Predetermined overall thermal transfer value coefficients ... transfer value... · Predetermined overall thermal transfer value coefﬁcients for Composite, Hot-Dry and Warm-Humid

PH

SD

a

ARRA

KOOAHW

1

1(

O‘htcbS[

cECBHlAO

0d

Energy and Buildings 42 (2010) 1841–1861

Contents lists available at ScienceDirect

Energy and Buildings

journa l homepage: www.e lsev ier .com/ locate /enbui ld

redetermined overall thermal transfer value coefficients for Composite,ot-Dry and Warm-Humid climates

eema Devgan, A.K. Jain, B. Bhattacharjee ∗

epartment of Civil Engineering, Indian Institute of Technology, Hauz khas, New Delhi 110016, India

r t i c l e i n f o

rticle history:eceived 10 February 2009eceived in revised form 20 May 2010ccepted 20 May 2010

eywords:TTVTTV coefficients

a b s t r a c t

This paper attempts to formulate Overall Thermal Transfer Value (OTTV) coefficients for Composite, Hot-Dry and Warm-Humid climates, the three main tropical climates in India. Four existing air-conditionedoffice buildings – two mid-rise and two high-rise were modeled as case studies using eQuest v.3.6, whichis a DoE2.2, based building energy simulation tool. Based on the study of building envelope, loads, oper-ation and HVAC system characteristics of these case study buildings, a hypothetical high-rise, 16 storeyoffice building, octagonal in plan was created for parametric studies. 98 types of opaque exterior wallconstructions and 93 types of glass constructions were varied sequentially in parametric runs to obtain

ir-conditioned office buildingsot-Dry climatearm-Humid climate & Composite climate

results for hourly wall conduction, glass conduction and glass radiation heat flow in eight orientationsfor each of the climate type. These hourly results were processed to obtain annual heat gain intensitiesfor each parametric case for all three modes of heat transfer. Regression analysis was used to obtain theOTTV coefficients – TDeq, DT and SF for the three climates. A new OTTV equation is obtained and presented.The set of coefficients obtained were verified by calculating the OTTV for the four case study buildings,

ns. Tal sp

for various parametric rucorrelation with the annu

. Introduction

.1. Global scenario on the use of overall thermal transfer valueOTTV) in building energy codes

In many countries with predominantly cooling based climates,TTV or overall thermal transfer value is used as part of the building

envelope’ energy code regulation. The basic assumption is that theigher the OTTV value, the greater will be the net heat gain throughhe building envelope and hence more energy will be required for
ooling. Some of the countries which have evolved OTTV baseduilding energy codes for their climate and building types includeingapore (first adopted in 1979) [1a,1b], Hong Kong [2], Thailand3], Sri Lanka [4] and Pakistan [5]. In these countries, high solar
Abbreviations: ASHRAE, American Society of Heating, Refrigerating and Air-onditioning Engineers; BEC(s), Building Energy Code(s); BEE, Bureau of Energyfficiency, Ministry of Power in Government of India; CDD, Cooling Degree Days;DH, Cooling Degree Hours; CR, Cooling Requirement; ECBC, Energy Conservationuilding Code; EPF, Envelope Performance Factor; ESM, External Shading Multiplier;DD, Heating Degree Days; HSA, Horizontal Shadow Angle; HVAC, Heating Venti-

ation and Air-Conditioning; ISHRAE, Indian Society of Heating, Refrigeration andir-Conditioning Engineers; IWEC, International Weather for Energy Calculations;TTV, Overall Thermal Transfer Value; VSA, Vertical Shadow Angle.∗ Corresponding author. Tel.: +91 11 2659 1193; fax: +91 11 2658 1117.

E-mail address: [email protected] (B. Bhattacharjee).

378-7788/$ – see front matter © 2010 Elsevier B.V. All rights reserved.oi:10.1016/j.enbuild.2010.05.021

he computed OTTV for the four case study buildings exhibits good linearace cooling plus heating energy use in three climates.

© 2010 Elsevier B.V. All rights reserved.

radiation during the summer months is the main source of solargains and also the reason for rising daytime air temperatures. Pre-dominantly, the winter season is either brief or mild and in somecases totally absent. The OTTV based building energy codes of thesecountries differ from each other based on their climatic and geo-graphical location and also in the manner in which the coefficientsTDeq, DT and SF are derived.

The exact methodology of obtaining the OTTV coefficients forbuilding codes of different countries is not widely reported in litera-ture. A detailed comparison of these OTTV codes is beyond the scopeof this paper. However, based on available literature [1a,1b,2,3,4,5],Table 1 gives a brief comparison of OTTV requirements and coeffi-cients for the five countries and those derived for India in this study.The OTTV based standards of Singapore, Hong Kong and Thailand(also Malaysia, Indonesia and Philippines – not listed in Table 1)have many similar features (such as OTTV limits, form of equationand parameters) as development of the younger standards has beenoften influenced by the preceding ones and all of them have usedthe ‘American technology’ or building energy simulation tools to setup the basic OTTV equations [6]. It is interesting to note that somevery recent BEC(s) e.g. of Jamaica (2003) [7] and Egypt (2003) [8]
have also adopted OTTV based approach for envelope performancecriteria. In these cases, more rigorous methods have been used forobtaining OTTV coefficients as the tools available for parametricstudies and simulation results have also become more advanced inthese years.
dx.doi.org/10.1016/j.enbuild.2010.05.021

http://www.sciencedirect.com/science/journal/03787788

http://www.elsevier.com/locate/enbuild

mailto:[email protected]

dx.doi.org/10.1016/j.enbuild.2010.05.021

1842 S. Devgan et al. / Energy and Build

Nomenclature

A net area of building envelope (m2)˛ solar altitude (angle above horizon)˛ wall solar absorptanceAe exposed area of the fenestration (m2)Af area of a fenestration (m2)Afi area of the glass in the ith orientationAr area of the opaque part of a roof (m2)As area of the skylight part of a roof (m2)Aw area of the opaque part of a wall (m2)Awi area of the opaque part of a wall in the ith orientation

(m2)CR cooling requirementDT temperature differenceG fraction of window area exposed to the sun = Ae/AfH number of air-conditioned hoursID direct radiationId diffused radiationIgci window conduction heat gain intensity (W/m2)Isoli window radiation heat gain intensity (W/m2)IT total radiationIwci wall conduction heat gain intensity (W/m2)LSU linear solar transmittance coefficientLGU Linear glass transmittance coefficient�1 projection angle of horizontal shading device with

respect to the horizontal plane (assume positive forpractical reason)

�2 projection angle of vertical fin with respect to wallorientation (positive is to the right of wall orienta-tion, negative if to the left of wall orientation).

Q net heat flow through the building envelope (W h)Qgci hourly glass conduction gain or loss from the ith

orientationQsoli hourly glass radiation gain from the ith orientationQwci hourly wall conduction gain or loss from the ith loca-

tion.SC shading coefficient of a fenestration or a skylightSF solar factor (W/m2)SHGC solar heat gain coefficientSGU second degree glass transmittance coefficientSSU second degree solar transmittance coefficientTDeq temperature difference equivalentTR tons of refrigerationUf thermal transmittance or U-value of window glassUw thermal transmittance or U-value of the opaque part

of a wallW total heat flowWWR window-to-wall area ratioz azimuth angle�T difference of temperature�1 vertical shadow angle

1

bocptcw

�2 horizontal shadow angle� wall solar azimuth

.1.1. Miscellaneous research and evolution of the OTTV approachFor over thirty years, the concept and definition of OTTV has

een evolving with the key objective to make OTTV an indicatorf the impact of the envelope on the cooling energy used in air-
onditioned buildings. In using OTTV as an indicator of the thermalerformance of building envelopes, it is most important to derivehe coefficients TDeq, DT and SF of the OTTV equation. These coeffi-ients address the interaction of the building envelope propertiesith climate conditions and building operation schedule [9]. The
ings 42 (2010) 1841–1861

form of the OTTV equation will vary if different choice of variablesand their ranges are taken in the analysis. The regression analysisused for determining coefficients makes the output of OTTV equa-tion specific to the selection of (a) climate type; (b) building typeand its operation and occupancy schedule (commercial or institu-tional or 8 or 24 h usage); (c) nature of simulation output (hourlyheat gains or cooling load or chiller load) and (d) analysis period(such as summer or whole year).

A recent study by Yik and Wan [6] has cited the history of theOTTV index and the assumptions made by various researchers forcomputing OTTV coefficients. Yik and Wan [6] summarise the keyassumptions made by various local researchers in their studies,including the basis upon which OTTV was quantified and the charac-teristics of the model buildings they used. Lam et al. [10,11] studiedthe use of two approaches to develop OTTV equations for HongKong. In the first, OTTV was evaluated based on heat gains and theresults were used to determine TDeq for opaque walls and roofs. Inthe second, the ‘OTTV’ actually represented the annual total cool-ing load due to the three heat gain components. The simulationprogram DoE-2 [12] was used to predict the heat gains and theresultant cooling loads. The cooling periods studied include a 10-hour day and a 24-hour day, each for a 5.5-day week over a 9-monthperiod (March to November). They recommended that OTTV calcu-lation should be based on heat gains while TDeq, DT and SF shouldbe evaluated based on fixed air-conditioning schedules for avoidingthe need for different sets of TDeq, DT and SF data [11].

Chow and Chan [13,14] also used DoE-2 to predict heat gains buttook a different approach to establish OTTV equations. They arguedthat due to the weather changes among the four seasons in HongKong, it would be inappropriate to base the calculation of OTTV onthe total heat gain throughout the year. Instead of from outdoorinto indoor, heat transmission through the envelope may reversein direction during certain air-conditioned hours in the year. Theyproposed to determine the total envelope heat gain by summingalgebraically the hourly heat gains from all envelope elements overjust those hours where the total envelope heat gain in the hourremained positive. OTTV was then determined by averaging thetotal envelope heat gain over such hours, and the OTTVs determinedfor a range of building models were used in a regression analysis toyield TDeq, SF, and DT as coefficients in the OTTV equations.

