benchmarking the energy efficiency and greenhouse gases emissions of school buildings in central...

Benchmarking the energy e�ciency and greenhouse gasesemissions of school buildings in central Argentina

C. FilippõÂ n*

National Research Council (CONICET), C.C.302, 6300, Santa Rosa, La Pampa, Argentina

Received 20 October 1998; accepted 17 May 1999

Abstract

The energy e�ciency and emissions of greenhouse gases were estimated for 15 public school buildings in the city of Santa

Rosa, in a central area of Argentina. The annual energy consumption of electricity and natural gas by square meter ofconstruction and by student was measured in each case. The greenhouse gases emissions were estimated using a standardmethod applied in the UK. The consumption of electricity and gas over the estimated needs of auxiliary heating, and theeconomic cost of the consumed energy, were used as indicators of energy e�ciency in the study buildings. The need for auxiliary

heating varied from school to school, but was lower than the current levels of heating, revealing an ine�cient use of energy. Alow emission of greenhouse gases was estimated for the local buildings in comparison to school buildings located in otherenvironments of the northern hemisphere. However, such emissions seem to be unnecessarily high for the local climatic

conditions. It is concluded that some standardized designs and management practices, as well as the development of localstandards for energy demand and greenhouse gas emissions are necessary to improve the energy e�ciency of buildings in thestudy region and to contribute to the prevention of environmental change globally. # 2000 Elsevier Science Ltd. All rights

reserved.

1. Introduction

Concern about local and global environmental issues

is arising over the developed and the developing world.

Global warming, ozone depletion, destruction of natu-

ral habitats and loss of biodiversity are the cause of

hard debate in international forums. Global warming,

and its various potential e�ects on the earth, is a con-

sequence of a long-term accumulation of the so-called

greenhouse gases (CO2, CH4, NO2, etc.) in the higher

layers of the atmosphere [1]. The emission of these

gases is the result of intensive human activities such as

the burning of fossil fuels, deforestation, land-use

changes, etc. It was estimated that CO2 density in the

atmosphere could explain about 50% of the warming

e�ect because its concentration may have exceeded thenatural assimilation capacity of the earth [2].

The challenge of reducing the emission of green-house gases at local and global levels requires beha-vioral changes in life styles and energy consumptionpatterns in people, and the use of more energy e�cientproduction, processing and distribution technologies[3]. Lenssen and Roodman estimated that almost onehalf of the global CO2 emissions can be attributedeach year to the combustion of fossil fuels in theurban building systems. Setting aside the constructionprocess, operations in buildings were responsible ofone third of the total energy consumption in 1992;26% of this amount was due to fuels burning [4].

The improvement of building techniques is analternative way to increase energy e�ciency and reducegas emissions from human settlements [5]. China wassuccessful in reducing one third of its energy consump-tion during the period 1978±1990 simply by usingsome elementary design and construction technologies

Building and Environment 35 (2000) 407±414

0360-1323/00/$ - see front matter # 2000 Elsevier Science Ltd. All rights reserved.

PII: S0360-1323(99 )00035 -9

www.elsevier.com/locate/buildenv

* Tel.: +54-2954-434222; fax: +54-2954-434222.

E-mail address: [email protected] (C. FilippõÂ n).

[6]. The Energy E�ciency O�ce (EEO) in Great Brit-ain has elaborated plans and practical guidelines tooptimize the use of energy in school buildings. Accord-ing to its o�cial estimations [7,8], the school buildingsin Great Britain produce approximately 6 million tonsof CO2 annually, which represents about 1% of thetotal emissions in Great Britain [9].

Even in a climatically favourable country, such asAustralia, buildings are responsible for approximately25% of the total energy consumption. This ®gure isabout 40% in the US and 45% in the UK [10]. InArgentina, the consumption of fossil energy in schoolbuildings may become critical in young quick-growingcities in which the youth population increases at higherrates in comparison to other older and stabilizedurban centers. Santa Rosa, a new capital city in a cen-tral province of Argentina is a typical case. The annualenrolment rate in the city clearly exceeded the nationalaverage [11]. However, very little attention was paid toaspects like environmental comfort, thermal and light-ing e�ciency, and the cost of energy use in schoolbuildings at that time, although they would receivemore careful analysis today.

