Evaluation of the Impact of the Surrounding Urban Morphology on Building Energy Consumption

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Evaluation of the impact of the surrounding urban morphology

Evaluation of the impact of the surrounding urban morphology

on building energy consumption

on building energy consumption

Nyuk Hien Wong

Nyuk Hien Wong

aa

, Steve Kardinal Jusuf 

, Steve Kardinal Jusuf 

bb,,⇑⇑

, Nedyomukti Imam Syafii

, Nedyomukti Imam Syafii

cc

, Yixing Chen

, Yixing Chen

aa

,,

Norwin Hajadi

Norwin Hajadi

aa

, Haripriya Sathyanarayanan

, Haripriya Sathyanarayanan

aa

, Yamini Vidya Manickavasagam

, Yamini Vidya Manickavasagam

aa

a

aDepartment of Building, National University of Singapore, SingaporeDepartment of Building, National University of Singapore, Singapore b

bCenter for Sustainable Asian Cities, National University of Singapore, SingaporeCenter for Sustainable Asian Cities, National University of Singapore, Singapore c

cInstitute of High Performance Computing, SingaporeInstitute of High Performance Computing, Singapore

Received 14 June 2010; received in revised form 30 October 2010; accepted 1 November 2010 Received 14 June 2010; received in revised form 30 October 2010; accepted 1 November 2010

Available online 3 December 2010 Available online 3 December 2010 Commun

Communicated by: icated by: AssociaAssociate te Editor Matheos Editor Matheos SantamSantamourisouris

Abstract Abstract

Empirical models of minimum (

Empirical models of minimum (T T minmin), average (), average (T T avgavg) and maximum () and maximum (T T maxmax) air temperature for Singapore estate have been developed) air temperature for Singapore estate have been developed

and validated based on a long-tem field measurement. There are three major urban elements, which influence the urban temperature at and validated based on a long-tem field measurement. There are three major urban elements, which influence the urban temperature at the local scale. Essentially, they are buildings, greenery and pavement. Other related parameters identified for the study, such as green the local scale. Essentially, they are buildings, greenery and pavement. Other related parameters identified for the study, such as green plot ratio (GnPR), sky view factor (SVF), surrounding building density, the wall surface area, pavement area, albedo are also evaluated plot ratio (GnPR), sky view factor (SVF), surrounding building density, the wall surface area, pavement area, albedo are also evaluated to give

to give a a bettebetter r underunderstandstanding on ing on the likely the likely impacimpact t of the of the modifimodified urban ed urban morphmorphology on ology on energenergy y consuconsumptiomption.n.

The objective of this research is to assess and to compare how the air temperature variation of urban condition can affect the building The objective of this research is to assess and to compare how the air temperature variation of urban condition can affect the building ene

energyrgy conconsumsumptiptionon inin trotropicpicalal cliclimatmatee ofof SinSingapgaporeore.. InIn ordorderer toto achachievievee thithiss goagoal,l, aa serseriesies ofof numnumeriericalcal calcalculculatiationon andand buibuildildingng simsimulaulatiotionn are

are utiutilizlized.ed. AA tottotalal ofof 3232 cascases,es, conconsidsiderieringng diffdiffereerentnt urburbanan mormorphophologlogiesies,, areare ideidentintifiedfied andand evaevalualuatedted toto givegive betbetterter aa undunderserstantandindingg onon thethe implication of urban forms, with the reference to the effect of varying density, height and greenery density. The results show that GnPR, implication of urban forms, with the reference to the effect of varying density, height and greenery density. The results show that GnPR, which related to the present of greenery, have the most significant impact on the energy consumption by reducing the temperature by up which related to the present of greenery, have the most significant impact on the energy consumption by reducing the temperature by up to 2

to 2°°C. The results also strongly indicate an energy saving of 4.5% if the urban elements are addressed effectively.C. The results also strongly indicate an energy saving of 4.5% if the urban elements are addressed effectively. Ó

Ó2010 Elsevier Ltd. All rights reserved.2010 Elsevier Ltd. All rights reserved.

Keywords:

Keywords: Impact; Urban Impact; Urban morpholomorphology; gy; Building energy Building energy consumptconsumption; Energy ion; Energy simulatsimulation; Singaporeion; Singapore

1.

1. IntroduIntroductionction Ur

Urbabaniznizatatioion n in in ththe e rerececent nt yeyearars s hahas s sisigngnificificanantlytly increased the necessity for the city to further develop itself  increased the necessity for the city to further develop itself  to accommodate the incoming inhabitants, in which to accommodate the incoming inhabitants, in which ame-liorate the

liorate the urban microcliurban microclimate. The mate. The develodevelopment may pment may leadlead to the increase of urban heat island (UHI) intensity, a to the increase of urban heat island (UHI) intensity, a phe-nomenon which air temperature in densely built urban area nomenon which air temperature in densely built urban area

are higher than the temperature of the surrounding rural are higher than the temperature of the surrounding rural area. Higher urban temperature has a serious impact on area. Higher urban temperature has a serious impact on the electricity demand for air conditioning of buildings, it the electricity demand for air conditioning of buildings, it also increase the smog production, while contributing to also increase the smog production, while contributing to increased emission of pollutants from power plants, increased emission of pollutants from power plants, includ-ing carbon dioxide, sulfur dioxide, nitrous oxides and other ing carbon dioxide, sulfur dioxide, nitrous oxides and other suspended particulates. UHI, however, could be found in suspended particulates. UHI, however, could be found in ev

everery y totown wn anand d cicity ty alall l ovover er ththe e woworld (rld (OkOke e anand d EaEas,s,

197

1971; 1; LanLandsbdsbergerg, , 1981981; 1; PadPadmanmanabhabhamuamurty, rty, 1991990/10/1991991;;

Sani, 1990/1991; Eliasson, 1996; Giridharan et al., 2007

Sani, 1990/1991; Eliasson, 1996; Giridharan et al., 2007).). The heat island intensity could easily reach up to 10 The heat island intensity could easily reach up to 10°°C,C,

which has been observed in India. Furthermore, it is clear which has been observed in India. Furthermore, it is clear th

that at ththis is inincrcreasease e of of ururbaban n aiair r tetempmpererataturure e wilwill l alalsoso increase the energy consumption by increasing the cooling increase the energy consumption by increasing the cooling

0038-092X/$ - see front matter

0038-092X/$ - see front matterÓÓ2010 Elsevier Ltd. All rights reserved.2010 Elsevier Ltd. All rights reserved.

doi:

doi:10.1016/j.solener.2010.11.00210.1016/j.solener.2010.11.002

Corresponding author. Address: Center for Sustainable Asian Cities,Corresponding author. Address: Center for Sustainable Asian Cities,

Nat

Nationaional l UniUniversversity ity of of SingSingaporapore, e, 4 4 ArcArchitehitecturcture e DriDrive, ve, SingSingapoaporere 117566, Singapore. Tel.: +65 6516 4691.

