Leicester LE1 9BH, United Kingdom

r t i c l e i n f o

rticle history:eceived 30 August 2011eceived in revised form4 November 2011ccepted 17 November 2011

eywords:nergy

a b s t r a c t

An increasing cause of electricity use and greenhouse gas emissions is from IT equipment such as comput-ers, printers, and servers, with worldwide computer use increasing from 1000 million PCs in 2006, to 1400million in 2010, and estimated to cause 3% of global electricity demand. Significant energy may be saved ifun-used devices are switched off. It was noted that the switch-off rates in the USA for desktop computersin 2006, could be as low as 30%, and there clearly is a need for up-to-date information. It has been difficultto provide accurate figures for switch-off rates, since previous monitoring of IT use has been expensive,requiring specialised equipment and electrical work, labour intensive (using walk-through surveys), or

lectricalfficiencyaseload

TCTtandby

both. This paper demonstrates two low-cost techniques for estimation of unoccupied PC use. Precisionand tracking for both were compared with actual power consumption, and subcircuit switch-off ratesappeared to be under 76%. Whole building IT related use (including servers) was around 40% of elec-trical baseload. A desktop switch-off campaign was instigated for accessible equipment, resulting in a20% reduction in electrical baseload. Extrapolation to a weeknight campaign suggests annual electricitysavings of around 12%.

. Introduction

The DUALL project began at the start of 2010. An acronym forDeliberative User Approach to the Living Lab’, it aimed to uncover

hether involvement in the design of IT-based user applicationsffects behaviour change and quantitatively reduces the energyonsumption and carbon footprint of IT use. This paper describes amall part of the work undertaken therein:

Whole building monitoring to determine electrical usage pat-terns.Streamlined methods (compared to walk through surveys) ofdetermination switch-off rates for IT equipment, using networkactivity and case temperatures.A consequent estimation of electrical baseload from IT deviceswhich are left running.The effectiveness of cutting this baseload using a switch-off cam-paign, validated by analysis of half-hourly electrical consumption

data.A projection of annual savings from a switch-off campaign, pri-marily cutting unnecessary IT power consumption.

∗ Corresponding author. Tel.: +44 116 257 7851.E-mail address: nbrown@dmu.ac.uk (N. Brown).

378-7788/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.enbuild.2011.11.023

© 2011 Elsevier B.V. All rights reserved.

No-one is left in any doubt that one of the pressing ‘grand chal-lenges’ is how nation-states learn to live within environmentallimits. It is of interest though how certain activities attract greaterattention than others. A case in point is the increasing environ-mental impact of modern society’s dependence on IT equipment.This ranges from the domestic user’s personal equipment such asmobile devices, home computers and wireless networks, to theworkplace where in the majority of industrial sectors there existsa complete dependence on IT equipment, from the PC, the serverrooms, networks, not least the global and unseen infrastructurethat underpins the internet. It may come as a surprise to some thenthat while aviation constitutes 2% of global CO2 emissions, elec-tricity consumption due to the use of IT devices is estimated tocontribute to 3% of global electricity demand, i.e. 2% of greenhousegas emissions [1].

It is not simply the energy consumption of IT that is the prob-lem though. It is the waste associated with these activities. In theUK, generating capacity of approximately 63 GW exists to supply apopulation of 70 million, where 44% of UK energy is consumed aspart of activities within buildings [2]. It has been argued that energyconservation, and the identification of ‘low hanging fruit’ throughenergy conservation schemes such as switch-off campaigns should

be the first step in reduction of energy use for any organisation,with more costly interventions such as improved appliances andbuilding fabric modifications to follow later, typically illustrated asan energy pyramid [3]. Research during the 2000s has found many

N. Brown et al. / Energy and Buildings 47 (2012) 74–83 75

Table 1Disaggregation of office equipment power consumption [14].