The general objective of this work was to evaluatethe energy use and gas-emission performance in asample of 15 school buildings located in the city ofSanta Rosa. Speci®c objectives were to:

1. Assess the annual energy consumption in thesampled schools.

2. Describe the energy consumption pro®le of build-ings in terms of energy sources (gas and electricity).

3. Estimate total and relative economic costs of theenergy consumed per m2 and per student.

4. Make an evaluation of the energy e�ciency and thepotential for greenhouse gases emission of the study

buildings in comparison to school buildings locatedin the northern hemisphere.

Our hypothesis was that school buildings in the studyregion have a poor performance in energy, economicand environmental terms.

2. Materials and methods

40% of the educational buildings in Santa Rosa wassampled involving 15 schools for the initial, primaryand secondary levels. Some geographic and climaticfeatures of Santa Rosa city are as follows:

. ÿ36.57 and 64.45 degrees latitude and longitude re-spectively.

. Altitude of 189 m above sea-level.

. Average, absolute maximum and absolute minimumtemperatures in the year are 15.58C, 42.08C andÿ12.08C respectively.

. The minimum average and maximum average are re-spectively 1.48C and 31.98C for the months of Julyand December.

. The average global radiation on a horizontal planeis 16 MJ day/m2.

. The estimations of the annual degree-days for heat-ing (on the basis of 168C) were 1136, and 128 forcooling on the basis of 238C.

In comparison to the standards that were establishedin Spain for a region having a similar geographic andclimatic pro®le [12], our buildings show higher coe�-cients of global thermal transmission (KG): a ®gure of1.4 W/m2/8C was an average value for our sample[13,14]. The thermal heating load (Q) was estimated asa function of the annual degree-days for 168C the

Table 1

A comparative characterization of the study schoolsa

Schools Area (m2) Volume (m3) KG (W/m20C) KGadm G (W/m30C) DD (8C)

CN 2624 11415 1.38 1.07 1.31 587

CSR 3608 15429 1.39 1.21 1.26 587

EN 3722 16388 1.41 1.25 1.06 433

EPET 4503 15944 1.79 1.18 1.48 433

FA 2135 7891 1.34 1.17 1.38 309

PLP 2937 11089 1.41 1.17 1.30 309

2 1417 5841 1.33 1.30 1.46 587

27 2136 8920 1.18 1.13 1.40 402

74 1228 5443 1.52 1.00 1.30 309

180 1136 3976 1.33 0.99 1.48 402

219 primary 1185 4239 1.13 0.91 1.50 402

219 kindergarten 192 670 1.33 0.94 1.90 309

221 3074 13387 1.54 1.09 1.53 402

240 1102 4506 1.33 1.04 1.43 402

254 1156 4592 1.30 1.03 1.40 309

a KG: thermal transmittance; KGadm: standards of thermal transmittance values for Spain; G: volumetric rate of heat loss; DD: degree-days

for 168C corrected by a factor which represents the annual period of use.

C. FilippõÂn / Building and Environment 35 (2000) 407±414408

building's volume (V), and the volumetric rate of heatloss (G) [15]. Annual degree-days was corrected by afactor which represents the annual period of real use[16]. In Table 1, a comparative characterization ofschools is presented in order to illustrate basic di�er-ences in factors that were utilized as an input to ouranalysis.

Using consumption records provided by two privatecompanies which supply natural gas and electricity toSanta Rosa city, both energy sources were analysed asseparate issues in order to estimate their relative con-tribution to the total energy consumption in the studybuildings. The analysis involved the period 1991±1996.The annual consumption was estimated in absolute(kWh/year) as well as in relative terms (kWh/m2 andkWh/student). A problem arose with estimations ofkWh/student because certain schools operate withmore than one educational program that a�ects hoursof physical occupation of the building on a daily basis.The analysis was done on the basis of o�cial enrol-ment data provided by the Ministry of Education ofLa Pampa Province [17].

The cost of the energy used in each building wasestimated simply by multiplying the amount of kWhof gas and electricity consumed per unit of time bytheir respective prices, which were US$ 0.015/kWh fornatural gas, and US$ 0.169/KWh for electricity.

According to EEO [8], the energy consumption inschool buildings is strongly determined by what it wasde®ned as Exposition Factor (EF). The EF is esti-mated on the basis of constant and variable factors,such as building features, climate conditions, patternsof energy use, cost of the energy, etc. In response tothis, buildings can be divided into three categories:sheltered, normal and exposed. In our case, most ofour analysed buildings fall into this last category.