Chow and Yu [15] also developed OTTV equations based on theheat gain, chilled water load and annual energy consumption, andconsidered the method proposed by Chow and Chan [14] the mostappropriate. Their study was based on a rectangular shape atriumhall and covered a range of set point indoor temperatures, including25, 23 and 21 ◦C. They opined that the use of OTTV alone would notensure energy efficient and cost effective building designs; the airleakage, the selection of heating, ventilating and air-conditioning(HVAC) systems and equipment, building energy management andother energy saving options, such as daylighting and solar heating,should also be considered.

Hui [16] developed a general methodology for developing OTTVequation that incorporates building energy simulation and mul-tiple regression techniques, but suggested to evaluate the solarfactor separately by means of ASHRAE’s or other methods. Sev-eral modifications to the original definition of OTTV (which wasbased on peak heat gains) have been proposed, such as basing it onannual heat gains, annual cooling loads or annual air-conditioningenergy use, all with the objective to obtain a parameter that canreflect the impact of envelope characteristics on the energy use forair-conditioning.

Turiel et al. [17], Chow and Lee [18], Chow and Chan [14], Chanand Chow [9], Lam [19], Yu and Chow [20], Chirarattananon andTaveekun [21] have revised the OTTV equation so that good cor-relation with the annual energy consumption of the buildings canbe obtained. Some of these studies have resulted in the country

S. Devgan et al. / Energy and Buildings 42 (2010) 1841–1861 1843

Table 1Comparison of predetermined OTTV coefficients of different countries.

Climate Singapore Hong Kong Thailand Sri Lanka Pakistan India

Source [1a,1b] [2] [3] [4] [5] (This study)Solar absorptanceincluded in wallconductioncomponent

No Yes No Yes No Yes

Glass conductioncomponentincluded

Yes No Yes Yes Yes Yes

TDeq for walls (◦C) (12) Single valuefor all types ofwalls andorientations

(Range 1.7–7.5)Table of values forfive differentdensities andspecified for 16orientations

(13.46) Differentvalue dependingon building type

(19.3) Single valuefor all types ofwalls andorientations

Single value for allorientations givenas a function of theweight of the wall

(30.5–52.5) Tableof values for threeclimates andspecified for 16orientations and asmall negativecoefficient as a f(U × ˛)2 (−4.5 to−5.75)independent oforientation

DT for windows(◦C)

(3.4) N/A (4.47) (3.6) Difference ofinterior andexterior designconditionsspecified for 12cities

(10.5, 14.5 and15.5) Differentvalues for each ofthe three climatezones and a smallnegative coefficientas a f(Uf)2(−0.69–−1.04)independent oforientation

SF for windows(W/m2)

(211) Single value.Correction factor(CF) specified for

(Range 104–202)Table of values for16 orientations

(172.9) (186) (Range 104–561)Table of values foreight orientations

(92–263) Table ofvalues for 16orientations for

oHos(sriiusogtaclblsctnu

1

lasti

eight orientationsfor 11 pitch angleof walls

r climate specific OTTV formulation for different building types.owever, some limitations can be listed which are characteristicf these early OTTV formulations and mainly result because of theimulation tools and capability available at that time. (1) Very few11–40) number of parametric cases were considered in regres-ion for OTTV analysis [9,14,17–21]. (2) The computer simulatedesults of three components of envelope heat gain are consideredn OTTV analysis. However, the heat gain for the entire envelopes considered without accounting for the heat gain from individ-al orientations of the building. (3) There is uncertainty about theelection of the time period for summing the envelope gains –nly summer months or all hours of the year with positive heatains [9]. (4) OTTV only accounts for the energy performance ofhe envelope. Some studies have performed multiple regressionnalysis for cooling requirement in air-conditioned buildings andonsidered OTTV as a variable along with other variables such asighting, ventilation, occupants, etc. [21]. In order to obtain theest energy performance of each variable it is better to perform

inear regression analysis. For one or all of the above reasons, sometudies have concluded that OTTV alone is not an adequate indi-ator of cooling energy use in air-conditioned buildings and felthe need to know how OTTV is distributed among its three compo-ents in order to determine the impact of OTTV on cooling energyse [17].

.1.2. Evaluation of the OTTV approach by Yik and WanWhile evaluating the ‘appropriateness’ of using OTTV to regu-

ate envelope energy performance of air-conditioned buildings Yiknd Wan [6] opine that for buildings in Hong Kong or places withimilar climate, heat transmission through walls and windows canake place in opposite directions at different times, and this makest difficult to derive a consistent envelope energy performance

for each of the 5climate zones

each of the threeclimate zones

index. In parts of buildings where there are high internal loads orsolar gains, envelope conduction loss can help reduce cooling loadon the air-conditioning system, and thus lower air-conditioningenergy use. For intermittently air-conditioned buildings, envelopeheat loss may also help reduce the pull-down load during thestart-up period, particularly after a prolonged shut-down period(e.g. a weekend). Therefore, a well insulated envelope with lowOTTV may not necessarily mean reduced energy use. To circum-vent this problem, proposals have been made to focus only onseveral hotter months in the year or to ignore periods with net heatlosses.

This aspect of OTTV formulation differs when one considers thesevere tropical climate of India as compared to the sub-tropicalHong Kong. The selection of the ‘analysis period’, for summing thehourly ‘gains’, depends on the unique characteristic of the climatetype. In this paper, suitable ‘analysis period’ is selected after severaltrials so as to obtain the OTTV coefficients that result in the bestcorrelation between the computed OTTV with annual space coolingand heating energy use.

Yik and Wan [6] point that all the methods of OTTV computationhave more fundamental drawbacks due to the assumptions madeimplicitly, which include:

1. The heat gains from each wall, roof, window or skylight could bedetermined independently from pre-calculated values of equiv-alent temperature difference (TDeq) and solar factor (SF).

2. The same value of TDeq would apply to walls or roofs of thesame construction and facing the same direction, and likewisethe same value of SF would apply to windows or skylights atthe same orientation, irrespective of the room dimensions andconfigurations.

1 d Build

3

btahtb

bswbeaIcv

ttrbsibtrfsmwWi

1I

putmwftUeBswtwu

asibmtwa

844 S. Devgan et al. / Energy an

. The OTTV of the entire envelope could be determined from theOTTVs of different walls, windows, roofs and skylights in theenvelope, as their area-weighted average.

Yik and Wan [6] see the above assumptions as ‘problem’ecause, in their view just like building cooling load estimation,here is a need to take detailed account of the zone geometrynd characteristics of envelope components, partitions and internaleat sources in OTTV calculation. Hence, Yik and Wan find assump-ions 1–3 above invalid. It appears that their view point is severelyiased by ASHRAE’s abandonment of OTTV in the late 1980s.

The results of the heat gain intensities of the study of modeluilding by Yik and Wan [6] show that using the heat gain inten-ities for the base case to estimate the OTTVs for the other casesould lead to very significant errors, with difference between the

ase case and specific case OTTVs ranging from −41% to +16%. How-ver, the methodology used in obtaining the OTTV values used in thebove comparison is not clear. Thus, the error may be case specific.t is also apparent that OTTV may provide as an easy instrument forontrolling envelope heat gain or loss, provided the coefficients areerified systematically.

The conclusions of Yik and Wan [6] are both positive and nega-ive in favor of the OTTV approach. Yik and Wan are in agreementhat OTTV is simple to use and thus the cost of implementing theegulatory control can be kept low, which may be a valid reason forasing the control on OTTV. However, they still favor the detailedimulation approach, where minimum performance required ofndividual types of envelope components can be specified on theasis of more basic characteristics, e.g. the characteristics of a par-icular wall construction and glazing and a window-to-wall areaatio limit, instead of using the simplistic OTTV method, which theyeel is prone to errors. Yik and Wan opine that the countries that aretill using OTTV as a means for controlling building energy perfor-ance should give a second thought to whether or not to continueith its use as a regulatory instrument. Thus, the study by Yik andan [6] points out the need for further research regarding the OTTV

ndex.

.2. OTTV approach in the context of Building Energy Code forndia

The greatest advantage of the OTTV index is that it can measureerformance of a building as a single numerical index, without these of a simulation program. The predetermined coefficients makehe computation of OTTV index for any building in any of the cli-

ates very easy as any architect or building designer can use itith a simple computer spreadsheet program. OTTV is also a per-

ormance based index as it allows the building designer to makerade-offs between different envelope parameters such as Uw, ˛,f, WWR, SC, etc. OTTV based building energy code can be consid-red as an alternative to the recently launched Energy Conservationuilding Code (ECBC) [22], 2007, which mainly recommends pre-criptive criteria for building envelope components such as roofs,alls, and windows for control and regulation of energy consump-

ion in buildings. In case of such a building energy code, complianceith the performance approach can only be demonstrated with these of a simulation program.

The insulation based prescriptive criteria in ECBC can be lookedt critically. The study by Lam et al. [23] and Radhi [24] areome pointers to the limitations of the role of thermal insulationn conserving energy consumption for cooling in air-conditioned
uildings; in the context of warm tropical and sub-tropical cli-ates. A recent study by Masoso and Grobler [25] has challenged
he well-established knowledge that the lower the U-value of aall, the lower the annual energy consumption of the heating

nd cooling systems. The OTTV approach described in this study

ings 42 (2010) 1841–1861

differs significantly from the ECBC [22] which uses the ‘compo-nent approach’ in its building envelope prescriptive criteria. OTTVapproach is an envelope performance based criteria which corre-lates net heat flow through the building envelope per unit area andunit air-conditioning hours. ECBC also includes an envelope perfor-mance based criteria referred as the ‘Envelope Trade-Off Option’.Compliance with the Envelope Trade-Off Option in ECBC is demon-strated if the ‘Envelope Performance Factor’ (EPF) of the proposeddesign is less than the standard design, where the standard designexactly complies with the envelope prescriptive criteria in ECBC.EPF coefficients have been listed for five climate zones of India –Composite, Hot-Dry, Warm-Humid, Moderate and Cold and dif-ferent values of U-factor and Solar Heat Gain Coefficient (SHGC)have been suggested for mass walls, curtain walls, roofs, north win-dows, non-north windows and skylights. Since the methodologyfor obtaining these EPF coefficients could not be found, the authorswere compelled to not comment on the basis and validation of the‘Envelope Trade-Off Option’ in ECBC [22].