117566, Singapore. Tel.: +65 6516 4691.

E-mail address:

E-mail address:steve.kj@nus.edu.sgsteve.kj@nus.edu.sg(S.K. Jusuf).(S.K. Jusuf).

www.elsevier.com/locate/solener www.elsevier.com/locate/solener

Solar Energy 85 (2011) 57–71 Solar Energy 85 (2011) 57–71

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load. In city of Athens, where the mean heat island load. In city of Athens, where the mean heat island inten-sity are found exceeding 10

sity are found exceeding 10 °°C, the cooling load of buildingC, the cooling load of building

in the urban area found to be doubled, the peak electricity in the urban area found to be doubled, the peak electricity load for cooling may be tripled because of the higher load for cooling may be tripled because of the higher ambi-ent temperature

ent temperatures s ((Santamauris et al., 2001Santamauris et al., 2001). Study by). Study by Kol-

Kol-okotroni et al. (2005)

okotroni et al. (2005) on London UHI found that duringon London UHI found that during typical hot week the rural reference office has 84% energy typical hot week the rural reference office has 84% energy demand for cooling as compared to a similar urban office demand for cooling as compared to a similar urban office based in the same location. Another study utilizing based in the same location. Another study utilizing mea-sured air

sured air temperatemperature data ture data and building energy simulationand building energy simulation at 24 different locations within London UHI found that at 24 different locations within London UHI found that urban cooling load is up to 25% higher than the rural load urban cooling load is up to 25% higher than the rural load over the year; however the annual heating load is reduced over the year; however the annual heating load is reduced by 22% (

by 22% (Kolokotroni et al., 2007Kolokotroni et al., 2007). Other urban modifica-). Other urban modifica-tions, nevertheless, also have been found altering the tions, nevertheless, also have been found altering the tem-p

pererataturure e in in ururbaban n aarerea a anand d yyet et momodidify fy ththe e enenerergygy consumption. Study in USA (

consumption. Study in USA (Akbari et al., 1992Akbari et al., 1992) found) found that large number of trees and urban parks able to reduce that large number of trees and urban parks able to reduce local air temperature by 0.5–5

local air temperature by 0.5–5 °°C. Each 1C. Each 1°°C drop in airC drop in air

temperature could lower the peak electric demand for temperature could lower the peak electric demand for cool-ing by 2–4%. Hence, urban elements such as trees or parks ing by 2–4%. Hence, urban elements such as trees or parks have significant impact and energy consumption.

have significant impact and energy consumption. Shashua-

Shashua-Bar et al. (2010)

Bar et al. (2010)studied the variables that influence the var-studied the variables that influence the var-iab

iabilitility y of of air air temptemperaterature ure in in urburban an strstreeteets s thathat t incincludludee trees, building configuration, albedo of the surroundings trees, building configuration, albedo of the surroundings and street ventilation. Some studies also investigated the and street ventilation. Some studies also investigated the selection of building materials which influences not only selection of building materials which influences not only the outdoor space but also the building energy the outdoor space but also the building energy consump-ti

tion on ((TaTahaha, , 19199797; ; AkAkbabari ri et et alal., ., 20200101; ; DoDoululos os et et alal.,.,

2004

2004). These variables are in the control of the urban plan-). These variables are in the control of the urban plan-ners and architects. Hence, the urban air temperature can ners and architects. Hence, the urban air temperature can be attenuated with a proper design and modeling.

be attenuated with a proper design and modeling.

In Singapore, with the present trend of having building In Singapore, with the present trend of having building going higher and closer to one another, as well as the going higher and closer to one another, as well as the exten-siv

sive e usausage ge of of air conditair conditioniioning, UHI ng, UHI are likely to are likely to occoccurur ((Wong and Chen, 2009; Jusuf et al., 2007Wong and Chen, 2009; Jusuf et al., 2007). The tempera-). The tempera-ture increases in urban area can lead to significant use of  ture increases in urban area can lead to significant use of  air conditioning. UHI studies in Singapore shows a air conditioning. UHI studies in Singapore shows a possi-ble increase of urban air temperature of 1

ble increase of urban air temperature of 1°°C. If the trendC. If the trend

keep on continue, within 50 year, the energy consumption keep on continue, within 50 year, the energy consumption for cooling will increase in order of 33 GW h per annum for cooling will increase in order of 33 GW h per annum

for the whole island (

for the whole island (Tso, 1994Tso, 1994). More recent study found). More recent study found that outdoor air temperature determines the energy savings that outdoor air temperature determines the energy savings of buildings. According to some studies, every 1

of buildings. According to some studies, every 1 °°C of out-C of

out-door air temperature reduction saves 5% of building energy door air temperature reduction saves 5% of building energy consumption (

consumption (Chen and Wong, 2006; Wong et al., 2009Chen and Wong, 2006; Wong et al., 2009).). It becomes increasingly important to study urban It becomes increasingly important to study urban micro-cli

climamatic tic enenvirvirononmenments ts anand d to to apapplply y ththesese e finfindidingngs s toto improve the people’s comfort and to decrease the energy improve the people’s comfort and to decrease the energy consumption in the urban areas. The present paper consumption in the urban areas. The present paper illus-trates the result of an urban study carried out in Singapore trates the result of an urban study carried out in Singapore aiming to assess and discuss the impact of the urban aiming to assess and discuss the impact of the urban mor-ph

pholologogyy onon ththee enenerergygy coconsnsumumptptionofionof ThThee PIPIXELXEL atat BuBuononaa Vista. Computer simulation software, TAS and Singapore Vista. Computer simulation software, TAS and Singapore urban air temperature prediction model, STEVE tool are urban air temperature prediction model, STEVE tool are utilize

utilized to evad to evaluateluate the impthe impactact of incrof increasedeased ambienambient tempet temper- r-ature on the cooling performance of building (see

ature on the cooling performance of building (see Fig. 1Fig. 1).). 2. Methodologies