Power consumption

Proportion of total Proportion of network attached Totals

Servers, etc. [1] Network devices 16.6% 24.4%Network printers 9.4% 13.8%

38.2%

Office IT [2] Office thin client 0.1% 0.1%Office standard desktop PC 18.0% 26.5%Office power desktop 3.8% 5.6%Monitors 10.9% 16.0%Mobile office laptop 8.7% 12.8%Mobile Field laptop 0.5% 0.7%

61.8%

Other office Photocopiers 3.9%Fax machines 0.4%Voice communications 27.2%

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All network attached (incl. monitors)

reas for rapid energy conservation; potential for quick savings inlectricity consumption has been identified by [4], where out ofours switch off rates can be as low as 30%. As well as employ-es not switching off computers at the end of the working day,nergy saving features are frequently not routinely employed, andre sometimes rendered ineffective by the choice of operating sys-em [5]. ‘Easy wins’ in energy conservation are not limited to IT,owever. In the UK, unoccupied office lighting can amount for upo 23–30% of total lighting use. National electricity waste from light-ng was estimated by extrapolating from these profiles to the UK,mounting to 1500–1900 GWh electricity, or 0.8–1.1 Mtonnes ofO2 per year [6]. In similar research, incidents of heating of unoccu-ied non-domestic buildings at night and at weekends, were foundo be as high as 30% of the stock examined [7]. This research alsohowed electrical baseloads within local authority buildings to bencreasing annually at a rate of 9%, of which IT use could be a causalactor, although clearly better data is needed to decouple (or oth-rwise) increasing energy use with productivity. Another examplef easily avoidable energy waste is illustrated in a survey of around00 retail premises, where very basic energy-saving measures foretention of heated air (closed doors, air curtains) were utilised innly 68.4% of buildings examined [8], and for cooling the figure was9% [9]. The emerging picture is that despite energy efficiency noteing a new subject, considerable scope still exists for reduction ofnergy consumption through very low cost interventions such ashecking heating controls, closing of doors, and switching off lightsnd appliances.

Homing in on the case of IT energy use, it is clear that betterata is needed to guide policy. This means detailed informationoncerning which appliances are used and when, and whereverossible disaggregated electrical feed data, for which a suitableaxonomy has been proposed [10]. A useful recent developmentas been widespread adoption of advanced metering, where build-

ng energy data is logged, typically at half-hourly intervals [11,12].hese data can be processed in order to identify particular eventsithin a building [13] and to look for phenomena such as electri-

al baseload, and out-of-hours usage [7]. Generally these data haveeen available for whole buildings, but sometimes electrical feedsre now disaggregated to smaller zones based on building servicesr activity. To determine use patterns of IT and electronic equip-ent fully, the logically ideal solution is to disaggregate down to

ppliance level, although this is rare in practice. Ref. [14] gives a

reakdown of office electricity use as shown in the following tableTable 1).

Adoption of sub metering strategies for buildings can provexpensive and disruptive, however (and rarely possible to

68.00%

appliance level). An alternative is to install non-invasive moni-toring systems such as current transformers (CTs), which may intheory, simply clip around a single phase cable, and when con-nected to a suitable datalogger, allow gathering of electrical usepatterns. However, since a single phase is needed for measurement,the technique can be classed as invasive, requiring access to wiringupstream of office sockets. In many organisations, merely attach-ing a CT to a cable in a distribution cabinet can mean overcomingoperational and bureaucratic hurdles which may prove too costlyand slow. Also, while producing fine grained data, appliances aregrouped at circuit level, not department (or desk) level. This canbe adequate for energy analysis purposes, but distribution panelsdo not always supply rational groups of employees IT equipment,and may also supply security systems, local HVAC, pumps, etc.,giving an unclear picture of how much electricity use is due toIT.

When seeking to feed back consumption data to employees,statistics for individuals or workgroups are preferable, especially ifenergy conservation is locked into a staff bonus system—so as sub-metering becomes cheaper, it could be expedient to build in highdensity circuit level monitoring. Short term financial pressures,however, mean organisations would not usually submeter beyondthe minimum demanded by legislation. One solution to evaluate ITswitch-off where funds exist for a limited amount of sub-metering,is to find the proportion of IT equipment which are switched offover a comparatively very large sample, and to check this againstareas where electrical feeds may be disaggregated down to officelevel.