ECBC [22] provides an option of ‘Whole Building PerformanceMethod’ in case a building is unable to comply with the prescrip-tive criteria. However, in this case the compliance of the proposedbuilding design can only be demonstrated with the use of energysimulation program. Based on the simulation guidelines providedin ECBC, the energy consumption of the proposed building design issimulated and compared with the energy consumption of a ‘Stan-dard Design’ and compliance is shown if the energy consumption ofthe proposed building design is lower than the energy consumptionof the ‘standard design’ building. This approach is not comparablewith the OTTV approach for two reasons. Firstly, the ‘Whole Build-ing Performance Method’ involves the performance of the buildingenvelope, lighting, HVAC system, service hot water and miscel-laneous loads, thus allowing trade-off between the individualperformances of different components of the building. However,OTTV is the performance of the building envelope alone and hencesuited for maximum enhancement of the energy efficiency of build-ing envelope. Secondly, compliance with the OTTV approach can bedemonstrated with a use of a simple computer spreadsheet pro-gram, unlike the ‘Whole Building Performance Method’, where it isnecessary to use an energy simulation tool.

OTTV approach adopted by many of the neighboring countries ofIndia has not been attempted for Indian climates. Hence, this paperformulates OTTV coefficients for Composite, Hot-Dry and Warm-Humid climate, represented by the tropical climates in three Indiancities. Energy simulation is performed for a hypothetical octagonalplan high-rise office building, using eQuest v.3.6, which is a DOE2.2,based simulation engine. 98 types of opaque exterior wall construc-tions and 93 types of glass constructions are varied sequentiallyto obtain results of wall conduction, glass conduction and glassradiation heat flow in 8 orientations from simulation runs for eachclimate type. Regression analysis is used to obtain OTTV coefficientsfor the three climates and the same have been verified with thesimulation results of the case study of four existing buildings.

2. OTTV formulation

2.1. Definition and OTTV equation

OTTV is defined as a measure of heat transfer through the exter-nal envelope of a building and can be expressed as Q/A per unit time.Three components of heat gain are considered through the build-
ing envelope. These are (1) conduction through opaque walls, (2)conduction through window glass and (3) solar radiation throughwindow glass. As walls at different orientation receive differentamounts of solar radiation, the general procedure is to calculatefirst the OTTV of “ith” individual walls with the same orientation


aw

O

A

f

O

aatbot

O

2

godbjHntpt

afst

Fig. 1. Building’s solar geometry.

nd construction [Eq. (1)], and then the OTTV of the whole exteriorall is given by the weighted average of these values [Eq. (2)]:

TTVi = [(Aw × Uw × ˛ × TDeq) + (Af × Uf × DT) + (Af × SC × SF)]Ai

(1)

nd, OTTVwall =∑

(OTTVi × Ai)∑Ai

(2)

Alternatively, the above equation can be written in a compactorm using the terms of WWR [Eq. (3)]:

TTVi = [(1 − WWR) × Uw × ˛ × TDeq] + [WWR × Uf × DT]

+ [WWR × SCf × SF] (3)

Chou and Lee [18] defined OTTV as ‘the annual heat gain of their-conditioned spaces in a building from the envelope during bothir-conditioned and non-air-conditioned periods, averaged overhe total air-conditioned hours throughout the year and normalisedy the envelope area enclosing such spaces [Eq. (4)]. This was basedn the consideration that the heat gain would ultimately contributeo the cooling load on air-conditioning systems.

TTVi = Q

(H × A)= [Iwci × (1 − WWR)] + (Igci × WWR)

+ (Isoli × WWR) (4)

.2. Deriving external shading multiplier (ESM)

Shading of windows is very important for reducing solar heatain to the building. This shading can be provided by projectionsver the windows (horizontal overhangs) or at the side of the win-ows (vertical fins), or a combination of both. Shading of windowsy adjacent wall surfaces can also be considered as vertical fin pro-

ection. Eq. (3) is valid when the walls and windows are unshaded.owever, when the walls or windows are partially shaded by exter-al shading, it is assumed for the purpose of OTTV computation thathe exposed portion receives the total radiation, IT, and the shadedortion receives only the diffuse radiation, Id. To take account ofhe partial shading, external shading multiplier (ESM) is used.

ESM is defined as the ratio of the total solar gain received throughwindow shaded by an external shading device to that received

rom the same window if it was completely unshaded. The totalolar heat gain through shaded window is obtained as the sum ofhe total radiation received on exposed surface and the diffused

Fig. 2. Horizontal shading device showing VSA and projection angle.

radiation received on shaded surface, multiplied by their respec-tive areas. The total radiation, IT, through an unshaded 3 mm clearglass multiplied by total fenestration area represents the total gainthrough unshaded fenestration.

The relevant solar data was obtained from IWEC (InternationalWeather for Energy Calculations) weather data for the three cities[26]. The fraction of window area exposed to the sun (‘G’ fac-tor = Ae/Af) for each sunshine hour for each of the eight orientationsis determined. The step by step methodology used in this study toobtain the ESM tables is similar to the method described in Singa-pore code [1a]. It is described briefly as follows:

(1) The eQuest v.3.6 simulations using IWEC weather data for thethree cities (New Delhi, Ahmadabad and Chennai) give outputof hourly results for each hour of the year. For all three cli-mates, values for altitude (∞, angle above the horizon shownin Fig. 1) and azimuth angle (z, compass orientation of a verticalplane through the sun, measured clockwise from north shownin Fig. 1) were available for each sunshine hour (altitude anglegreater than zero). Thus, wall solar azimuth (� shown in Fig. 1)for each of the eight orientations was computed for each hour.

(2) The next step is the determination of fraction of window areaexposed to the sun given by ‘G’ factor. The (G) at every sunshinehour for a given orientation was determined using the value ofVSA or HSA computed for that hour.

(3) For continuous horizontal projection fixed at window headlevel (Fig. 2), G is derived:

G =(

Ae

Af

)= 1 −

[(P

A

)× (cos �1 tan �1 + sin �1)

](5)

(4) For continuous vertical fins in an array (Fig. 3), G is derived:

G =(

Ae

Af

)= 1 −

[(P

Af

)× (cos �2 tan �2 + sin �2)

](6)

(5) Once hourly values of G are known, (G × ID) + Id was computedfor all sunshine hours for all the summer months. Only the sum-mer months are considered in the shading period for the ESMcomputation because it is assumed that the shading during win-ters is small as the altitude of the sun is low and hence canbe ignored. The extreme and median parametric cases from
this study were selected. Summer months were selected asthose months of the year where the simulation results show theheating requirement if any, to be less than one percent of thecooling requirement. Thus, on the basis of simulation results ofthis study, summer months considered are March to October in

1846 S. Devgan et al. / Energy and Build

Fig. 3. Vertical shading fins showing HSA and projection angle.

Fig. 4. 3-D view of buildin


ings 42 (2010) 1841–1861

Composite climate, February to November in Hot-Dry climateand January to December in Warm-Humid climate.

(6) Eq. (7) was used to obtain the final value of ESM.

ESM =∑SH

h=1[(G × ID) + Id]h∑SHh=1[IT]h

(7)

The subscript ‘h’ refers to hourly values and ‘SH’ refers to thetotal sunshine hours of the summer months. For different typesof horizontal and vertical shading devices (�1 and �2 = 10◦, 20◦,30◦, 40◦ and 50◦ and P/Af = 0.25, 0.5, 1.0 and 1.25) the ESM valuesfor Composite, Hot-Dry and Warm-Humid climate have beencomputed in this study using the method described in this sec-
tion. Tables A1–A4 in Appendix A present the ESM values forComposite climate; (1) horizontal shading device, (2) both sidevertical fins, (3) right side vertical fin and (4) left side verticalfin. Similar tables for Hot-Dry and Warm-Humid climate areavailable in [27].
g ‘A’ in eQuest v.3.6.

g ‘B’ in eQuest v.3.6.


Table 2Summary of data for the four case study buildings and the model building.

Buildings A B C D Model

Tnhcmbhrs

Number of storey 6 6Total floor area (m2) 30,658 30,250Envelope area (m2) 10,587 9459WWR 0.28 0.45

Many of the OTTV based BECs of countries such as Hong Kong,hailand, Sri Lanka, Pakistan and Singapore have made use of exter-al shading multiplier (ESM) for window radiation component ofeat gain alone, ESM being the multiplier for horizontal or verti-al or the combination shading device. In this study, the simulation
odels of the case study buildings have shown that shading of walls
y adjacent surfaces also results in the lowering of the conductioneat gain component. This is because the sol-air temperature effectepresented in TDeq is reduced due to partial shading from directolar radiation for certain hours of the year. Hence the ESM multi-



15 15 1520,199 33,740 38,7158112 15,633 18,5730.47 0.41 0.4

plier (for vertical fins) should apply to the opaque wall conductioncomponent in the OTTV equation.

2.3. Building energy simulation and case study buildings

The program selected for use here is eQuest v.3.6, which is basedon DoE-2 [12] simulation engine. DoE-2 is the most widely adoptedsimulation program especially in the development of buildingenergy standards. DoE-2 has been validated by several studies suchas Sullivan and Winkelmann [28]. eQuest v3.6 offers a wide range

g ‘C’ in eQuest v.3.6.

g ‘D’ in eQuest v.3.6.

1 d Buildings 42 (2010) 1841–1861

oma

R(ioswbteosbacti

astrocsetclc

carrr[pu

sbsmostatamerapSI

2

tT

constant and new glass type was selected each time (from the pre-defined glass library of eQuest/DOE-2), thus varying the Uf and SC.The range of values of Uf was 0.99–6.31 (W/m2 ◦C) and range for SCwas 0.19–1.


f simulation features for a detailed whole building energy perfor-ance analysis. The program is also well suited for parametric runs

nd multiple variable selections in the hourly report blocks.Most air-conditioned office buildings in Delhi, National Capital

egion (NCR) and other metropolitan cities of India are mid-rise4–6 storey) or high-rise (above 10 storey). Large curtain wall glaz-ng and stone cladding on opaque masonry walls are common forffice and commercial buildings in many cities in India. Four repre-entative existing case study office buildings (Building A, B, C and D)ere selected from Delhi NCR region. Basic data collected for these

uildings included architectural drawings, occupancy and opera-ions schedule, HVAC system and plant details and electrical andquipment loads. Figs. 4–7 provide the three-dimensional viewsf the four buildings as modeled in eQuest v.3.6. Table 2 gives theummary of data for the four case study buildings and the modeluilding, Table 3 gives the input data for operation, internal loadsnd HVAC system, Table 4a gives the typical opaque constructionharacteristics of the case study buildings and Table 4b gives theypical glass construction characteristics of the case study build-ngs.