On the other hand, CO2 emissions were estimatedfollowing the EEO's scheme [7], which has developed amethod based on the use of standardized conversionfactors. Such factors were 0.20 and 0.70 kg CO2/kWh,or 52 and 188 kg CO2/GJ of natural gas and electricityconsumed respectively. Accordingly, in response totheir CO2 emission rate, buildings are classi®ed as low,medium and high CO2 producers.

Mean values (X ), standard deviations (SD ) andcoe�cients of variation (CV ) were estimated as stat-istical measurements to quantify the average perform-ance of the buildings and their ranges of variability.

3. Results and discussion

In order to identify di�erences in the functionaldiversity of the study buildings, ®gures showing di�er-ent dates of building, physical capacity (number of stu-dents), daily hours of use, and relative consumption of

natural gas and electricity were presented. These valueswere used in turn to estimate Q (kWh), energy e�-ciency (kWh/m2 and kWh/student), and CO2 emissions(kg/m2) on an annual basis.

As Table 2 shows, large di�erences between theschool buildings exist related to speci®c characteristicsof the functioning and energy consumption patterns ofthe analysed sample. A predominant characteristic inthe energy consumption pro®le is that natural gasaccounts, on average, for nearly 90% of the energyused, while the remaining consumption is of electricity.Energy consumption behavior may be driven by theusage patterns of this cheaper source of energy. More-over, the variability in electricity consumption is sig-ni®cantly higher than the variability in gasconsumption, showing that the former shows a rathererratic pattern of use in the comparison of schools.

In Table 3, absolute ®gures of energy consumption(gas, electricity and total) and Q estimations are pre-sented for each school building, and the mean and thestatistical dispersion of data across the schools are alsopresented. Big di�erences in the consumption of kWhamong the analysed buildings are evident since the CVvalues oscillate between 50% and 90%. The most out-standing result is that the total energy consumptionclearly exceeds the demand auxiliary heating (Q) inalmost all cases. The study buildings are using an aver-age 80% more energy than would be necessary tomaintain inside conditions of thermal comfort. Theseresults con®rm the former hypothesis that e�ciency ofenergy use in school buildings is unnecessarily lowand, as a consequence, too much energy is wasted withno extra bene®t. Additional con®rmation arises whenthe degree of association between total consumptionand hours of use shows a low and non signi®cant (P> 0.05) correlation coe�cient of 0.19 [18]. The energyuse e�ciency seems to depend on factors other thanthe amount of students or the number of hours of use.In relation to the energy e�ciency, Table 4 provideshelpful information about how energy is used byspeci®c units; kWh per m2 of cover area, and per stu-dent attending classes in the study buildings. Again,schools di�er greatly in their energy use e�ciency.What are the causes of such di�erences? This questionis impossible to fully answer within the scope of infor-mation available. Probably, a realistic answer is thatthe excess of energy wasted depends more on subjec-tive human decisions (of directors, teachers and admin-istrators) than on well founded, technically-drivendecisions. It may depend on door opening patterns,leakage of air, orientation of doors and windows, pre-vailing wind direction, etc.

The analysis of cost in energy consumption providesan additional perspective to the problem (Table 5). Inspite of the fact that natural gas accounts for about90% of the total consumption of energy, in economic

C. FilippõÂn / Building and Environment 35 (2000) 407±414 409

terms it accounts less than 50% of the total annualcost. Electricity, which supplies only 10% of the totalenergy, accounts for more than 50% of the total costshowing that it is a very expensive source of energy forthe region. In absolute terms, the variability of costamong buildings is very high. However, the analysis

gains additional value if we refer to the cost per unitof reference (m2 and student) since this approach canprovide an idea about how cost-e�ective was the useof energy in each building (Table 6). An importantissue to note here is that variability of energy use issigni®cantly lower for each m2 than for each student.A possible explanation for this is that energy adminis-

Table 2

Speci®c characteristics of educational buildings and the relative contribution of natural gas (NG) and electricity (E) to the total energy consump-

tiona

Schools Date of building Occupancy (h/day) Number of pupils Relative contribution (%)

NG E

CN 1940 19 1100 95 5

CSR 1980 19 864 73 27

EN 1940 14 1391 90.5 9.5

EPET 1970 14 1167 84.5 15.5

FA 1990 10 370 91.8 8.2

PLP 1990 10 396 94.5 5.5

2 1960 16 625 93 7

27 1970 13 832 93.4 6.6

74 1950 10 682 93.6 6.4

180 1980 13 731 88.9 11.1

219 primary 1980 13 591 81.3 18.7

219 kindergarten 1980 10 65 91.1 8.9

221 1980 13 910 83 17

240 1980 13 400 87 13

254 1980 10 313 96.8 3.2

Average X-

89.2 10.8

Standard deviation (SD ) 6.2 6.2

Coe�cient of variability (CV ) 6.9 57

a Sources: Local electricity and gas companies.