A lateral approach is to circumvent direct electrical monitoringcompletely and to use computer networks. A ‘cheap and cheerful’method of duty cycle disaggregation for IT equipment is to ‘ping’computer network cards whereby one computer scans a networkfor signs of life. This will return a basic on/off response, and will notindicate modes of standby, such that an ‘off’ response as offered bya ping may not indicate that a PC is switched off at the wall andtherefore indicates a ‘best case scenario’, power may still be drawnin passive standby. However, pinging will not indicate the status ofmonitors or (non-network attached) peripherals. What this tech-nique does offer though is a very rapid and low cost picture of wherePCs (and similar equipment) are left fully running, and correlationagainst actual power consumption as measured by submeteringmay be sought. The technique also allows for desk-level disaggre-

gation, for feedback of consumption patterns to individuals, and nosoftware requires installation on PCs (although some power man-agement software may ultimately be a recommendation of such anaudit).

76 N. Brown et al. / Energy and Bu

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nected to a plug socket. In many items of electrical equipment,

ig. 1. Relative merits of differing techniques for PC consumption analysis, typicalotential sample sizes are shown to the right.

This technique ultimately offers spinoffs in many areas suchs building services or manufacturing, where much equipmentncreasingly attached to networks may prove to have significanttandby loads, e.g. CNC machine tools, to certain network attachedMS systems, even vending machines or VOIP telephony equip-ent. The diversity of such equipment may limit other approaches

uch as software solutions, but still facilitates an audit.A further low-cost measure for data gathering of appliance usage

atterns is to simply monitor case temperatures using low-cost<$60 [USD]) temperature sensors/loggers. This is particularly easyn the case of IT equipment since a small extract fan is normallyresent, blowing warm air onto any sensor positioned over it. Foruty cycle detection this can offer precision greater than 97% [15].he relative merits of differing methods of analysis are shown

elow in Fig. 1. Clearly no method in isolation is perfect, since aalance must be struck between financial outlay, lead time, andathering a representative sample of PCs.

Fig. 2. Dataflows for comparison between inference of IT consumption patter

ildings 47 (2012) 74–83

Direct power monitoring using a (shunt type) power meter (andseparate PC for logging) may be seen as a credible reference, withaccurate and precise results, although a small number of com-puters may be monitored in this way. Sub metering is capable ofmonitoring loads including a large number of PCs, but may takeconsiderable time to install, and will also pick up any load on themetered circuit introduced by office staff, such as commonplaceloads, e.g. desk lighting, although even high load appliances suchas fan heaters may be used. Ultimately, fusion between techniquessuch as case temperature monitoring and ethernet pinging shouldbe able to be used to provide a framework for extrapolation ofPC usage patterns and power, to estimate the total electrical loaddue to desktop IT in general cases for whole organisations, indeed,extrapolated to national building energy stock models. Dataflowsas such are illustrated below in Fig. 2.

1.1. Modes of standby for electrical equipment

Research in domestic building IT energy consumption by Ref.[16] has described that IT appliance use patterns are slightly morecomplex than those for electrical equipment such as lighting, orwhite goods, where typically a simple ON/OFF pattern of usageis to be expected. A simplified disaggregation of usage modes isdescribed thus:

1. On—the appliance is fully operational, and capable of drawingthe maximum power for the device.

2. Active standby—a PC running a screensaver for example, couldbe described as being in active standby, as could a DVD playerwhich may have all circuits powered up, with a DVD present, butnot actually playing the DVD.

3. Passive standby—this would be when most of the device is appar-ently powered down, but is not actually physically switched off.An example of this would be a personal computer, which hasbeen switched off on the front and at the wall, but is still con-

circuits will still be drawing power. Sometimes the amount ofpower drawn a passive standby is not considerably lower thanthat drawn in fully operational mode.

ns and actual power consumption measurement through submetering.

nd Buildings 47 (2012) 74–83 77

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. Off—explicitly that the device is either switched off at the wall,or is actually unplugged.