2. Methodologies  2.1. Object of study  2.1. Object of study

The Pixel is a 3-storey air conditioned office building, The Pixel is a 3-storey air conditioned office building, used by a leading digital media company. It is located in used by a leading digital media company. It is located in the one-north estate, Singapore. The facility is also the one-north estate, Singapore. The facility is also strate-gically located in the midst of landscaped greeneries with gically located in the midst of landscaped greeneries with park opposite its main entrance.

park opposite its main entrance.  2.2. The case studies

 2.2. The case studies A base case (

A base case (Fig. 2Fig. 2) and 32 cases () and 32 cases (Fig. 3Fig. 3) were ) were employemployeded to have better understanding on the impact of different to have better understanding on the impact of different sur-rounding urban morphologies to the energy consumption rounding urban morphologies to the energy consumption of the building. These various cases will address the of the building. These various cases will address the opti-mum solution between greenery, building area and building mum solution between greenery, building area and building densit

density y to address the to address the impact of the impact of the tempertemperature on energyature on energy consumption of the PIXEL building.

consumption of the PIXEL building. Table 1Table 1 shows theshows the different parameters used as the case studies.

different parameters used as the case studies.

The base-case model consists of building and greenery, The base-case model consists of building and greenery, which represent the actual surrounding urban morphology which represent the actual surrounding urban morphology of the PIXEL building. A typical hot day condition on 21st of the PIXEL building. A typical hot day condition on 21st May 2008 was chosen as the background temperature for May 2008 was chosen as the background temperature for

Fig. 1. Aerial view and front photo of the PIXEL building. Fig. 1. Aerial view and front photo of the PIXEL building.

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the STEVE tool. As mentioned, STEVE tool predicts the the STEVE tool. As mentioned, STEVE tool predicts the air temperature of a point based on the 50 m radius. As air temperature of a point based on the 50 m radius. As to cover up the area of study, four circles of 50 m radius to cover up the area of study, four circles of 50 m radius were developed for the STEVE model. Named as A, B, C were developed for the STEVE model. Named as A, B, C and D, each of these circles will have its own calculation, and D, each of these circles will have its own calculation, see

see Fig. 2Fig. 2. The final predicted air temperature, which is. The final predicted air temperature, which is the avera

the average ge of of foufour r prepredictdicted ed air temperair temperatuature re (A, B, (A, B, CC and D), will be used in the TAS software as the boundary and D), will be used in the TAS software as the boundary condition to develop cooling load and energy consumption condition to develop cooling load and energy consumption demand of the building.

demand of the building.

Given the same methodology as the Base Case, the first Given the same methodology as the Base Case, the first 12 Cases (Case 1–Case 12) are trying to find out the role of  12 Cases (Case 1–Case 12) are trying to find out the role of  the individual paramet

the individual parameter on er on the urban air the urban air tempertemperature andature and the building cooling load. These basic parameters are the the building cooling load. These basic parameters are the surrou

surrounding nding greenegreenery, ry, quantquantified ified asas GreGreen en PloPlot t RatRatioio – –  GnPR (Case 1–Case 4), the surrounding building height GnPR (Case 1–Case 4), the surrounding building height  – HEIGHT (Case 5–Case 8) and surrounding building  – HEIGHT (Case 5–Case 8) and surrounding building den-sity – DENSITY (Case 9–Case 12). The next 12 cases (Case sity – DENSITY (Case 9–Case 12). The next 12 cases (Case 13–Case 24) are basically the combination of two of these 13–Case 24) are basically the combination of two of these parameters. Combinations are worked out based on parameters. Combinations are worked out based on uni-fo

formrm, , rarandndom om anand d ststraratutum m effeffecectsts. . CoCombmbininatatioion n of of  HEIG

HEIGHT HT and DENSIand DENSITY, TY, for exampfor example, le, has impacthas impacts s onon the greenery and sky view factors (SVF). The last eight the greenery and sky view factors (SVF). The last eight cases (Case 25–Case 32), are combination of three of cases (Case 25–Case 32), are combination of three of them.them. The

These se eigeight ht cascases es of of pospossibsible le comcombinbinatioation n were were havhavinging

similar methodology as the base case in order to have a fair similar methodology as the base case in order to have a fair comparison.

comparison.   2.3. STEVE tool    2.3. STEVE tool 

Scr

Screeneening ing TooTool l for for EstEstate ate EnvEnviroironmennment t EvaEvalualuationtion,, STEVE tool (

STEVE tool (Jusuf and Wong, 2009Jusuf and Wong, 2009), is a web based appli-), is a web based appli-cation that is specific to an estate and it calculates the cation that is specific to an estate and it calculates theT T maxmax,,

T avgavgandand T T minmin of a point interest of an estate. A set of threeof a point interest of an estate. A set of three

equations shown in Eq.

equations shown in Eq. (1)(1) gives the correlation betweengives the correlation between the

the urburban an mormorphophologlogy y parparameameterters s (bu(buildiilding, ng, pavpavemeementnt and greenery) and estate

and greenery) and estate air temperatureair temperature. . These predictioThese predictionn models were based on the empirical data collected over a models were based on the empirical data collected over a period of close to 3 years as part of the development of  period of close to 3 years as part of the development of  an

an assassessessmenment t metmethod to hod to evaevalualuate te the impact of the impact of estestateate development (in this case, NUS Kent Ridge Campus and development (in this case, NUS Kent Ridge Campus and One

One NorNorth)th), , whiwhich ch incincludludes es the the assassessmessment ent metmethod of hod of  existing greenery condition (

existing greenery condition (Wong and Jusuf, 2008aWong and Jusuf, 2008a) and) and greenery condition for a proposed master plan in an estate greenery condition for a proposed master plan in an estate dev

develoelopmenpment t ((WoWong ng anand d JuJususuf, f, 20200808bb). ). ThThe e grgreeeeneneryry assessment used Green Plot Ratio (GnPR) method. The assessment used Green Plot Ratio (GnPR) method. The GnPR is derived from the average of greenery on a lot, GnPR is derived from the average of greenery on a lot, using the leaf area index (LAI), in proportion to the total using the leaf area index (LAI), in proportion to the total lot area (

lot area (Ong, 2003Ong, 2003). The higher the GnPR value, the den-). The higher the GnPR value, the den-ser the greenery condition in a built environment.

ser the greenery condition in a built environment.