Like many other building energy simulation tools, eQuest v.3.6 islso based on the model representation of the actual building to beimulated. The “input” aims to represent the building as an abstrac-ion of the reality and this process determines the accuracy of theesults. On the other hand, the “output” consists of reporting resultsf the simulations, comparing them with real-energy-use data andhecking accuracy. The main parameter for the acceptance the casetudy building models in this study was the comparison of monthlynergy consumption of the real and the simulated buildings. Sincehe actual monthly energy consumption data for three out of fourase study buildings (-Buildings ‘B’, ‘C’ and ‘D’) was available for ateast one analysis year, it was possible to validate their model aslose to existing operations.

Pedrini et al. [29] describe a methodology for building modelalibration where, with different levels of information details andccuracy the modeled energy end-use results can be very to closeeal figures. Such model calibration is even more important inetrofit studies, as compared to the use in parametric studiesequired in this present study. The methodology of Pedrini et al.29] is divided into three stages, (1) simulation from building designlans and documentation; (2) walk-through and audit and (3) end-se energy measurements.

Some of these methods of Pedrini et al. [29] were used in thistudy so that the simulated energy end-use of the four case studyuildings could be adjusted to real figures allowing better analy-is in parametric studies. Using all the survey information, the firstodels of the case study buildings were created in eQuest. Some

f the inputs were assumed, due to lack of documentation. In thisituation, the eQuest library with appropriate defaults was foundo be extremely useful. Simulation results of the first model usu-lly deviated from the actual (metered) monthly consumption upo a range ±15–35%. The model refinement involved giving moreccurate details of the building envelope components and refine-ent of schedules such as occupancy, operations – fans, lighting,

tc. Fig. 8 compares the simulated monthly energy consumptionesults of the one of the four buildings (Building ‘D’) with thectual metered energy consumption data of the analysis year. Fig. 9resents the percentage deviation between actual (metered) andimulated Monthly Energy Consumption of Buildings ‘B’, ‘C’ and ‘D’.n all cases the difference is not larger than ±15% for any month.

.4. The model building

Based on the characteristics of the existing buildings a hypo-hetical model office building was created for simulation study.he model building is comparable to the existing buildings in its

Fig. 8. Comparison of actual and simulated monthly energy consumption of building‘D’.

window-to-wall area ratio (WWR) and HVAC system type. Thebaseline envelope characteristics of the model building are not ofany consequence as they are varied in parametric runs for OTTV for-mulation. The envelope and floor area of the model also do not affectthe results required from parametric studies. The important charac-teristic of the model building is its octagonal plan (see Fig. 10). Thiswas chosen to represent the eight orientations (N, S, E, W, NE, SE,SW and NW). All the surfaces of the model building were assumedto be completely unshaded throughout the year. The model build-ing can be considered to be the representative of majority of theair-conditioned office building in India.

Parametric runs were carried out for 98 types of opaque wallconstructions and 93 types of glass constructions to estimate theQ. First some new materials were added in the eQuest v3.6 library,by specifying their thermal conductivity, density, specific heat andthickness. Then 98 opaque wall constructions were defined using‘layers input’ giving the thickness of each material and its positionin the wall section from outside to inside. For the first 98 runs glassproperties were kept the same, opaque wall construction was var-ied in each consecutive run, thus varying Uw and ˛ each time. Therange of values of Uw was 0.23–3.9 (W/m2 ◦C) and range for ˛ was0.25–0.85. In the next 93 runs, opaque wall construction was kept

Fig. 9. Percentage deviation between actual and simulated monthly energy con-sumption of buildings ‘B’, ‘C’ and ‘D’.


Table 3Input data for operation, internal loads and HVAC system for the case study buildings and model building.

Common characteristics of the case study buildings and ‘model building’

Operating hours 08:00–20:00 h (Monday to Saturday) Sunday Off. Total 10 holidays in a year (other than Sundays).Lighting power density 20 W/m2

Equipment power density 2 W/m2

Occupant density 18 m2/person.Air side system Single zone air handler with hot water reheatThermostat set points 23 ◦C for cooling, 21 ◦C for heating

Buildings A B C D Model

Cooling load (tons ofrefrigeration) (TR)

900 800 600 1000 Auto-sized

Chillers 4 Nos. 300 TR eachScrew

4 Nos. 150 TR eachScrew and 2 Nos. 100TR each Screw

2SS

Fig. 10. 2-D plan view and 3–D view of Model building in eQuest v.3.6.

Table 4aTypical opaque construction characteristics of the case study buildings.

Component U-value (W/m2 ◦C) Description

External opaque wall 2.27 2.5 cm (granite/Dholpur stoneInternal opaque wall 3.3 2 cm gypsum board + 11 cm thRoof 1.47 10 cm brick tile + 10 cm mud pFloor 1.13 1 cm vitrified tile + 5 cm ceme

Table 4bTypical glass construction characteristics of the case study buildings.

Fenestration Different types of heat reflective or double glazin

building A B

Name of glass Antilio Silver Classic Green

Uf of glass (W/m2 ◦C) 2.8 5.7SC of glass 0.67 0.45VLT of glass 0.6 0.31

Nos. 225 TR eachcrew and 1 No. 150 TRcrew

2 Nos. 425 TR eachVAM and 1 No. 400 TRCentrifugal (laterchanged)

The target coefficients to be obtained from regression analysisare TDeq, DT and SF. In these parametric runs internal loads arekept unchanged so that the resulting heat gain intensities (in eachorientation and each component) varied with the external factorsonly.

The HVAC design for the model building was typically for bothcooling and heating and specified to be ‘auto-sized’ to allow for thesystem to resize itself with the changes in the building space cool-ing and heating load due to different envelope constructions. Thesame building model was simulated using weather data of threeIndian cities – New Delhi (lat. 28.58, long. −77.20), Ahmadabad (lat.23.07, long. −72.63) and Chennai (lat. 13, long. −80.18). The IWECweather data [26] for the three cities was used in eQuest v.3.6 com-patible format. The solar radiation data in the IWEC weather filesfor these cities was found to have good match with both the mea-sured data of Mani [30] and calculated data of Bansal and Minke[31]. Tables B1–B3 in Appendix B present the comparison of solarradiation data of IWEC [26], ISHRAE [26], Mani [30] and Bansal andMinke [31].

3. Regression analysis and results

3.1. Creating hourly reports in eQuestv.3.6

Among the several types of results reported by eQuest/DOE-2, the relevant results of the parametric runs are contained inthe Hourly Simulation results. In this study, 16 hourly simulation

) cladding + 2.5 cm cement mortar + 23 cm thick brick wall + 2 cm cement plasterick brick wall + 2 cm cement plasterhuska + 15 cm reinforced concrete slab + 2.5 cm cement plaster

nt concrete + 10 cm reinforced concrete slab + 2.5 cm cement plaster

gs with air gap

C D

Green Reflectasol (6 mmreflective greenoutside + 12 mmgap + 5 mm clear glassinside)

Classic Blue and SilverBlue

2.83 5.70.26 0.450.16 0.23

1 d Build

rtfectsaroTtsv

ipoed(

ddre‘

wvccrwrrv

aoi(awtdtwoTtitp

3g

sop

t


eports were created for each of the four case study buildings andhe hypothetical model building. These 16 hourly reports – oneor each of the eight wall orientations and one for each of theight window orientations, were created by a simple two step pro-ess. In the first step, an ‘Hourly Report Block’ was created wherehe hourly variables to be obtained as output in the report wereelected from pick up lists. In step two, an ‘Hourly Report’ was cre-ted which referred to an ‘Hourly Report Block’ and an establishedeport schedule (for which calendar dates is the Hourly Report to beutput) and the reporting frequency (i.e., hourly, daily, monthly).he output of these 16 hourly reports was generated for each ofhe 97 + 93 parametric cases for all five building projects (four casetudy buildings and the hypothetical model building) in eQuest.3.6.

The criteria for selecting any window (or wall) have been that its adjacent to an air-conditioned space and in such a solar geometricosition in the building that it remains largely unshaded through-ut the year. Two hourly results from the series were selected forach window orientation – ‘Conduction heat gain through win-ow (Btu/hr)’1 and ‘Heat gain by solar radiation (after shading)Btu/hr)’.

In order to check the shading of the wall or the window or forifferent hours of the year, ‘Fraction of window area shaded fromirect solar radiation’ or ‘Fraction of wall shaded from direct solaradiation’ may be selected from the hourly results series. The rel-vant result selected from the series for each wall orientation was

Unweighted heat transfer from wall to space (Btu/hr)’.On the completion of all the parametric runs (97 types of opaque

all constructions and 93 types of glass constructions) in eQuest. 3.6, all the results were ‘exported’ as 190 hourly results.csv filesontaining hourly results of 16 hourly reports each. Each reportontained wall conduction (Qwci), glass conduction (Qgci) and glassadiation (Qsoli) results for eight orientations of the walls andindows for all the 8760 h of the year. Total number of hourly

esults.csv files generated in this study, at the end of parametricuns for five building projects for three tropical climates in eQuest.3.6 was 190 × 5 × 3 = 2850.

Of the three types of hourly heat flows, Qsoli or window radi-tion heat gain for each hour in the ith orientation is dependentn the sun’s position in the sky at a particular hour, location (lat-tude of a place), the window azimuth and the shading coefficientSCf) of the window glass. Needless to say, but Qsoli is positive forll the sunshine hours and zero after sunset and before sunrise. Theindow conduction heat gain/loss for each hour in the ith orien-

ation (Qgci) is independent of the window orientation and largelyepends on the temperature difference between outside and insidehe building and the U-value (Uf) or insulating properties of theindow glass. The most peculiar is the heat gain/loss pattern of

paque wall conduction or Qwci for each hour in the ith orientation.his wall conduction component is very sensitive to various fac-ors such as thermal mass storage effect of the wall construction,ts U-value (Uw) and solar absorptance (˛), orientation of the wall orhe sol–air temperature impact and lastly the diurnal temperaturerofile in different months of the year.