.2. Test building and living lab

The test building is the Queens Building at DeMontfort Uni-ersity, Leicester, in the English Midlands. Constructed in 1991 asn advanced naturally ventilated building of 10,000 m2, it mostlyouses an Engineering Department, the IESD, a number of laborato-ies, lecture theatres, and offices for research and administration.fter construction, the environmental performance of the build-

ng was good, although during the intervening years, electricalonsumption has increased, partly through increased usage of ITquipment, and partly also through changes in building use (forxample the construction of a number of multimedia studios,equiring air conditioning). As a result, while energy use for heatingnd cooling could be expected to be commensurate with build-ngs exceeding construction standards from the early 1990s, theest building is not atypical of UK non-domestic stock, in that thectivities within the building, as opposed to the building fabrictself, have contributed significantly to changes in electricity con-umption. Similarities can therefore be drawn, with local authorityuildings, commercial offices, and any organisation where office-ased electricity consumption is an issue of importance to energyanagers.

. Methodology

.1. Whole building monitoring

Whole building monitoring is carried out using a half hourlyetering system for gas, water and electricity. Data are relayed

ia a low-power radio network to a central receiver, and are then

ploaded to a MySQL database server. A very similar system haseen used in the past in order to examine energy consumption of

ocal authority buildings by the authors [7]. The general schematicf the low-power radio system is shown below in Fig. 3.

Fig. 4. Complications in disaggr

Fig. 3. Schematic of low power radio network.

2.2. Submetering

Building submetering was somewhat more difficult to arrangethan whole building monitoring, since electrical feeds in the testbuilding do not follow as rational pattern as was originally expected(Figs. 4–6). The test building has over 100 distribution cabinets,from which typically five or six electrical feeds will go to local cir-cuits, to power items such as lighting for a particular office, liftmotors, local circuits feeding sockets, water heaters, and so forth.As a result, in order to disaggregate electrical feeds for IT powerconsumption, it was necessary to introduce sub metering at circuitbreaker level. This would mean that these distribution boards may

have for example three or four circuits which would be of interestin the project, requiring routing through separate submeters.

egation of electrical feeds.

78 N. Brown et al. / Energy and Bu

Fig. 5. Submeters.

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cases as shown in Fig. 8, or the air outlet from a PC power supplycooling fan, as shown in Fig. 9, or above passive ventilation slots.Typically exhaust temperature is some 5 degrees above ambient.

Fig. 7. Profile of case temperature for electrical appliance.

Fig. 6. Telemetry equipment.

.3. Pinging

In order to provide an approximation of duty cycles for a largeumber of PCs, a short software routine was written to automat-

cally scan the local network, which runs the Ping command (aetwork testing command native to Unix, DOS/Windows, etc.). Thisrovides a very quick and low-cost method of arriving at an approx-

mation of IT switch off rates. A host computer runs the code, whichuns in two passes:

. A weekly scan is made of the local area network, in order to findIP addresses of active computers.

. Every 15 min, the host computer sends out a signal to all com-puters on the local subnet from this list, grouped by IP address.

ildings 47 (2012) 74–83

Since contacting each PC to view its network status would betime-consuming, resulting in a scan of the whole subnet takingaround 5 min, this would limit the amount of computers on thesubnet which would be scanned in total.

Two principal modifications have been made to the ping: firstly,only one attempt is made at contacting each computer, since allcomputers are deemed to be connected to the network can functionproperly when switched on. Secondly, timeout time is shortenedfrom 100 to 10 ms. As a result it is possible to scan over 100 com-puters in slightly more than 60 s. The results from pinging presenta ‘best case scenario’ only, a computer which does not return asignal of being fully operational is not necessarily off, since it willnot return a successful response to the network if it is in passivestandby. This means that the resultant estimates of switch off ratesare rather conservative, and the amount of computers which aredrawing power could be greater, so switch off rates are not over-stated.