Fig. 2.

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T  

T  minmin ððCCÞ Þ ¼¼ 44::061061 þþ 00::839 Ref 839 Ref T  T  minmin ððCÞ þCÞ þ 00::004 PAVE004 PAVE ðð%%Þ ÀÞ À 00::193 GnPR193 GnPR ÀÀ 00::029 HBDG029 HBDG þþ 11::339E339E ÀÀ 06 WALL06 WALL ððmm22ÞÞ T  

T  avgavg ððCCÞ Þ ¼¼ 22::347347 þþ 00::904 Ref 904 Ref T  T  avgavg ððCÞ þCÞ þ 55::786E786E ÀÀ 05 SOLAR05 SOLARtotaltotal ððWW==mm22Þ þÞ þ 00::007 PAVE007 PAVE ðð%%ÞÞ

À

À 00::06 GnPR06 GnPR ÀÀ 00::015 HBDG015 HBDG þþ 11::311 E311 E ÀÀ 05WALL05WALL ððmm22Þ þÞ þ 00::633 SVF633 SVF

T  

T  maxmax ððCCÞ Þ ¼¼ 77::542542 þþ 00::684 Ref 684 Ref T  T  maxmax ððCCÞ þÞ þ 00::003 SOLAR003 SOLARmaxmax ððWW==mm22Þ þÞ þ 00::005 PAVE005 PAVE ðð%%ÞÞ

À

À 00::016 HBDG016 HBDG þþ 66::777E777E ÀÀ 06 WALL06 WALL ððmm22Þ þÞ þ 11::467 SVF467 SVF þþ 11::466 ALB466 ALB ðð11ÞÞ

Fig. 3. 32 Study cases. Fig. 3. 32 Study cases.

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Dai

Daily ly minminimuimum m ((T T minmin), average (), average (T T avgavg) ) and maximand maximumum

((T T maxmax) temperature of each point of measurements were) temperature of each point of measurements were

calculated as dependent variable of the air temperature calculated as dependent variable of the air temperature pre-diction model. The independent variables of the models can diction model. The independent variables of the models can be categorized into:

be categorized into:

1. Clima

1. Climate te prepredicdictortors:s: daily minidaily minimummum ((T T min-rmin-r),), averageaverage

((T T avg-ravg-r)) and maximumand maximum ((T T max-rmax-r)) temperaturetemperature at meteoro-at

meteoro-log

logicaical l stastatiotion;n; aaveverarage ge oof f dadaiily ly sosolalar r raraddiaiatitionon (SOLAR). For the SOLAR predictor, average of daily (SOLAR). For the SOLAR predictor, average of daily sol

solar ar radradiatiiation on tottotal al (SO(SOLARLARtotaltotal) ) wawas s usused ined in T T avgavg

Fig. 3

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models, while average of solar radiation maximum of  models, while average of solar radiation maximum of  th

the e daday y (S(SOLAOLARRmaxmax) ) wawas s uusesed d in in ththee T T maxmax model.model.

SOLAR predictor is not applicable for

SOLAR predictor is not applicable for T T minmin model.model.

2. Urban morphology predictors:

2. Urban morphology predictors: percentage of pavementpercentage of pavement are

area a oveover r R R 50 50 m m sursurfacface e areareaa (PAVE),(PAVE), average heightaverage height to building area ratio

to building area ratio (HBDG),(HBDG), total wall surface areatotal wall surface area (WALL),

(WALL), GreGreen en PloPlot t RatRatioio (GnPR),(GnPR), sky sky vieview w facfactortor (SVF) and average surface albedo (ALB).

(SVF) and average surface albedo (ALB).

Each set point covers a surface area within a radius of  Each set point covers a surface area within a radius of  50 m. Interpolation is carried out to obtain the estate air 50 m. Interpolation is carried out to obtain the estate air tem

temperperatuature re disdistribtributioution n of of the the estestateate. . By By chachanginging ng thethe urban morphology parameters, STEVE tool can provide urban morphology parameters, STEVE tool can provide the urban ambient temperatures to TAS software as the the urban ambient temperatures to TAS software as the boundary conditions.

boundary conditions. Fo

For r ththee ClimClimate ate PrediPredictorsctors, , ththis is simsimululatation ion ststududy y isis using the weather data on 21st May 2008 from National using the weather data on 21st May 2008 from National

Univ

Universiersity ty of of SinSingapgapore ore (NUS(NUS) ) metmeteoroeorologlogical ical stastatiotion,n, located at the rooftop of Faculty of Engineering, about located at the rooftop of Faculty of Engineering, about 2 km from the Pixel building, with details as follow: 2 km from the Pixel building, with details as follow:

a. a. T T min-rmin-r= 27.24= 27.24°°C.C. b. b. T T avg-ravg-r= 28.97= 28.97°°C.C. c. c. T T max-rmax-r= 31.14= 31.14°°C.C. d. SOLAR

d. SOLARtotaltotal= 5058.39 W/m= 5058.39 W/m22..

e. SOLAR

e. SOLARmaxmax= 764 W/m= 764 W/m22..