.2. Selection of analysis period for summation of hourlyains/losses

It is common understanding that during summers when the out-ide temperatures are high for all hours of the day and most hoursf the night, net heat flow through the building envelope would beositive and during winters, when outside temperatures are lower

1 eQuest v.3.6 uses all FPS units. Conversions were made to SI units for processinghe obtained results.

ings 42 (2010) 1841–1861

than the air-conditioned indoors, net heat flow through the build-ing envelope would be negative. Wall conduction heat gain/loss orQwci for each hour in the ith orientation is dependent on the high-est number of factors and peculiar pattern of positive (heat gain)and negative (heat loss) ‘direction’ of heat flow can be observed atdifferent hours and at different times of the year.

Of the three climates considered in this study, the Compos-ite climate (annual heating degree days (HDD)base 18 ◦C = 321 andannual cooling degree hours (CDH)base 23 ◦C = 37,420) [26] is repre-sentative of the largest area in the Indian subcontinent. Thoughpredominantly cooling based Composite climate is unique becauseof its short and extreme winter between the months of Decemberand January. Hot-Dry climate (annual HDDbase 18 ◦C = 13 and annualCDHbase 23 ◦C = 46,656) [26] also has a winter between Decemberand January but it is mild in comparison to the Composite cli-mate. Warm-Humid climate (annual HDDbase 18 ◦C = 0 and annualCDHbase 23 ◦C = 46,651) [26] has only summer season all 12 monthsof the year.

From the various hourly results.csv files obtained in this study,some more observations can be made. In Composite climate, duringsummers (May, June, July and August) Qwci remains positive forall 24 h in all orientations. In certain months of the year diurnalvariations in temperature are very high and the ‘direction’ of wallconduction heat flow reverses to ‘negative’ indicating heat lossessometime in the late evening or night time and again reverses to‘positive’ indicating heat gains sometime early in the day beforenoon. The hour(s) at which the ‘direction’ of heat flow reverses,the magnitude of the conduction heat transfer and the number ofhours for which the heat flow remains ‘negative’ is dependent onfactors such as wall construction, outside temperature profile andwall orientation.

Summation of hourly results of Qwci during winter period(December and January) in Composite and Hot-Dry climate haveshown that, there is net heat ‘gain’ from certain orientations of wallsand at the same time there is net heat ‘loss’ from other wall orien-tations within the same building. Since the direction of heat flowmay be opposite in two different spaces of the building (depend-ing on the orientation of the walls adjoining that space and thesize of windows) sometimes, there is simultaneous or intermittentheating and cooling requirement in different spaces of the build-ings. Even during the hours of net heat loss, sometimes there is acooling requirement due to internal gains in certain spaces of thebuildings.

Net window conduction is always ‘negative’ (meaning heat flowfrom inside of the building to outside) in winter period in bothComposite and Hot-Dry climate, although there may be few hoursof positive gain (meaning heat flow from outside of the building toinside) during the afternoon due to winter sun.

The building envelope is required to minimise winter losses toreduce space heating energy use and regulate the solar heat gainsto reduce space cooling energy use. Hence the approach for esti-mating annual heat gains in Composite and Hot-Dry climate hasbeen to sum the net summer gains from February to November andreduce the net winter loss for December and January for each of theeight building orientations individually. The criteria for selectingthe months of December and January alone as the winter monthsbecause the numbers of HDDbase 18 ◦C were significantly higher inthese months as compared to the rest of the year.

Many researchers have considered different types of ‘analysisperiod’ for the OTTV formulation in their respective countries. Inthis study also, several types of ‘analysis period’ were considered for
the three tropical climates (such as only summer months/sum of allhours in the year/sum of all hours with only positive gains/absolutesum of all hours) and based on a trial and error methodology, the‘analysis period’ that best correlated the OTTV coefficients withannual space cooling and heating energy was selected. While doing

Buildings 42 (2010) 1841–1861 1851

vfpiaHtca

mtstdM

3

acitsc

I

wea

I

we

I

w

tfitc

ahIaSp

I

I

a

I

fice only in cases where there is no reversal of direction of heat flowduring the year.

To improve the correlation between predicted values of Iwci forthe given values of Uw × ˛ and explain the net annual wall con-

S. Devgan et al. / Energy and

erification studies for predetermined OTTV coefficients it wasound that if only summer months are considered in the ‘analysiseriod’ the OTTV obtained from the coefficients would have pos-

tive correlation with the space cooling energy of those monthslone and not the annual space cooling and heating energy use.owever, if ‘net summer gain minus winter loss’ is considered as

he analysis period, then the OTTV obtained from predeterminedoefficients had positive correlation with the annual space heatingnd cooling energy use.

Thus, for Composite and Hot-Dry climate ‘net summer gaininus winter losses’ was considered as the analysis period and for

he Warm humid climate, the ‘sum of all hours in the year’ was con-idered as the ‘analysis period’. Once the analysis period is decided,he summation for net summer gains minus winter losses has beenone in this study using a simple Visual Basic program (Macro) inicrosoft Excel.

.3. Computing hourly heat gain intensities for each orientation

From the values of(∑

analysis periodQwci

),(∑

analysis periodQgci

)nd

(∑analysis periodQsoli

)for all eight orientations of the hypotheti-

al ‘model building’, the wall conduction heat gain intensity for theth orientation (Iwci), the window conduction heat gain intensity forhe ith orientation (Igci) and the window radiation heat gain inten-ity for the ith orientation(Isoli) were computed for each parametricase using Eqs. (8)–(10).

wci =∑

analysis periodQwci

H × Awi(8)

here Qwei = hourly wall conduction gain or loss from the ith ori-ntation; Awi = area of the wall in the ith orientation; H = number ofir-conditioned hours.

gci =∑

analysis periodQgci

H × Afi(9)

here Qgci = hourly glass conduction gain or loss from the ith ori-ntation; Afi = area of the glass in the ith orientation.

soli =∑

analysis periodQsoli

H × Afi(10)

here Qgci = hourly glass solar gain from the ith orientation.For computing the total number of air-conditioned hours (H),

he 12-h operation schedule of the office buildings is consideredrom Monday to Saturday and the hours of Sundays and other hol-days are reduced from the total hours of the analysis period. Thus,he total number of air-conditioned hours (H) is 3636 in all threelimates.

Iwci, Igci and Isoli are dependent on the characteristics of the wallnd window constructions as seen from the OTTV equation andence a large data set of 97 values of Iwci and 93 values of Igci and

soli were used in regression analysis (using Eqs. (11)–(13)) in anttempt to obtain the constant or ‘predicted’ values of TDeq, DT andF when the values of Uw × ˛, Uf and SC are varied each time in thearametric runs.

wci = Uw × ˛ × TDeq (11)

gci = Uf × DT (12)

nd

soli = SCf × SF (13)

Fig. 11. Correlation of Iwci (N, S, E, W walls) and Uw × ˛- Composite climate.

4. Results from regression analysis

4.1. Relationships obtained from regression curves

The variation of Iwci with Uw × ˛ obtained from a large numberof values of Uw × ˛ and corresponding Iwci for all eight wall orienta-tions in Composite climate is represented in Figs. 11 and 12. Theseregression curves exhibit a deviation from the commonly assumedlinear trend between Iwci and Uw × ˛. The linear relationship iscommonly represented in the OTTV equation. The coefficient of cor-relation (R) for linear variation for all eight wall orientations in thethree climates ranges from 0.91 to 0.95. Thus, through assumptionof linear trend only 95% of the variation is explained. The philos-ophy behind assumption of linear trend is based on the fact thatsteady heat flux (gain or loss) is the product of U-value of the walland the temperature difference between outside and inside. Wallsolar absorptance (˛) accounts for incident radiation impinging onopaque wall. However, when accounting for conduction heat gainand loss through the wall for all hours of the year; in many casesthe direction of heat flow reverses during the 24 h diurnal cycleand from summers to winters. The linear relationship would suf-

Fig. 12. Correlation of Iwci (NE, SE, SW, NW walls) and Uw × ˛- Composite climate.

1 d Buildings 42 (2010) 1841–1861

dstfcdWcit

asrvitwotot

tlzhilalelwh

Fwoetltstt

Fig. 13. Correlation of Igci (N, S, E, W windows) and Uf – Composite climate.

TO


uction heat flow pattern in an air-conditioned office building; aecond degree polynomial relation has been attempted to correlatehe data of simulated values of Iwci with varying Uw × ˛. It has beenound in this study that second degree polynomial exhibits betterorrelation between the data of Iwci and Uw × ˛. The same secondegree polynomial relation has been attempted in the Hot-Dry andarm Humid climate also. For the three climates, the coefficient of

orrelation (R) for predicted values of Iwci for given values of Uw × ˛n case of second degree polynomial relationship ranges from 0.93o 0.96.

Hence, the single coefficient of TDeq in Eq. (11), now translatess two coefficients – one of the linear term and the other of theecond degree term. These coefficients are defined as LSU and SSU,espectively. The second degree coefficient (SSU) has a negativealue and it is unaffected by orientation of the wall surface andt has a different value for each climate. From this it can be inferredhat wall conduction heat gain intensity does not increase linearlyith Uw × ˛. In fact there is a diminishing effect related to the sec-

nd power of Uw × ˛. This can be attributed to the heat losses duringhe night and that is why SSU is independent of orientation. The ratef change of heat loss with Uw × ˛ is also proportional to Uw × ˛;hus the magnitude of this loss is proportional to (Uw × ˛)2.

Climatic zones where diurnal variation and annual variation ofemperature is small the reversal of heat flow direction would beeast; the linear relation would be mostly observed. For climaticones characterized by high diurnal and annual variation reversal ofeat flow direction would cause deviation from linear trend. Phys-

cally speaking there is always some amount of night and winteross in all tropical climates. But this effect is small for small diurnalnd annual variation of temperature. Even in those climates wherearge diurnal and annual temperature variations are observed thisffect would be small when wall transmittance (Uw) is small. Forarger U-values, the heat loss would be large at night and during

inter and when one looks at net annual energy implication forigher U-values the diminishing effect is encountered.