2.4. Case temperature monitoring

Previous research [15] has shown that the use of low-costtemperature logging devices, is an easy way to gather electricalappliance usage patterns. The basic principle is fairly simple forthe vast majority of devices: if the device is warm, or is becom-ing warmer, then power is flowing. If the device is cooling down,or at a temperature close to ambient, then the device is not usingelectricity. When processing temperature data, some initial signalprocessing to remove noise is followed by rule based processing toestablish duty cycles which can produce a very precise estimationof appliance usage (Fig. 7).

Hobo temperature loggers are used, made by Onset Corporation,with logging set to 5-min intervals. These are precise to 0.1 ◦C at25 ◦C, and accurate according to specifications to least 0.47 ◦C. Theseare mounted above ventilation slots, such as main outlets from PC

Fig. 8. Main board monitoring.

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Fig. 9. PSU monitoring.

The technique is described more fully in Ref. [15]. In brief, refer-ing to Fig. 7, we use the measurement of the first differential ofemperature (1):

t = �Tp

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Results are fed through a low-pass filter (a moving average, oronvolution filter), and a simple rule based algorithm applied toetect events ABC and D. When compared to duty cycle measure-ent using a benchtop power meter (the reference device), case

emperature measurement was found to offer accuracy of duty

ycle estimation of 97.1%. A total of 21 PCs were monitored usinghis technique, forming a subset of the total number of networkttached devices.

Fig. 10. Case temperature monitor

ildings 47 (2012) 74–83 79

2.5. Monitoring campaign

Whole building monitoring was carried out using half-hourlymetering, and a subcircuit (a large office) was also submetered.Building-wide ethernet pinging took place, covering 90 devices,representing around 25% of total IT equipment. Ethernet pingingwas carried out within the office area to evaluate duty cycles ofup to 50 network attached devices, and a subgroup of 21 personalcomputers were monitored for case temperature.

2.6. Manual surveys and switch-off campaign, and half-hourlydata analysis

A switch-off campaign was started to attempt to reduce energyuse, and gauge the potential impact of IT switch off. During a switch-off weekend (16–17 October 2010), a small team of building staffcooperated and switched-off all accessible computers (500 com-puters) (and lighting) from 17:00 Friday (building use not expectedto pick up until Monday morning). Using half-hourly metering data,total consumption for the period 11/2009 to 11/2010 was cal-culated, for weeknight evenings from 17:00 until 07:00, and forweekend use. Weekend evening consumption was disaggregatedfrom daytime consumption for analysis, since parts of the buildingare in normal use at weekends.

3. Results

3.1. Switch-off rates

Fig. 10 shows the results from processing of case tempera-

test area. What is notable in this particular case is that switch offat weekends is encouraging for the test sample. However, theseresults were taken after an energy awareness campaign in an

ing to establish duty cycles.

80 N. Brown et al. / Energy and Buildings 47 (2012) 74–83

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Fig. 11. Office electrical consumption, ping

ffice where many researchers work [in the field of energy effi-iency]. Results are presented therefore, as an illustration of theffectiveness of case temperature monitoring to show duty cycles,nd are not necessarily indicative of energy consumption through-ut the whole building.

Fig. 11 offers a comparison between case temperature mon-toring to establish PC use duty cycles with the vertical axisepresenting the total number of PCs which were identified as ‘on’hrough case temperature monitoring. The ping response, i.e. theotal number of operational network attached devices within theest area, which responded is also shown. As can be seen from theiagram, the estimation of PC usage, tracks closely with the overallower consumption for the office test area.

A regression analysis (Fig. 12) was carried out in order to estab-ish any link between the number of responses from network

ttached IT devices to pings (i.e. the predicted electrical load), andverall building power consumption (i.e. the actual electrical load).s can be seen from the following diagram, there is reasonably goodlustering around the best fit straight-line, with an R2 value of 0.77.

Fig. 12. Regression analysis for whole build

se and case temperature based duty cycles.