Table 3

Annual energy consumption and thermal heating load estimations in

the study educational buildingsa

Schools NGC EC TEC Q

CN 235335 11453 246788 210782

CSR 200660 73918 274578 274027

EN 365091 38485 403576 180423

EPET 451337 82538 533875 243430

FA 181057 16191 197248 76104

PLP 535224 30920 566144 109415

2 129808 9738 139546 185740

27 248348 17562 265910 120439

74 100732 6913 197645 52494

180 103583 12881 116464 168128

219 primary 94821 21794 116615 61733

219 kindergarten 41191 4009 45200 9494

221 292296 60024 352320 197538

240 163219 24289 187508 62144

254 138111 4580 142691 47694

Average X-

218721 27686.3 246407.2 133395.7

Standard deviation (SD ) 135536 24400.4 151326.5 75673.7

Coe�cient of variability (CV ) 61.97 88.1 61.4 56.8

a NGC: natural gas consumption; EC: electricity consumption;

TEC: total energy consumption; Q: thermal heating load. All ®gures

are expressed in KWh. Source: Local electricity and gas companies.

Table 4

Annual energy consumption per square meter of construction and

per student

Schools Total energy consumption

KWh/m2 Kwh/pupil

CN 94.1 224

CSR 76.9 317.8

EN 108.4 290

EPET 118.5 457.5

FA 92.5 533

PLP 192.7 1430

2 111.2 223.2

27 124.5 320.2

74 87.6 158.1

180 102.5 159.1

219 primary 98.4 496.3

219 kindergarten 235.4 695.5

221 116.3 387.3

240 159.0 469.3

254 123.5 456.3

Average X-

122.7 441.2

Standard deviation (SD ) 41.1 301.7

Coe�cient of variability (CV ) 33.5 68.4


trators in schools make their decisions on heatingin terms of physical space and not in terms ofstudent attendance. This is understandable if we con-sider that each building must be heated independent ofthe number of students; however, this decision does

not justify an excess of energy wasted in the heatingprocess.

In relation to each student of the primary and

Table 5

Cost (US$) of the annual natural gas, electricity and total energy consumptiona

Schools Annual energy consumption ($) Annual consumption (%)

NGC EC total NG E

CN 6331 2204 8535 74.2 25.8

CSR 3010 12492 15502 19.4 80.6

EN 5476 6504 11980 45.7 54.3

EPET 6770 13949 20719 32.7 67.3

FA 2716 2736 5452 49.8 50.2

PLP 8028 5225 13253 60.6 39.4

2 1947 1646 3593 54.2 45.8

27 3725 2968 6693 55.6 44.4

74 1511 1168 2679 56.4 43.6

180 1554 2177 3731 41.6 58.4

219 primary 1422 3683 5105 27.8 72.2

219 kindergarten 618 677.5 1295.5 47.7 52.3

221 4384 10144 14528 30.2 69.8

240 2448 4105 6553 37.4 62.6

255 2072 774 2846 72.8 27.2

Average X-

3467.5 4696.8 8164.3 47 53

Standard deviation (SD ) 2171.2 4112.3 5538.5 15.4 15.4

Coe�cient of variability (CV ) 62.6 87.5 67.8 32.7 29

a GN: gas natural (Kwh); EE: energõÂ a eleÂ ctrica (Kwh). Prices of energy in 1996, natural gas: US$ 0.015/KWh and electricity: US$ 0.169/Kwh.

Source: Local electricity and gas companies.