Figs. 13 and 14 represent the variation of Igci with Uf andigs. 15 and 16 represent the variation of Isoli with SCf for all eightall orientations in Composite climate. These curves have been

btained from large number of parametric results as mentionedarlier. In Figs. 13 and 14 again, there is a deviation from the linearrend. In fact, the coefficient of correlation (R) for Igci vs. Uf for the
inear relationship ranges from 0.8 to 0.84. Thus, through assump-ion of linear trend only 84% of the variation is explained. Hence, aecond degree polynomial relation has been attempted to correlatehe window conduction heat gain intensity (Igci) and the transmit-ance of glass (Uf). The same second degree polynomial relation
able 5rientation dependent OTTV coefficients – LSU and SF – for the three climates.

Orientation LSU

Composite Hot-Dry Warm-Hum

N 30.5 36.8 42.4S 40.5 44.9 49.6E 37.4 42.5 47.9W 41.9 47.6 52.3NE 33.0 38.9 44.5SE 39.9 44.3 49.2SW 43.3 48.2 52.5NW 36.0 42.2 47.3NNE 31.7 37.9 43.5ENE 35.2 40.7 46.2ESE 38.6 43.4 48.5SSE 40.2 44.6 49.4SSW 41.9 46.5 51.0WSW 42.6 47.9 52.4WNW 39.0 44.9 49.8NNW 33.2 39.5 44.9

Fig. 14. Correlation of Igci (NE, SE, SW, NW walls) and Uf – Composite climate.

has been attempted in the Hot-Dry and Warm Humid climate also.For the three climates, the coefficient of correlation (R) for pre-
dicted values of Igci and given values of Uf in case of second degreepolynomial relationship ranges from 0.97 to 0.98.
The coefficient of DT in Eq. (12) now translates as two coeffi-cients, one each of the linear and the second degree term, definedas LGU and SGU, respectively. It is observed that the coefficients in

SF

id Composite Hot-Dry Warm-Humid

92 98 137180 164 206164 159 200216 219 263115 118 159182 167 206221 213 253146 155 197104 108 148139 139 180173 163 203181 165 206201 188 230219 216 258181 187 230120 127 167


F

ttwblfc

sfleFvr

4

f

F

Table 6Orientation independent OTTV coefficients – SSU, LGU and SGU – for the three climates.

Coefficient Climate

Composite Hot-Dry Warm-Humid

SSU −4.45 −5.45 −5.75

ig. 15. Correlation of Isoli (N, S, E, W windows) and SC – Composite climate.

he polynomial equation do not change with orientation. Conduc-ion through window glass is unaffected by the orientation of theindow as it is dependent primarily on temperature difference

etween outside and inside rather than solar radiation. The non-inearity in this equation is also due to heat losses during nightor similar reasons as those of the walls, as explained earlier foronduction through opaque wall.

Figs. 15 and 16 show that window radiation heat gain inten-ity (Isoli) is linearly related to the shading coefficient of glass (SCf)or Composite climate and the coefficients of solar factor (SF) areargely dependent on the orientation of the window. The same lin-ar relation is seen in the Hot-Dry and Warm Humid climate also.or the three climates, the coefficient of correlation (R) for predictedalues of Isoli and given values of SCf in case of linear relationshipanges from 0.97 to 0.98.

.2. New OTTV formulation

A new OTTV formulation for the three climates thus obtainedrom regression analysis in this study can be written as:

OTTVi

= [SSU × (Uw × ˛)2 + LSU × (Uw × ˛)] × [1 − WWR]+[SGU × (Uf)

2 + LGU × Uf] × [WWR]+(SF × SC × WWR)

(14)

ig. 16. Correlation of Isoli (NE, SE, SW, NW walls) and SC – Composite climate.

LGU 10.5 14.5 15.5SGU −0.69 −0.95 −1.04LGU/SGU 15.2 15.2 14.9

The coefficients LSU, SSU, LGU, SGU and SF have been collectivelytermed in this study as ‘predetermined OTTV coefficients’ for officebuildings in the three tropical climates. Values of these coeffi-cients for sixteen orientations in the three climates are listed inTables 5 and 6. While the orientation dependent coefficients ofLSU and SF for N, S, E, W, NE, SE, SW and NW orientations havebeen obtained from regression analysis, the same coefficients forNNE, ENE, ESE, SSE, WSW, WNW and NNW have been obtained byinterpolation. All orientation independent values in Table 5 of SSU,LGU and SGU are averages of the close range of values depicted inFigs. 11–14. These predetermined OTTV coefficients may be usedby any architect or building designer to compute the OTTV of anyair-conditioned office building in any of the three climates.

This OTTV formulation [Eq. (14)] is valid for a building whenall exterior wall and window surfaces are unshaded. However, inactual cases, some or all of the building envelope surfaces in dif-ferent orientations may be fully or partially shaded by horizontalshading devices, vertical fins or adjacent surfaces. Although, themethod of obtaining ESM has been described for window radia-tion gain, the same ESM applies for wall conduction. Hence the ESMneeds to be applied to all three components of OTTV. For differentcombinations of P/L and pitch angles of walls (�), ESM tables wereobtained for all three climates. Tables A1–A4 in Appendix A providethe ESM tables for Composite climate. For use in OTTV calculation,appropriate value of ESM1 for wall shading and ESM2 for windowshading may be read depending on type of external shading.

OTTVi

= [SSU × (Uw × ˛)2 + LSU × (Uw × ˛)] × [1 − WWR] × ESM1

+[SGU × (Uf)2 + LGU × Uf] × [WWR]

+(SF × SC × WWR) × ESM2

(15)

5. Discussion of predetermined OTTV coefficients

The orientation dependent coefficient LSU varies between30.5–52.5 for different orientations and climates (Table 5). The low-est value of 30.5 is for Composite climate in North orientation.The highest value of 52.5 is for Warm-Humid climate in South-west orientation. In all three climates, Composite, Hot-Dry andWarm-Humid, the coefficient of LSU is highest for West, South-Westand West-South-West orientation. This is because the West direc-tion receives the peak solar radiation at the time of the day whenambient temperatures are also high. Hence the high temperaturedifferential and the effect of sol-air-temperature combine to resultin the highest LSU towards the West. The North receives the lowestincident solar radiation and hence the coefficient of LSU is lowestfor North in all three climates.

The negative value of SSU denotes the night and winter lossesby wall conduction. Although certain wall orientations have netheat losses during winters other orientations may have net heatgains even during the winter months because of sol–air tempera-
ture effect during the day. However, SSU depends on the U-value ofthe wall (Uw) and diurnal characteristics of the climate and henceis unaffected by orientation. The value of SSU varies between −4.45and −5.75 for the three climates (Table 6).

1854 S. Devgan et al. / Energy and Buildings 42 (2010) 1841–1861

Fi

ttcccwtdloiH

iwie

ctttCccedri

6

ibaEacsucfcc

From Figs. 17–19 it is also apparent that OTTV index can be usedto regulate annual space cooling and heating energy use and appro-priate limit may be fixed in the building energy codes depending onthe OTTV characteristics of the climate type. The percentage of enve-

ig. 17. Correlation of OTTV from predetermined coefficients and annual space cool-ng and heating energy use – Composite climate.

The coefficients of LGU and SGU representing DT (difference ofemperature) for glass conduction are both independent of orien-ation. The second degree polynomial relationship improves theorrelation between Igci and Uf significantly (by almost 15%) asompared to the commonly represented linear correlation. Theoefficient SGU has a negative value, representing the night andinter losses and because the Uf is significantly higher then Uw

he direction of heat flow reverses more often in case of glass con-uction as compared to the wall conduction. The coefficient LGU is

owest (10.5) in Composite climate but the ratio of LGU to SGU isf similar magnitude in all three tropical climates, indicating sim-lar glass conduction losses averaged over the year in Composite,ot-Dry and Warm-Humid climates.

The magnitude of SF for different orientations of windows isndicative of the solar gain one would expect from a particular

indow orientation on an annual basis. In all three climates, SFs highest for West and South-west and lowest in North and North-ast orientations.

From Tables 5 and 6 it is evident that the magnitude of the OTTVoefficients is highest for the Warm-Humid climate. This is becausehere is no winter period in Warm-Humid climate and the heat flowhrough the building envelope remains largely positive throughouthe year. Hence, the balancing effect due of heating period like inomposite climate is absent in Warm-Humid climate. Compositelimate has a more extreme winter as compared to the Hot-Drylimate and hence the magnitude of the OTTV coefficients is low-st for the Composite climate. Hot-Dry climate has the maximumiurnal variation of temperature throughout the year. Hence, theatio LSU/SSU for wall conduction heat transfer in Hot-Dry climates the lowest.

. Verification of the OTTV coefficients

The obtained OTTV coefficients (Tables 5 and 6) have been ver-fied with their use for the four case study office buildings. Theuildings were modeled without the effect of surface shading (bydjacent surfaces) so that OTTV coefficients can be used without theSMs. Parametric runs were carried out for the case study buildingsnd OTTV was computed using predetermined coefficients for eachase. This computed OTTV value was found to have linear relation-hip with the simulated annual space cooling plus heating energy
se per unit building floor area for all four buildings in the threelimates. The plot of this for the different values of OTTV, for allour buildings in the three climates is given in Figs. 17–19. Theoefficient of correlation, R for OTTV obtained from predeterminedoefficients and annual space cooling plus heating energy use per
Fig. 18. Correlation of OTTV from predetermined coefficients and annual space cool-ing and heating energy use – Hot-Dry climate.

unit building floor area ranges between 0.91 and 0.92. The OTTV isper unit envelope area and the annual cooling energy is per unitfloor area of the building. If the data of only one building is plotted,R is very high. No such correlation between OTTV values and sim-ulated annual space cooling plus heating energy use was found ifthe OTTV coefficients consisted of the linear term alone.

The constant values in these plots represent the energy usecontributed by the non-envelope related parameters such as light-ing, occupants, equipment and process loads. It is observed thatinternal loads have an indirect impact on annual space coolingand heating energy use. In composite climate, internal loads con-tribute in reducing the heating requirement during winters. Thereis no such effect in the Warm-Humid climate throughout the year.Hence for the same intensity of internal loads, the internal loadscomponent in the annual space cooling and heating energy use ishighest for the Warm-Humid climate and lowest for Compositeclimate.

Fig. 19. Correlation of OTTV from predetermined coefficients and annual space cool-ing and heating energy use – Warm-Humid climate.