3.2. Electrical baseloads

Fig. 13 shows the effect of the first switch off campaign carriedout within the test building. As can be seen, on Saturday, 17 October,a clear drop of around 20% of electrical baseload had occurred.

3.3. After-hours consumption analysis

Results from analysis of consumption for a whole year are shownin Table 2, with savings estimates estimated from switch-off cam-paign data (Fig. 13). The total baseload consumption for weekendsis not significantly lower, when factoring in daytime consumptionas well as night-time consumption. However, the total consump-tion for weeknights only, adds up to a significant proportion ofthe total annual consumption. The total weeknight – plus – week-

end consumption, factoring in daytime adds up to a slightly higheramount still. The eventual total electricity saving from a switchoff campaign, which would be affected only the night-time switchoffers for both all weekend and weekdays would be in the order of

ing electrical consumption vs. IT use.

N. Brown et al. / Energy and Buildings 47 (2012) 74–83 81

Fig. 13. Whole building energy consumption showing baseload drop from switch-off campaign.

Table 2Whole building out of hours consumption 11/2009 to 11/2010.

Start date: 11/03/2008 00:00 Stop date: 11/03/2009 00:00

Total consumption (kWh) Cost at 0.10/unit 20% saving

Monday 12 am to 7 am 41,784 4178.4 835.68Monday 5 pm to 12 pm 47,009 4700.9 940.18Tuesday 12 am to 7 am 40,930 4093 818.6Tuesday 5 pm to 12 pm 47,318 4731.8 946.36Wednesday 12 am to 7 am 39,809 3980.9 796.18Wednesday 5 pm to Thursday 7 am 47,540 4754 950.8Thursday 12 am to 7 am 51,288 5128.8 1025.76Thursday 5 pm to 12 pm 47,822 4782.2 956.44Friday 12 am to 7 am 42,885 4288.5 857.7Saturday 12 am to 7 am 37,666 3766.6 753.32Saturday 5 pm to 12 pm 37,622 3762.2 752.44Sunday 12 am to 7 am 38,235 3823.5 764.7Sunday 5 pm to 12 pm 38,592 3859.2 771.84Friday 5 pm to Monday 7 am 354,636 35463.6 7092.72

Total nights 519,908 51990.8 10398.16Total weekend-night only 264,390 26439 5287.8Total whole weekend 354,636 35463.6 7092.72Total weeknights + weekend 719,237 71923.7 14384.74

Annual total consumption 1,126,836

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Weekend and weekday night switch off only % electriWeeknight and whole weekend switch off % electri

.2%. However, a total weekend switch off campaign could poten-ially save around 12.7%.

. Discussion

.1. Usefulness of case temperature monitoring

Not all devices were able to be monitored for case temperatures,ince some were permanently on devices, temperature monitoringherefore being a subset of the total number of network connectedT devices. The correlation between ping response, and duty cyclesrom case temperatures is particularly noticeable in the weekend

eriod between 10th January and 24th of January, where someunday usage is visible (Fig. 11). Naturally, a small lag will existetween PC initially being switched on, and case temperature reg-

stered as usage. This can be seen quite clearly for the first Thursday

ving 9.2ving 12.7

and Friday when results were taken, whereby a small lag is visi-ble. Usefulness of case temperatures for detecting out of hours andweekend usage is clear, however, and future work could utilise thismethod to identify switch off rates of, for example, telecommuni-cations equipment, monitors, and other office hardware. While thesample size is arguably too small to draw statistically significantconclusions, it has been demonstrated that it is possible to gaugethe switch off rates of IT equipment.

4.2. Usefulness of ethernet polling—whole building

Ethernet polling has offered a very low cost and way to find

duty cycles for network connected IT hardware. A reasonable esti-mation of the baseload, due to IT use has been provided as wasdemonstrated in Fig. 10. This refers to devices fitted with a net-work card, such that a caveat applies with regard to monitors, and

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ther IT hardware, which cannot be reached with a ping. This intro-uces some uncertainty as to the actual power consumption, butoes still give a very clear indication of switch off rates. The plotuggests that the IT baseload is around 45 kW. Switching off allccessible IT equipment and lighting saves a total of 20 kW fromhe whole building baseload, indicating that at least 25 kW worthf remaining consumption is from network attached devices whichust usually be left running. This would include printers, servers,

nd clusters (e.g. for graphics rendering), which are used on a fairlyonstant basis.