Table 6

Cost (US$) of the annual energy consumption per m2 and per stu-

denta

Schools per m2 per student

NGC EC total NGC EC total

CN 2.4 0.8 3.2 5.7 2 7.7

CSR 0.8 3.5 4.3 3.5 14.5 18

EN 1.5 1.7 3.2 3.9 4.7 8.6

EPET 1.5 3.1 4.6 5.8 12 17.8

FA 1.2 1.3 2.5 7.3 7.4 14.7

PLP 2.7 1.8 4.5 20.3 13.2 33.5

2 1.5 1.3 2.8 3.1 2.6 5.7

27 1.7 1.4 3.1 4.5 3.6 8.1

74 1.2 0.95 2.1 2.2 1.7 3.9

180 1.4 1.9 3.3 2.1 3 5.1

219 primary 1.2 3.1 4.3 2.4 6.2 8.6

219 kindergarten 3.2 3.5 6.7 9.5 10.4 19.9

221 1.4 3.3 4.7 4.8 11.1 15.9

240 2.2 3.7 5.9 6.1 10.3 16.4

255 1.8 0.7 2.5 6.6 2.5 9.1

Average X-

1.7 2.1 3.85 5.85 7 12.9

Standard Deviation (SD ) 0.62 1.06 1.26 4.35 4.4 7.5

Coe�cient of variability (CV ) 36.5 49.7 32.7 74.3 62.2 58.3

a NGC: natural gas consumption (Kwh); EC: electricity consump-

tion (Kwh).

Table 7

CO2 emissions in the study educational buildings considering the

natural gas (NG) and the electricity (E) as main sources of energya

Schools CO2 emissions (Kg/m2 per year)

NG E Total

Secondary

CN 17.9 3.1 21.0

CSR 11.1 14.9 26.0

EN 19.6 7.2 26.8

EPET 20.0 12.8 32.8

FA 17.0 5.3 22.3

PLP 36.4 7.3 43.7

Primary

2 20.7 5.5 26.2

27 23.3 5.7 29.0

74 16.4 3.9 20.3

180 18.2 7.9 26.1

219 16 12.9 28.9

221 19.3 13.9 33.2

240 29.6 20.7 50.3

255 23.9 2.8 25.7

Kindergarten

219 42.9 14.6 57.5

Average X-

22.1 9.2 31.4

Standard deviation (SD ) 8.0 5.1 10.5

Coe�cient of variability (CV ) 36.3 55.9 33.4

a Conversion factors: natural gas=0.20 Kg/kWh and electri-

city=0.70 Kg/kWh, or natural gas=52 Kg/GJ and electricity=188

Kg/GJ. Source: EEO (1994).


initial levels, the average cost per secondary studentis almost 70% higher (US$ 16.7 versus US$ 10.3).Putting aside the salary cost, energy accounts foran average of 33.8% of the total cost in schools.

The estimation of CO2 emission per school is pre-sented in Table 7. Figures oscillate between 20 and 60kg CO2/m

2 showing, as was expected, a large variabil-ity among the analysed buildings. If these results arecompared with indices of environmental performanceassessment from EEO [8] collected in a colder environ-ment, it is clear that low-emission patterns predomi-nate in our sample. 87% of the analysed buildingswould be considered as low-CO2 producers, and theremainder as medium to high CO2 producers.

An important consideration is that emissions fromheating in buildings should be adjusted to each speci®cenvironment. Di�erent standards should be developedfor di�erent environments. Common sense suggeststhat the same standard of emission may be invalid ifwe compare buildings located in cold, temperate, orwarmer climates because the Q value varies greatlyamong di�erent conditions. A more valid approachwould be to assess the performance of a building in re-lation to energy-e�cient buildings, which minimize theuse of auxiliary energy. Results from FilippõÂ n et al.[19±23] demonstrated that about 80% of fossil-fuelenergy can be saved if some elemental principles ofenergy-saving design are applied to buildings in thelocal environment. Passive solar heating during thewinter, cross ventilation and night time cooling duringthe summer, and year round daylighting, orientation,reduced thermal transmittance of walls, roofs and car-pentry, reduced air in®ltration and heat losses are,among others, key technological improvements whichcan be added at relatively low cost in public buildings.

4. Conclusions

The energy performance in school buildings, as wellas in all kinds of public and private buildings, shouldbe assessed taking into account not only energy butalso economic and environmental considerations. Ab-solute values of energy consumption and energy costmay not be signi®cant if they are considered in iso-lation, setting aside other comparable examples. To behelpful, comparisons should be done in relative terms,for example; measuring the energy consumption,energy cost, or CO2 emission per m2, per student, orthe use of other useful reference units. Practical resultsfrom energy-e�cient buildings in our region demon-strate that a huge improvement in the performance ofbuildings can be reached by means of good physicaland climatic design and through a careful managementof standardized seasonal practices.