S. Devgan et al. / Energy and Build

FO

luc

tmftd

TE

ig. 20. Correlation of OTTV obtained from predetermined coefficients and ESM andTTV from simulated heat gains.

ope contribution in the annual space cooling and heating energyse and target value of annual energy consumption can affect thehoice of the OTTV limit.

The ESM are also verified by modeling the case buildings withhe effect of surface shading and the OTTV obtained from predeter-

ined coefficients and ESM correlates well with the OTTV obtainedrom simulated heat gains, for all four case study buildings in allhree climates. Fig. 20 shows an illustration of this for a sampleata set of Composite climate.

able A1SM for horizontal shading device in Composite climate.

�1 orientation 0◦ 10◦

P/Af = 1 N 0.953 0.951S 0.616 0.614E 0.681 0.660W 0.663 0.624NE 0.837 0.829SE 0.631 0.618SW 0.624 0.601NW 0.736 0.716

P/Af = 0.5 N 0.959 0.958S 0.677 0.663E 0.773 0.751W 0.770 0.742NE 0.876 0.867SE 0.720 0.699SW 0.725 0.700NW 0.809 0.790



ings 42 (2010) 1841–1861 1855

7. Conclusion

A set of OTTV coefficients have been proposed for 3 types oftropical climates and verified. These coefficients can be used forcalculating the OTTV value for 12-h operation, high-rise or mid-rise air-conditioned office buildings, situated in places with similartropical climates. Using a similar method as described in this paper,OTTV coefficients can be derived for other building types with dif-ferent operation schedules. Regulating the OTTV of the buildingenvelope can result in energy efficient design of the building enve-lope as this study has shown that OTTV correlates well with annualspace cooling and heating energy use.

Acknowledgements

The authors would like to acknowledge the staff and manage-ment of M/s. Unitech Ltd. for imparting the necessary informationrelated to the four case study buildings. As a special mention theauthors would also like to thank Mr. Marlin Addison of DoE-2,United States, for valuable inputs in the simulation methods andanalysis.

Appendix A.

Tables A1–A4.

Appendix B.

Tables B1–B3.

20◦ 30◦ 40◦ 50◦

0.951 0.951 0.951 0.9510.614 0.613 0.613 0.6130.648 0.641 0.637 0.6370.597 0.579 0.568 0.5620.824 0.823 0.823 0.8220.611 0.607 0.606 0.6060.587 0.578 0.574 0.5720.698 0.686 0.681 0.678

0.957 0.956 0.956 0.9560.653 0.647 0.646 0.6490.734 0.723 0.717 0.7170.719 0.701 0.690 0.6860.860 0.855 0.853 0.8540.684 0.674 0.670 0.6720.681 0.668 0.661 0.6610.776 0.765 0.758 0.755

0.966 0.966 0.966 0.9680.760 0.759 0.763 0.7730.842 0.837 0.837 0.8400.837 0.829 0.825 0.8260.913 0.911 0.912 0.9140.811 0.807 0.808 0.8150.810 0.804 0.804 0.8090.861 0.856 0.855 0.858

0.951 0.951 0.951 0.9510.613 0.613 0.613 0.6130.639 0.637 0.636 0.6360.575 0.564 0.559 0.5590.823 0.822 0.822 0.8220.607 0.606 0.606 0.6060.577 0.572 0.571 0.5700.685 0.679 0.676 0.676

1856 S. Devgan et al. / Energy and Buildings 42 (2010) 1841–1861

Table A2ESM for vertical shading device with both side fins in Composite climate.

�2 orientation 0◦ 10◦ 20◦ 30◦ 40◦ 50◦

P/Af = 1 N 0.951 0.969 0.975 0.981 0.986 0.991S 0.762 0.853 0.856 0.864 0.873 0.884E 0.869 0.813 0.859 0.899 0.933 0.959W 0.942 0.636 0.673 0.723 0.777 0.824NE 0.836 0.996 0.999 1.000 1.000 1.000SE 0.898 0.704 0.709 0.730 0.766 0.814SW 0.763 0.835 0.864 0.894 0.914 0.932NW 0.828 0.805 0.766 0.738 0.728 0.745

P/Af = 0.5 N 0.951 0.965 0.968 0.971 0.974 0.976S 0.848 0.773 0.779 0.786 0.793 0.798E 0.921 0.740 0.767 0.790 0.809 0.823W 0.976 0.596 0.613 0.632 0.661 0.690NE 0.893 0.941 0.950 0.956 0.959 0.958SE 0.968 0.647 0.660 0.676 0.696 0.720SW 0.876 0.721 0.743 0.763 0.779 0.790NW 0.953 0.713 0.706 0.698 0.697 0.708



Table A3ESM for vertical shading device with right side fin in Composite climate.

�2 orientation 0◦ 10◦ 20◦ 30◦ 40◦ 50◦





S.Devgan

etal./Energy

andBuildings

42(2010)

1841–18611857

Table A4ESM for vertical shading device with left side fin in Composite climate.

�2 orientation 0◦ 10◦ 20◦ 30◦ 40◦ 50◦





1858S.D

evganet

al./Energyand

Buildings42

(2010)1841–1861

Table B1Comparison of Solar radiation Data of IWEC, ISHRAE, MANI and BANSAL – for New Delhi.

Month Day Houra Direct normal solar radiation Total horizontal solar radiation Solar altitude Solar azimuth

IWEC (Btu/hr ft2) IWEC (kWh/m2) MANI (kWh/m2) IWEC (Btu/hr ft2) IWEC (kWh/m2) MANI (kWh/m2) ISHRAE (kWh/m2) BANSAL (kWh/m2) IWEC MANI IWEC

1 1 7 0 0.064 0 0 0.004 0.106 0.108 1.17 1.2 117.171 1 8 0 0 0.316 7 0.022 0.096 0.236 0.256 8.04 8.3 121.791 1 9 97 0.306 0.509 54 0.170 0.271 0.348 0.424 18.67 19.2 130.861 1 10 210 0.662 0.611 114 0.359 0.433 0.399 0.558 27.75 28.7 142.181 1 11 259 0.816 0.674 163 0.514 0.556 0.445 0.641 34.52 36 156.251 1 12 281 0.886 0.69 194 0.612 0.618 0.450 0.669 38.06 40 172.831 1 13 285 0.898 0.689 204 0.643 0.615 0.408 0.641 37.69 40 190.331 1 14 285 0.898 0.664 191 0.602 0.552 0.354 0.558 33.50 36 206.531 1 15 259 0.816 0.609 156 0.492 0.43 0.243 0.424 26.24 28.7 220.081 1 16 210 0.662 0.512 104 0.328 0.268 0.116 0.256 16.83 19.2 230.951 1 17 93 0.293 0.333 43 0.136 0.099 0.000 0.108 5.99 8.3 239.671 1 18 0 0.000 0.078 4 0.013 0.005 0.000 0.1 0.10 1.2 243.496 3 6 0 0.000 2 0.006 0.036 0.144 0.207 6.46 68.176 3 7 16 0.050 0.136 29 0.091 0.164 0.275 0.411 18.93 17 74.426 3 8 88 0.277 0.249 92 0.290 0.339 0.420 0.61 31.78 29.7 80.266 3 9 170 0.536 0.329 166 0.523 0.51 0.605 0.779 44.85 42.8 86.236 3 10 214 0.675 0.381 232 0.731 0.657 0.793 0.911 58.02 55.9 93.396 3 11 250 0.788 0.403 282 0.889 0.757 0.897 0.99 71.02 69 105.046 3 12 276 0.870 0.415 312 0.984 0.809 0.897 1.02 82.29 81.2 144.436 3 13 278 0.876 0.419 320 1.009 0.813 0.828 0.99 78.84 81.2 237.606 3 14 274 0.864 0.4 304 0.958 0.75 0.665 0.911 66.45 69 260.116 3 15 246 0.776 0.361 266 0.839 0.639 0.508 0.779 53.34 55.9 269.406 3 16 205 0.646 0.3 210 0.662 0.483 0.333 0.61 40.19 42.8 275.966 3 17 148 0.467 0.232 140 0.441 0.318 0.161 0.411 27.18 29.7 281.806 3 18 62 0.195 0.151 67 0.211 0.159 0.028 0.207 14.45 17 287.736 3 19 0 0.000 14 0.044 0.036 0.000 4.08 293.14

a The value in this column refers to the hour time, i.e. 7 refers to 7 a.m. and the previous two columns give the date and the number of the month in a year respectively (starting January = 1).

S.Devgan

etal./Energy

andBuildings

42(2010)

1841–18611859

Table B2Comparison of solar radiation data of IWEC, ISHRAE, MANI and BANSAL – for Ahamadabad.

Month Day Hour Direct normal solar radiation Total horizontal solar radiation Solar altitude Solar azimuth


1 1 7 0 0 0.050 0 0.011 0.149 0.11 0.57 2.4 115.431 1 8 0 0 0.435 4 0.013 0.147 0.346 0.261 7.27 10.7 118.881 1 9 69 0.218 0.68 48 0.151 0.36 0.526 0.431 18.90 22.3 126.521 1 10 165 0.520 0.78 113 0.356 0.544 0.656 0.565 29.25 32.5 136.541 1 11 245 0.772 0.828 169 0.533 0.674 0.717 0.648 37.57 40.5 149.761 1 12 261 0.823 0.844 207 0.653 0.741 0.717 0.676 42.75 45.1 166.541 1 13 269 0.848 0.833 224 0.706 0.735 0.650 0.648 43.72 45.1 185.481 1 14 268 0.845 0.797 218 0.687 0.661 0.523 0.565 40.23 40.5 203.481 1 15 257 0.810 0.745 188 0.593 0.529 0.343 0.431 33.11 32.5 218.251 1 16 224 0.706 0.638 138 0.435 0.345 0.150 0.261 23.51 22.3 229.541 1 17 148 0.467 0.42 74 0.233 0.138 0 0.11 12.35 10.7 238.101 1 18 16 0.050 0.061 14 0.044 0.011 0 0.1 3.22 2.4 243.286 4 6 0 0 0 0 0 0.024 0.155 0.204 3.43 67.136 4 7 0 0 0.148 11 0.035 0.145 0.319 0.409 13.38 15.4 71.116 4 8 62 0.195 0.236 62 0.195 0.314 0.499 0.609 26.60 28.6 75.586 4 9 41 0.129 0.301 112 0.353 0.484 0.691 0.779 40.08 42.1 79.526 4 10 130 0.410 0.375 187 0.590 0.641 0.803 0.911 53.72 55.6 83.206 4 11 185 0.583 0.414 252 0.794 0.749 0.814 0.991 67.47 69.3 87.006 4 12 216 0.681 0.442 296 0.933 0.804 0.716 1.021 81.26 83.1 92.836 4 13 242 0.763 0.467 316 0.996 0.822 0.657 0.991 84.83 83.1 263.176 4 14 264 0.832 0.467 310 0.977 0.765 0.530 0.911 71.04 69.3 271.886 4 15 256 0.807 0.439 279 0.880 0.652 0.411 0.779 57.27 55.6 275.866 4 16 226 0.712 0.39 227 0.716 0.498 0.251 0.609 43.60 42.1 279.526 4 17 179 0.564 0.286 158 0.498 0.316 0.122 0.409 30.08 28.6 283.366 4 18 96 0.303 0.148 84 0.265 0.141 0 0.204 16.78 15.4 287.666 4 19 17 0.054 0 21 0.066 0.022 0 5.08 292.16

1860S.D

evganet

al./Energyand

Buildings42

(2010)1841–1861

Table B3Comparison of solar radiation data of IWEC, ISHRAE, MANI and BANSAL – for Chennai.