Fig. 10 shows that from a total of around 85 devices which wereolled, always more than 20 would respond to a ping, meaninghat they were either on or in active standby. It must be remem-ered that this is the absolute minimum of appliances which wereesponding to a ping, implying a maximum switch off rate of 76%,ut usually considerably lower than this since appliances in passivetandby would not respond (but may be drawing, e.g. 5–15 W).

.3. Pinging and case temperature experiments within office zone

An extension of the pinging technique in order to improveunctionality would be relatively simple, whereby permanentlyonnected (and permanently on) hardware would be disaggregatedrom desktop PCs, etc., addressable by switch off campaigns. Thisould form the basis for future work, and a relatively simple item ofomputer software could be used by energy managers in order todentify potential opportunities for energy saving, indeed, to powerown infrastructure. It is interesting to note the difference in switchff rates between week nights and weekends, whereby an averagef around 16 devices which are left running on weeknights reduceso 11. Referring to Fig. 9 again, IT baseload appears to be 12 unitsesponding to a ping compared to a peak usage of 50, implyinggain, a switch off rate of 76%—by necessity sample size was heav-ly limited to a subcircuit with known hardware, so these results

ust be treated with caution.

.4. Effect of switch off campaign

The Queens Building switch off campaign was a simple attempto understand the dynamics of consumption from the macro-uilding perspective and the impact of IT equipment on theaseload. Based on the price of 13.2p per kWh a typical day inueens building costs around £420 ($670) (inclusive of base-load),

he baseload of 100 kWh costing £316 ($503). Reducing the week-nd baseload by 20% resulted in a financial saving of £134 ($213).

Through planning and implementing the switch off (whichntailed a complete walk-through of the building switching offvery unused computer and light) it was found that in manynstances the default position towards IT equipment was to leaveverything on. This is especially the case in the many computer-eaching labs that contain between 20 and 50 computers each. It

ust be remembered, however, that the building in question mayave usage at weekends, so a complete switch off or accessiblequipment may not always be possible. Ultimately, a switch offampaign could save in the order of 12% at maximum, and at least% at minimum, based on the analysis of results, as shown in Table 2.

. Conclusions

This paper has demonstrated the effectiveness of ethernetolling, and case temperature monitoring as alternative methodsf rapidly ascertaining IT switch off rates in the building. Switch off

ates for personal IT equipment from network activity appeared toe close to 76% (for a relatively small sample, and notwithstandingassive standby), with previous research suggesting that switch-ff rates would normally be much lower. The achievable savings

[

ildings 47 (2012) 74–83

on electricity use from a manual switch off campaign of personalIT equipment were at least 9% and up to 12% depending on imple-mentation.

Noticeable from these results was that a significant amount ofIT equipment, which is network connected is not easily switchedoff, with upwards of 40% of such equipment contributing toelectrical baseload. Therefore, it becomes clear that other powermanagement techniques would be needed in order to tacklethis wastage of out of hours electricity, where such devicesmay not be required all of the time to be operational, such asoperating system software interventions, or choice of hardware.This does not factor in potential savings from daytime powermanagement, including for use of energy saving settings on ITequipment, and switching off of unused equipment in unoccupiedoffices, which strongly suggests that potential savings are higherstill.

The opportunities for savings then are evident. The preva-lence of ICT (and the important IT subset) equipment continuesunchallenged so the need for improved management of the equip-ment must go hand-in hand with improved user-literacy and‘e-citizenship’. The question of who in the workplace is responsiblefor those savings must be saved for another day. For now, whileenvironmental regulation is catching up with other sectors such astransport, ICT remains an unseen cost. There is an opportunity forthe users of ICT—individuals, organisations and manufacturers toface up to the environmental cost of their activities now so we cancontinue to enjoy the benefits of increased ICT.