However, barriers to the implementation of such

improvements include a cultural inertia that cannotalways be successfully overcome. In spite of a de®cientdesign, much can be done to improve the energy e�-ciency from a management perspective by: (1) period-ical maintenance of boiling pans; (2) strategic locationof thermostats according to desired outcomes; (3)identi®cation of areas of large energy waste to elimin-ate sources of energy loss; (4) monitoring consumptionrecords to identify abnormal changes; (5) encouragingthe sta� and students to make an e�cient use ofenergy, and illustrating to them how a good energymanagement can provide both economic and environ-mental bene®ts; (6) incorporating the energy utilizationto the educational annual programs; and (7) trainingand distributing written procedural guidelines amongbuilding administrators.

Finally, the comparison of our results with thoseobtained under di�erent environmental conditionspractice may only have a relative value if the environ-ments di�er too much. Each location should developits own standards of energy, economic and environ-mental costs by taking into account the particular con-straints of its own environment. But it is interesting tocompare consumption of our own local schools withothers countries. The Energy Consumption Yardsticksof UK schools divide primary and secondary schools,without swimming pool, in low, medium or high con-sumption. The ®gures for primary schools are lowerthan 157, between 157±216, and higher than 216 kWh/m2, to low, medium and high consumption, respect-ively. The ®gures for secondary schools are lower than173, between 173±235 and higher than 235 kWh/m2, tolow, medium and high consumption, respectively.Another indices provides a single measure of buildingperformance and can be expressed, for example, interms of CO2 emissions. The UK Carbon DioxideYardsticks divide the schools in low, medium and highemissions. For primary schools, ®gures lower than 41,®gures between 41±57 and ®gures higher than 57 CO2/m2 de®ne low, medium and high emissions schools.For secondary schools, without swimming pool, ®gureslower than 46, ®gures between 46±63 and ®gureshigher than 63 CO2/m

2 de®ne low, medium and highemissions schools [7]. Comparing our schools with UKbenchmarks the 87% of secondary and primaryschools are in the range of low emissions. The emis-sions/m2 of the kindergarten analysed represent anexample of a building with high emissions. The 20%of the schools analysed are buildings with mediumconsumption.

In spite of the energy situation, which is not embar-rassing at the moment, it should be advantageous todesign as a ®rst step, an action plan to achieve costsaving by bringing down energy consumption. At themoment school buildings are being refurbished tosupply the needs arising from the new provincial law


of education. The situation provides excellent opportu-nities for improving the energy e�ciency of schoolbuildings and their comfort conditions. The planshould involve speci®c actions and projects and shouldbe consulted frequently and reviewed annually. Thesecond step is to motivate sta� and pupils to adoptgood housekeeping. In the school buildings analysed,one example stands out as a prime candidate for thisapproach Ð a school whose study plan is orientatedto technology and electricity education. The strategiesto saving energy, to improve comfort conditions andto reduce maintenance costs are shown in Table 8.

The improvement of thermal transmittance (KG),with 0.05 m of thickness of insulation, in walls androofs, and the replacement simple glazing by doubleglazing in windows, represents an energy saving ofaround 40%. The original ®gure of KG is 1.79 W/m28C and the new ®gure including the strategiesdescribed should be 1.04 W/m28C.

The use of performance benchmarks obtained fromthe monitoring of energy-e�cient designs in ourregion, is a ®rst step to developing local regional stan-dards.

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Table 8

Measure Aim

Windows and doors . Draughtproof round windows and doors

. Fit automatic door closing devices

. Fit weather-stripped double glazing during refurbishment Reduce air in®ltration and rate of heat loss

. Reduce excessive glazing area

Walls and roofs . Insulate attics

. Add internal insulation to walls Reduce rate of heat loss

. Insulate roofs

Boilers . Improve insulation to boilers and pipework Reduce heat loss and improve operating

e�ciency

. Replace old, ine�cient boilers Improve operating e�ciency

Controls . Install zone controls, especially for areas with extended hours of

use

. Install weather-compensating controls Reduce heating demand and/or avoid

overheating

. Install tamperproof thermostats

. Install optimum start control to reduce pre-heating times Reduce operating periods

. Fit time controls to eliminate out-of-hours heating

. Consider installing a simple Building Energy Management System

for larger premises

Improve comfort, reduce heating demand, avoid

overheating and reduce operating periods

Lighting . Install more e�cient lighting or improved switching arrangements

. Install automatic lighting controls (time, daylight, occupant

detection)

Reduce electricity demand

Shading . Re-design ®xed shading devices in the north glazed areas Improve radiation solar gains

. Design the surrounding landscape Improve the shading and cooling


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