  2.4. TAS simulation   2.4. TAS simulation

TAS (

TAS (www.edsl.netwww.edsl.net) ) has the has the capcapabiability of lity of perperforformingming dynamic thermal and it allows the designers to accurately dynamic thermal and it allows the designers to accurately predict the energy consumption. Based on the boundary predict the energy consumption. Based on the boundary conditions calculated by STEVE tool, TAS software are conditions calculated by STEVE tool, TAS software are

Table 1 Table 1

Study cases input values. Study cases input values. Study Study cases cases Surroundings Surroundings greenery (GnPR) greenery (GnPR) Surroundings Surroundings building

buildings s heightheight

Surroundings Surroundings buildings density buildings density Remarks Remarks Base Base Case Case

11..445 5 EExxiissttiinng g EExxiissttiinng g AAccttuuaal l ccoonnddiittiioonn G

GnnPPR R CCaasse e 1 1 1 1 EExxiissttiinng g EExxiissttiinngg C

Caasse e 2 2 2 2 EExxiissttiinng g EExxiissttiinngg C

Caasse e 3 3 3 3 EExxiissttiinng g EExxiissttiinngg C

Caasse e 4 4 4 4 EExxiissttiinng g EExxiissttiinngg H

Heeiigghht t CCaasse e 5 5 EExxiissttiinng g 115 5 m m 111 1 bblloocckkss C

Caasse e 6 6 EExxiissttiinng g 330 0 m m 111 1 bblloocckkss C

Caasse e 7 7 EExxiissttiinng g 445 5 m m 111 1 bblloocckkss C

Caasse e 8 8 EExxiissttiinng g 660 0 m m 111 1 bblloocckkss D

Deennssiitty y CCaasse e 9 9 EExxiissttiinng g 115 5 m m 111 1 bblloocckks s CCaasse e 9 9 iis s tthhe e ssaamme e wwiitth h CCaasse e 55. . IIt t iis s rreepprreesseenntteedd for group comparison

for group comparison C

Caasse e 110 0 EExxiissttiinng g 115 5 m m 114 4 bblloocckkss C

Caasse e 111 E1 Exxiissttiinng g 330 0 m m 111 1 bblloocckks s CCaasse e 111 1 iis s tthhe e ssaamme e wwiitth h CCaasse e 66. . IIt t iis s rreepprreesseenntteedd for group comparison

for group comparison C

Caasse e 112 2 EExxiissttiinng g 330 0 m m 114 4 bblloocckkss Height and

Height and density density

C

Caasse e 113 3 EExxiissttiinng g 115 5 mm,,330 0 mm,,445 5 mm,,660 0 m m 229 9 bblloocckkss C

Caasse e 114 E x4 E xiissttiinng g 115 5 mm,,330 0 mm,,445 5 mm,,660 0 m 2m 29 9 bblloocckks s DDiiffffeerreennt t ccoonnfifigguurraattiioonn C

Caasse e 115 5 EExxiissttiinng g 660 0 m m 229 9 bblloocckkss C

Caasse e 116 6 EExxiissttiinng g 115 5 mm,,330 0 mm,,445 5 mm,,660 0 m m 111 1 bblloocckkss G

GnnPPR R aannd d hheeiigghht t CCaasse e 117 7 4 4 660 0 m m 111 1 bblloocckkss C

Caasse e 118 8 00..6 6 660 0 m m 111 1 bblloocckkss C

Caasse e 119 9 4 4 115 5 m m 111 1 bblloocckkss C

Caasse e 220 0 00..6 6 115 5 m m 111 1 bblloocckkss GnPR and GnPR and density density C Caasse e 221 1 4 4 330 0 m m 2299 C Caasse e 222 2 00..6 6 330 0 m m 2299 C Caasse e 223 3 4 4 330 0 m m 1111 C Caasse e 224 4 00..6 6 330 0 m m 1111 GnPR, height GnPR, height and density and density C Caasse e 225 5 4 4 660 0 m m 2299 C Caasse e 226 6 4 4 115 5 m m 2299 C Caasse e 227 7 00..6 6 660 0 m m 2299 C Caasse e 228 8 00..6 6 660 0 m m 1111 C Caasse e 229 9 4 4 660 0 m m 1111 C Caasse e 330 0 4 4 115 5 m m 1111 C Caasse e 331 1 00..6 6 115 5 m m 2299 C Caasse e 332 2 00..6 6 115 5 m m 1111

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able to generate cooling load and energy consumption able to generate cooling load and energy consumption pre-diction of the PIXEL building.

diction of the PIXEL building. Th

The e bubuildilding ing mamateteririals als ththat at arare e usused ed to to dedevevelolop p ththee PIXEL model are as follows:

PIXEL model are as follows: 1.

1. External wall External wall : brick 200 mm.: brick 200 mm. 2.

2. Internal wall Internal wall : brick 100 mm.: brick 100 mm. 3.

3. Ceiling/floorCeiling/floor: concrete 250 mm.: concrete 250 mm. 4.

4. Ground Ground : concrete 175 mm and soil 1000 mm.: concrete 175 mm and soil 1000 mm. 5.

5. WindowWindow: single blue glass 8 mm.: single blue glass 8 mm. 6.

6. DoorDoor: wood 40 mm.: wood 40 mm.

The boundary conditions used in this TAS simulation The boundary conditions used in this TAS simulation for all of the case studies are as follows:

for all of the case studies are as follows:

1. Air conditioning is on from 08.00 to 22.00 h (extended 1. Air conditioning is on from 08.00 to 22.00 h (extended

office hours). office hours).

2. Thermostat setting: 2. Thermostat setting:

a.

a. TemTemperaperaturture e uppupper er limilimit: t: 2424°°C C anand d lolowewer r limlimit:it:

21 21°°C.C.

b.

b. RH upper limit: 70RH upper limit: 70% and lower limit: 60%% and lower limit: 60%.. 3. Infiltration: 0.3ACH.

3. Infiltration: 0.3ACH.

4. Internal heat load was omitted to get the energy saving 4. Internal heat load was omitted to get the energy saving

that considering the air temperature heat load. that considering the air temperature heat load.

3. Result and discussion 3. Result and discussion

The result data from the STEVE tool and TAS software The result data from the STEVE tool and TAS software have been examined in order to assess the ambient air have been examined in order to assess the ambient air tem-perature around the PIXEL building and its energy perature around the PIXEL building and its energy con-sum

sumptioption n basbased ed on on givgiven en diffdiffereerent nt urburban an mormorphophologlogies.ies. Table 2

Table 2 shows the predictedshows the predicted T T maxmax,, T T avgavg andand T T minmin of theof the

base-case model, which are deviate from the background base-case model, which are deviate from the background

air temperature measured at NUS meteorological station air temperature measured at NUS meteorological station (Met Data) and calculated data from STEVE tool (Base (Met Data) and calculated data from STEVE tool (Base Case). The deviations are mainly due to the urban Case). The deviations are mainly due to the urban mor-pho

phologlogy y conconditdition ion sursurrouroundinding ng the the PixPixel. el. On On the the samsamee day, with the result from the STEVE tool, total cooling day, with the result from the STEVE tool, total cooling load of 4133 kW h was derived from TAS software.