Acknowledgements

This paper has evolved from research undertaken as part of aJISC’s ‘Greening ICT Programme’. The authors gratefully acknowl-edge the commitment and encouragement of JISC, notably theprogramme manager Rob Bristow. Special thanks go to everyonewho gave up their personal (and work) time to talk about energyand green ICT and to all at the IESD, most importantly UmakantPancholi, Energy Manager of DeMontfort University.

References

[1] P. James, L. Hopkinson, Sustainable ICT in Further and Higher Education—AReport for the Joint Information Services Committee (JISC), University of Brad-ford, UK, 2009.

[2] DEFRA, DEFRA, e-Digest Statistics about: Climate Change, Department for Envi-ronment, Food and Rural Affairs, Nobel House, London, 2006.

[3] A. Gulkis, Energy pyramid: the best path to energy savings, Journal of Soil andWater Conservation 64 (2009) 143–144.

[4] C.A. Webber, J.A. Roberson, M.C. McWhinney, R.E. Brown, M.J. Pinckard, J.F.Busch, After-hours power status of office equipment in the USA, Energy (2006)2487–2502.

[5] V.G. Moshnyaga, How to really save computer energy? in: Proc. of the 2008International Conference on Computer Design, CDES 2008, 2008.

[6] N. Brown, Image Processing for Overnight Lighting Quantification in Buildings,IEECB, Frankfurt, 2010.

[7] N. Brown, A.J. Wright, A. Shukla, G. Stuart, Longitudinal analysis of energymetering data from non-domestic buildings Building Research and Information38 (1) (2010) 80–91.

[8] N. Brown, J. Caeiro, A.J. Wright, H. Bruhns, H. Altan, T. Oreszczyn, Large scaleenergy surveys in the UK retail sector, in: Proc. of the RICS Annual ConferenceCobra 2006, London, 2006.

[9] N. Brown, J. Caeiro, A.J. Wright, H. Bruhns, A. Summerfield, T. Oreszczyn, Aircon-ditioning surveys in the UK retail sector, or ‘keeping the cold in’, in: Proc. of theFourth International Conference Improving Energy Efficiency in CommercialBuildings (IEECB’06), Frankfurt, 2006.

10] L. Marjanovic, M.J. Coleman, H. Bruhns, A. Summerfield, A. Wright, Appliancestaxonomy across both domestic and non-domestic building sectors, ASHRAETransactions 114 (Part 1) (2008) 491–499.

11] N. Brown, A.J. Wright, New approaches to the gathering, analysis and interpre-

tation of half hourly energy metering data from buildings, International Journalof Low Carbon Technologies 2 (1) (2007) 80–96.

12] W. Younger, Using interval meter data for improved facility management,Energy Engineering: Journal of the Association of Energy Engineering 104 (5)(2007) 21–33.

nd Bu

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[

N. Brown et al. / Energy a

13] G. Stuart, P. Fleming, V. Ferreira, P.1. Harris, Rapid analysis of time series data toidentify changes in electricity consumption patterns in UK secondary schools,

Building and Environment 42 (4) (2007) 1568–1580.

14] G. Bacon, IBM White Paper—Energy and Low Carbon: More Haste, Less Waste,IBM, London, 2011.

15] N. Brown, A.J. Wright, Non-invasive and cost effective monitoring of energyconsumption patterns for electrical equipment, in: Proc. of the Fourth

[

ildings 47 (2012) 74–83 83

International Conference Improving Energy Efficiency in Commercial Buildings(IEECB’08), 2008.

16] M. Coleman, A. Wright, N. Brown, Residential consumer electronics appli-ance monitoring: a practical perspective and implications for inter-disciplinaryresearch, in: Proc. of the 5th International Conference on Energy Effi-ciency on Domestic Appliances and Lighting EEDAL 2009, Berlin, Germany,2009.