Month Day Hour Direct normal solar radiation Total horizontal solar radiation Solar altitude Solar azimuth


1 1 7 0 0 0.08 2 0.006 0.125 0.178 6.08 4.50 115.361 1 8 40 0.126 0.382 35 0.110 0.029 0.241 0.390 18.48 15.60 120.031 1 9 141 0.444 0.53 105 0.331 0.194 0.342 0.582 30.68 28.30 127.141 1 10 194 0.612 0.517 167 0.526 0.397 0.416 0.728 41.51 40.00 137.771 1 11 192 0.605 0.535 209 0.659 0.543 0.456 0.820 49.83 49.60 153.611 1 12 171 0.539 0.581 224 0.706 0.660 0.458 0.852 53.84 55.30 175.081 1 13 188 0.593 0.62 235 0.741 0.732 0.462 0.820 52.15 55.30 197.941 1 14 194 0.612 0.629 223 0.703 0.749 0.381 0.728 45.41 49.60 216.301 1 15 197 0.621 0.616 187 0.590 0.697 0.290 0.582 35.45 40.00 228.921 1 16 147 0.463 0.538 125 0.394 0.579 0.151 0.390 23.73 28.30 237.321 1 17 60 0.189 0.459 57 0.180 0.414 0.026 0.178 11.03 15.60 243.001 1 18 0 0 0.175 6 0.019 0.207 0.000 0.100 2.24 4.50 245.756 9 6 0 0 0 0 0.000 0.107 0.125 4.93 67.606 9 7 0 0 0.118 13 0.041 0.007 0.228 0.335 16.73 11.80 69.656 9 8 17 0.054 0.259 54 0.170 0.099 0.343 0.554 30.49 25.50 70.876 9 9 23 0.073 0.356 101 0.318 0.271 0.480 0.742 44.30 39.30 70.556 9 10 52 0.164 0.432 154 0.485 0.458 0.559 0.888 57.97 53.10 67.406 9 11 74 0.233 0.464 200 0.630 0.627 0.601 0.978 70.97 66.40 56.226 9 12 96 0.303 0.474 234 0.738 0.745 0.545 1.010 79.89 77.70 11.136 9 13 44 0.139 0.452 223 0.703 0.799 0.503 0.978 74.25 77.70 310.796 9 14 61 0.192 0.402 193 0.608 0.788 0.425 0.888 61.74 66.40 294.486 9 15 38 0.120 0.346 151 0.476 0.706 0.344 0.742 48.17 53.10 289.956 9 16 70 0.221 0.279 140 0.441 0.577 0.225 0.554 34.38 39.30 289.046 9 17 65 0.205 0.203 95 0.299 0.415 0.091 0.335 20.59 25.50 289.886 9 18 31 0.098 0.105 35 0.110 0.244 0.000 0.125 6.93 11.80 291.996 9 19 0 0 0 2 0.006 0.091 0.000 0.09 293.53

Build

R

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[

[eling and calibration in warm climates, Building & Environment 37 (2002)903–912.

[30] A. Mani, Handbook of Solar Radiation Data for India, Allied Publishers Private

S. Devgan et al. / Energy and

eferences

[1] http://www.bca.gov.sg/PerformanceBased/others/ETTV.pdf.;http://www.bca.gov.sg/PerformanceBased/others/RETV.pdf.

[2] http://www.bd.gov.hk/english/documents/code/e ottv.html.[3] http://www.iea.org/Textbase/work/2006/buildings/surapong background.pdf.[4] http://www.meew.gov.mv/projects/smiles/reports/Reports and Documents/

07.DSM%20Mission-2%20 (Feb%202005)/Building%20energy-%20BEEC%20Sri%20Lanka.pdf.

[5] http://www.peemac.sdnpk.org/resource/pdf/buildingecp.pdf.[6] F.W.H. Yik, K.S.Y. Wan, An evaluation of the appropriateness of using overall

thermal transfer value (OTTV) to regulate envelope energy performance of air-conditioned buildings, Energy 30 (2005) 41–71.

[7] http://www.jbs.org.jm/nbc/JamaicaApplicationDocumentIECCFinal.pdf.[8] J. Huang, J. Deringer, K. Moncef, M. Jamil, The development of residential and

commercial building energy standards for Egypt. LBNL-55521, in: Proceedingsof Energy Conservation in Buildings Workshop, Kuwait, 2003.

[9] K.T. Chan, W.K. Chow, Energy impact of commercial-building envelope in sub-tropical climate, Applied Energy 60 (1998) 21–39.

10] J.C. Lam, S.C.M. Hui, S.Y. Yuen, OTTV calculation for Hong Kong part 1—heat gain,research report no. BC/92/05, Hong Kong: Department of Building & Construc-tion, The City University of Hong Kong, 1992.

11] J.C. Lam, S.C.M. Hui, OTTV calculation for Hong Kong part 2—cooling load,research report no. BC/92/11, Hong Kong: Department of Building & Construc-tion, The City University of Hong Kong, 1992.

12] Lawrence Berkeley Laboratory, DOE-2 reference manual, version 2.1A, Berke-ley, CA: Energy and Environmental Division, Lawrence Berkeley Laboratory,LBL-8706 Rev. 2, LA-7689-M (Rev. 2), 1981.

13] W.K. Chow, K.T. Chan, Overall thermal transfer values for building envelopesin Hong Kong, Applied Energy 42 (4) (1992) 289–312.

14] W.K. Chow, K.T. Chan, Parameterization study of the overall thermal transfervalue equations for buildings, Applied Energy 50 (3) (1995) 247–268.

15] W.K. Chow, P.C.H. Yu, Controlling building energy use by overall thermal trans-fer value (OTTV), Energy 25 (5) (2000) 463–478.

16] S.C.M. Hui, Energy performance of air-conditioned buildings in Hong Kong,Unpublished PhD Thesis, City University of Hong Kong, 1996.

[

ings 42 (2010) 1841–1861 1861

17] I. Turiel, R. Curtis, M.D. Levine, Analysis of energy conservation standards forSingapore Office Buildings, Energy 10 (1985) 95–107.

18] S.K. Chow, Y.K. Lee, A simplified overall thermal transfer value equation forbuilding envelopes, Energy 13 (1988) 657–670.

19] J.C. Lam, Energy analysis of commercial buildings in subtropical climates, Build-ing & Environment 35 (2000) 19–26.

20] P.C.H. Yu, W.K. Chow, Energy use in commercial buildings in Hong Kong,Applied Energy 69 (2001) 243–255.

21] S. Chirarattananon, J. Taveekun, An OTTV-based energy estimation model forcommercial buildings in Thailand, Energy and Buildings 36 (2004) 680–689.

22] Energy Conservation Building Code, Bureau of Energy Efficiency, Ministry ofPower, Government of India, http://www.bee-india.nic.in, 2007.

23] J.C. Lam, K.K.W. Wan, C.L. Tsang, L. Yang, Building energy efficiency in differentclimates, Energy Conversion and Management 49 (8) (2008) 2354–2366.

24] H. Radhi, Can envelope codes reduce electricity and CO2 emissions in differenttypes of buildings in the hot climate of Bahrain? Energy 34 (2009) 205–215.

25] O.T. Masoso, L.J. Grobler, A new and innovative look at anti-insulation behaviorin building energy consumption, Energy and Buildings 40 (2008) 1889–1894.

26] http://www.eere.energy.gov/buildings/energyplus/cfm/weather data3.cfm/region=2 asia wmo region 2/country=IND/cname=India.

27] S. Devgan, Development of OTTV based approach for three tropical climates asan alternative to prescriptive approach in building energy codes, PhD thesis,submitted to Indian Institute of Technology, Delhi, India, 2009.

28] R. Sullivan, F. Winkelmann, Validation Studies of the DOE-2 Building EnergySimulation Program, Final report, Environment Energy Technologies Division,LBNL-42241, 1998.

29] A. Pedrini, F.S. Westphal, R. Lamberts, A methodology for building energy mod-

Limited, 1981.31] N.K. Bansal, G. Minke, Climatic Zones & Rural Housing in India, Part 1 of Indo-

German project on Passive Space Conditioning, 1988.

http://www.bca.gov.sg/PerformanceBased/others/ETTV.pdf

http://www.bca.gov.sg/PerformanceBased/others/RETV.pdf

http://www.bd.gov.hk/english/documents/code/e_ottv.html

http://www.iea.org/Textbase/work/2006/buildings/surapong_background.pdf

http://www.meew.gov.mv/projects/smiles/reports/Reports_and_Documents/07.DSM%20Mission-2

http://www.peemac.sdnpk.org/resource/pdf/buildingecp.pdf

http://www.jbs.org.jm/nbc/JamaicaApplicationDocumentIECCFinal.pdf

http://www.bee-india.nic.in/

http://www.eere.energy.gov/buildings/energyplus/cfm/weather_data3.cfm/region=2_asia_wmo_region_2/country=IND/cname=India