load of 4133 kW h was derived from TAS software. 3.1. Varying the GnPR values

3.1. Varying the GnPR values Fig. 4

Fig. 4 shows comparison between the base case and theshows comparison between the base case and the first four cases. With GnPR ranging from 1 to 4, the Met first four cases. With GnPR ranging from 1 to 4, the Met data is found to have the lowest temperature during the data is found to have the lowest temperature during the day and the hottest temperature during the night compare day and the hottest temperature during the night compare to

to ththe e ototheher r cacaseses. s. HoHowewevever, r, ththe e cacalculculatlated ed dadata ta frfromom STEVE tool are likely found the opposite. The base case STEVE tool are likely found the opposite. The base case calculated data (Base Case) is found to be warmer as calculated data (Base Case) is found to be warmer as com-pared to the cases with more GnPR value (Cases 2, 3, 4). pared to the cases with more GnPR value (Cases 2, 3, 4). The temperature differences are found to be up to 1.3 The temperature differences are found to be up to 1.3°°CC

during the daytime. Furthermore, the calculated result also during the daytime. Furthermore, the calculated result also shows that there is a likely reduction of around 0.20

shows that there is a likely reduction of around 0.20 °°C C onon

the

the T T minmin with the increase of GnPR of 1. A high GnPRwith the increase of GnPR of 1. A high GnPR

means there are more greenery around the vicinity. The means there are more greenery around the vicinity. The greenery provides cooling effect not only from its greenery provides cooling effect not only from its evapo-transpiration process but also from its shading. On every transpiration process but also from its shading. On every GnPR increase of 1, it reduces the SVF value by 0.2 which GnPR increase of 1, it reduces the SVF value by 0.2 which in turn, it reduces the

in turn, it reduces the T T maxmax by 0.29by 0.29 °°C, seeC, see Fig. 5Fig. 5 ((WongWong

and Jusuf, 2010

and Jusuf, 2010).).

Table 2 Table 2

Predicted air temperature and weather data. Predicted air temperature and weather data.

Predicted

PredictedT T maxmax,,T T avgavgandand T T minminof of 

base-case model in the pixel ( base-case model in the pixel (°°C)C)

Weather data on 21st Weather data on 21st May 2008 ( May 2008 (°°C)C) T  T maxmax 3322..883 3 3311..1144 T  T avgavg 2299..556 6 2288..9977 T  T minmin 2266..779 9 2277..2244 Fig. 4.

Fig. 4. Diurnal temperaDiurnal temperature distributiture distributions Case 1–Case 4.ons Case 1–Case 4. Fig. 5.

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Th

Thee inincrcreaeasese inin GnPGnPRR hahass nonotabtablele impimpactact inin cocoololiningg loloadad as shown on

as shown on Fig. 6Fig. 6. There is a reduction of 2–6% in terms of . There is a reduction of 2–6% in terms of  cooling load between the simulated cases based on varying cooling load between the simulated cases based on varying the GnPR, with GnPR value of 4 have the highest cooling the GnPR, with GnPR value of 4 have the highest cooling load reduction of 6% (267 kW h) when compared to base load reduction of 6% (267 kW h) when compared to base case. It a

case. It also canlso can be said tbe said that thehat the introdintroduction ouction off more gremore green- en-ery results in a lower cooling load.

ery results in a lower cooling load.

3.2. Varying the surrounding building height values 3.2. Varying the surrounding building height values

As shown in

As shown in Fig. 7Fig. 7, compared to the Met data, the tem-, compared to the Met data, the tem-peratures become warmer when new building blocks are peratures become warmer when new building blocks are added around the PIXEL building. During the day, the added around the PIXEL building. During the day, the temper

temperature difference could easily ature difference could easily reach 0.84reach 0.84°°C. C. InteresInterest-

t-ingly, the Base Case shows a different result. During the ingly, the Base Case shows a different result. During the hottest hour of the day, additional building tends to lower hottest hour of the day, additional building tends to lower the temperature. Furthermore, as illustrated in

the temperature. Furthermore, as illustrated in Fig. 8Fig. 8, the, the “

“higheshighest t surrousurrounding nding buildbuildingsings”” case (Case 8) is havingcase (Case 8) is having the lowest temper

the lowest temperatuature re as as compcompareared d to to the other the other cascase.e. One possible reason is the increase of surrounding building One possible reason is the increase of surrounding building height reduces the SVF, which provides more shading to its height reduces the SVF, which provides more shading to its surrounding environment (

surrounding environment (Wong and Jusuf, 2010Wong and Jusuf, 2010). If only). If only

building height is being considered, a taller building seems building height is being considered, a taller building seems gives more benefit to its surrounding environment.

gives more benefit to its surrounding environment. In terms of total load (

In terms of total load (Fig. 9Fig. 9), the surrounding building), the surrounding building height seems has positive impact on cooling load reduction. height seems has positive impact on cooling load reduction. There is a reduction of cooling load up to 4.70% in Max There is a reduction of cooling load up to 4.70% in Max Heig

Heightht (60 (60 m)m) whenwhencompcomparedaredtoto thethe BaseBase CaseCase.. InteInterestrestinglingly,y, there is a small difference between 60 m and 45 m (0.2% in there is a small difference between 60 m and 45 m (0.2% in cooling load). A likely reason is once effective shading has cooling load). A likely reason is once effective shading has been

been achiachieveevedd throthroughugh parparticuticularlar surrsurroundoundinging heigheight,ht, thethe fur- fur-ther increase in height only contributes to increased wall ther increase in height only contributes to increased wall sur-face area. Thus, only slightly increase the air temperature, face area. Thus, only slightly increase the air temperature, which in turn have almost the same total cooling load. which in turn have almost the same total cooling load. 3.3. Varying the surrounding building density

3.3. Varying the surrounding building density To further

To further undersunderstand the tand the impact of impact of surrosurrounding build-unding build-ing on the local air temperature condition, Case 9–Case 12 ing on the local air temperature condition, Case 9–Case 12 are being developed and assessed. Similar to the finding on are being developed and assessed. Similar to the finding on pre

previovious us cascases es stustudy dy (Ca(Cases ses 5–85–8), ), by by chachanginging ng the sur-the sur-rounding building condition by mean of increasing its rounding building condition by mean of increasing its den-sity, the temperatures around the PIXEL are significantly sity, the temperatures around the PIXEL are significantly higher during the day as compared to the Met data but higher during the day as compared to the Met data but

4133 4133 4031 4031 39703970 3913 3913 38663866 82 82 163163 220220 267 267 0 0 500 500 1000 1000 1500 1500 2000 2000 2500 2500 3000 3000 3500 3500 4000 4000 4500 4500 b baasse e ccaassee ccaasse e 11 ccaasse e 22 ccaasse e 33 ccaasse e 44     K     K     W     W     h

    h total loadtotal load

total load reduction

total load reduction

Fig. 6. Total load and total load reduction of Case 1–Case 4. Fig. 6. Total load and total load reduction of Case 1–Case 4.

Fig. 7.

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lower as compared to the Base Case, as shown in

lower as compared to the Base Case, as shown in Fig. 10Fig. 10.. Th

The e tetempmperaeratuture re diffdiffererenences ces arare e fofounund d in in ththe e rarangnge e of of  1.1–1.6

1.1–1.6°°C if compared to Met data and up to 0.6C if compared to Met data and up to 0.6 °°C C asas

compared to the Base Case. However, during the night, compared to the Base Case. However, during the night, the

the prepresensence ce of of new new addadditioitional nal buibuildinldings gs tentends ds to to rairaisese the temperature by increasing the pavement surface area the temperature by increasing the pavement surface area and reducing the greenery area, in which increases the and reducing the greenery area, in which increases the ther-ma

mal l cacapapacicity ty ((ChChen en anand d WoWongng, , 20200606). ). AltAlthohougugh, h, ththee increase was found not to significant if only the increase was found not to significant if only the surround-ing buildsurround-ing density are considered. Case with less density ing building density are considered. Case with less density

(Case 9 and Case 11) has the lowest temperature ( (Case 9 and Case 11) has the lowest temperature (T T maxmax,,

T avgavg andand T T minmin) as compared to the other cases with more) as compared to the other cases with more

density combinations. It is clear that increasing the density combinations. It is clear that increasing the sur-rounding buildings density reduces GnPR and likely show rounding buildings density reduces GnPR and likely show a negative impact to the air temperature, but there is a a negative impact to the air temperature, but there is a trend where a lower surrounding building density results trend where a lower surrounding building density results in a lower air temperature (

in a lower air temperature (Fig. 11Fig. 11).).

Case with less surrounding building density (additional Case with less surrounding building density (additional 11 units of surroundings buildings – Case 9 and Case 11) 11 units of surroundings buildings – Case 9 and Case 11) ha

has s poposisititive ve imimpapact ct on on ththe e cocoololining g loload ad up up to to 2.2.7%7% (112 kW h) as compared to the Base

(112 kW h) as compared to the Base Case, in terms of cool-Case, in terms of cool-ing load reduction, as shown in

ing load reduction, as shown in Fig. 12Fig. 12. The figure also. The figure also illustrates that more density (additional 14 units of illustrates that more density (additional 14 units of sur-rounding buildings – Cases 10 and 12) has lesser impact rounding buildings – Cases 10 and 12) has lesser impact on the cooling load in terms of reduction. The negative on the cooling load in terms of reduction. The negative value, however, shows that at more density condition (Case value, however, shows that at more density condition (Case 10), the cooling load is higher than the Base Case. The 10), the cooling load is higher than the Base Case. The notable increase, nonetheless, is only 0.6% (29 kW h) as notable increase, nonetheless, is only 0.6% (29 kW h) as compared to the Base Case. Only after the surrounding compared to the Base Case. Only after the surrounding buildings heights are increased, the benefit of surrounding buildings heights are increased, the benefit of surrounding buildings can be spotted. As found on earlier cases, buildings can be spotted. As found on earlier cases, sur-ro

rounundiding ng bubuilildindings gs hehelp lp rereduducicing ng ththe e cocoolioling ng loload ad byby increasing the wall and surface area (PAVE) and increasing the wall and surface area (PAVE) and decreas-ing the SVF, thus increasdecreas-ing the shadowdecreas-ing effect.

ing the SVF, thus increasing the shadowing effect.

4133 4133 40664066 4021 4021 3 3994477 33993399 67 67 112112 186186 194 194 0 0 500 500 1000 1000 1500 1500 2000 2000 2500 2500 3000 3000 3500 3500 4000 4000 4500 4500 b baasse e ccaassee ccaasse e 55 ccaasse e 66 ccaasse e 77 ccaasse e 88     K     K     W     W     h

    h total loadtotal load

total load reduction

total load reduction

Fig. 9.

Fig. 9. Total load and total load reductiTotal load and total load reduction of Case 5–Case 8.on of Case 5–Case 8.

Fig. 10.

Fig. 10. Diurnal temperaDiurnal temperature distributiture distributions Case 9–Case 12.ons Case 9–Case 12. Fig. 8.

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3.4. Varying combination of surrounding building height and  3.4. Varying combination of surrounding building height and  density

density

The first combination case being studied is the The first combination case being studied is the combina-tion of surrounding building height and density. Buildings tion of surrounding building height and density. Buildings have always related to increase in

have always related to increase in wall area, pavement areawall area, pavement area (PAVE) and decrease in SVF. As expected, the (PAVE) and decrease in SVF. As expected, the

combina-tion of two parameters related to the surrounding buildings tion of two parameters related to the surrounding buildings is found warmer than the Met data and cooler than the is found warmer than the Met data and cooler than the cal-culated data (Base Case) during the day (

culated data (Base Case) during the day (Fig. 13Fig. 13). Com-). Com-pared to Base Case, the temperature different are in the pared to Base Case, the temperature different are in the range of 0.7–0.9

range of 0.7–0.9°°C. Looking atC. Looking at Fig. 14Fig. 14, even though the, even though the

difference is not too obvious, the graphs suggest that the difference is not too obvious, the graphs suggest that the combination of max HEIGHT and max DENSITY has combination of max HEIGHT and max DENSITY has the lowest

the lowest T T maxmax andand T T avgavg, which is likely to happen during, which is likely to happen during

the day. Possible reason is that there is a decrease in the the day. Possible reason is that there is a decrease in the SVF, which means, it increases the shading area.

SVF, which means, it increases the shading area. As shown in

As shown in Fig. 15Fig. 15, the surrounding building height, the surrounding building height and density have positive impact on the cooling load in and density have positive impact on the cooling load in terms of total load reduction. The graph also shows that terms of total load reduction. The graph also shows that there is a reduction of cooling load up to 4.76% in Height there is a reduction of cooling load up to 4.76% in Height of 15–60 M + Max Density (Case 14) as compared to the of 15–60 M + Max Density (Case 14) as compared to the base case.

base case.

3.5. Varying greenery density (GnPR) and surrounding  3.5. Varying greenery density (GnPR) and surrounding  building height (HEIGHT)

building height (HEIGHT) Figs. 16 and 17

Figs. 16 and 17show the temperature distribution basedshow the temperature distribution based on the combination of modified GnPR and surrounding on the combination of modified GnPR and surrounding 4133 4133 40664066 41624162 4021 4021 40854085 67 67 4848 -29 -29 112 112 -500 -500 0 0 500 500 1000 1000 1500 1500 2000 2000 2500 2500 3000 3000 3500 3500 4000 4000 4500 4500 b baasse e ccaassee ccaasse e 99 ccaasse e 1100 ccaasse e 1111 ccaasse e 1122     K     K     W     W     h

    h total loadtotal load

total load reduction

total load reduction

Fig. 12. Total load and total load reduction of Case 9–Case 12. Fig. 12. Total load and total load reduction of Case 9–Case 12.

Fig. 13.

Fig. 13. Diurnal temperDiurnal temperature distributature distributions Case 13–Case 16.ions Case 13–Case 16. Fig. 11.

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building height. The graph shows that the combination of  building height. The graph shows that the combination of  max GnPR and max HEIGHT have the lowest

max GnPR and max HEIGHT have the lowest T T maxmax andand

T minmin. However, the lowest. However, the lowest T T avgavg is found at the case withis found at the case with

th

thee cocombmbininatiationon ofof mamaxx GnGnPRPR anandd minmin HEHEIGIGHT.HT. ThThee tetem- m-peratu

perature differences, comparere differences, compared d to the to the Base Case, are Base Case, are foundfound up to 1.2

up to 1.2°°C. The C. The possibpossible reason is le reason is the increase of buildingthe increase of building

height reduces the SVF (shading effect). Once the canyon is height reduces the SVF (shading effect). Once the canyon is completely shaded by the surrounded buildings, increasing completely shaded by the surrounded buildings, increasing the HEIGHT will not give any positive impact. The the HEIGHT will not give any positive impact. The T T avgavg

starts to increase due to the increase of Wall areas. The high starts to increase due to the increase of Wall areas. The high GnPR, however, helps to balance the negative impact. GnPR, however, helps to balance the negative impact.

Between the Case 17 and Case 19, where the GnPR is at Between the Case 17 and Case 19, where the GnPR is at the maximum and the HEIGHT varies from maximum to the maximum and the HEIGHT varies from maximum to minimum, there is no significant change in all the minimum, there is no significant change in all the temper-atures. Hence, it can be said that the main governing factor atures. Hence, it can be said that the main governing factor fo

for r ththe e rereduductction ion of of tetempmperaeratuture re is is ststill ill ththe e grgreeeeneneryry (GnPR).

(GnPR). Fig. 18

Fig. 18 shows the total load of combination of GnPRshows the total load of combination of GnPR and HEIGHT. Basically, the graph suggests that GnPR and HEIGHT. Basically, the graph suggests that GnPR tends to give a positive impact and HEIGHT gives a tends to give a positive impact and HEIGHT gives a neg-ative impact in terms of total cooling reduction. Regardless ative impact in terms of total cooling reduction. Regardless the HEIGTH variation, Case 17 and Case 19, which have the HEIGTH variation, Case 17 and Case 19, which have max GnPR, show slightly more than 10% (428 kW h and max GnPR, show slightly more than 10% (428 kW h and 443 kW h respectively) total cooling load reduction. 443 kW h respectively) total cooling load reduction. How-ever, further investigation shows that max HEIGHT seems ever, further investigation shows that max HEIGHT seems to have more benefit than min HEIGHT as shown in Case to have more benefit than min HEIGHT as shown in Case 18 and Case 20.

18 and Case 20.

3.6. Varying greenery density (GnPR) and surrounding  3.6. Varying greenery density (GnPR) and surrounding  building density (DENSITY)

building density (DENSITY) Figs. 19 and 20

Figs. 19 and 20 show the diurnal temperature, the max,show the diurnal temperature, the max, average and min temperature based on modifying GnPR average and min temperature based on modifying GnPR and DENSITY values, the temperature distribution seems and DENSITY values, the temperature distribution seems to have similar trend to the previous cases when the GnPR to have similar trend to the previous cases when the GnPR

Fig. 16.

Fig. 16. Diurnal temperaDiurnal temperature distributture distributions Case 17–Case 20.ions Case 17–Case 20. Fig. 14.

Fig. 14. T T maxmax,, T T avgavgandandT T minminof Case 13–Case 16.of Case 13–Case 16.

4133 4133 3936 3936 39713971 40054005 39623962 197 197 162162 128128 171171 0 0 500 500 1000 1000 1500 1500 2000 2000 2500 2500 3000 3000 3500 3500 4000 4000 4500 4500 b baasse e ccaassee ccaasse e 1133 ccaasse e 1144 ccaasse e 1155 ccaasse e 1166     K     K     W     W     h

    h total loadtotal load

total load reduction

total load reduction

Fig. 15.

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and HEIGHT are combined. The lowest temperatures are and HEIGHT are combined. The lowest temperatures are found when max GnPR is combined with min DENSITY, found when max GnPR is combined with min DENSITY, with temperature difference up to 1.2

with temperature difference up to 1.2 °°C compared to theC compared to the

Base Case, during daytime. The result supports earlier Base Case, during daytime. The result supports earlier find-ing that greenery give positive impact to the environment ing that greenery give positive impact to the environment due to its shading and DENSITY, which related to due to its shading and DENSITY, which related to build-ing pavement area, gives negative impact due to increase ing pavement area, gives negative impact due to increase of wall surface area and pavement.

of wall surface area and pavement.

Similar to the HEIGHT, the change in DENSITY seems Similar to the HEIGHT, the change in DENSITY seems to

to shshow ow a a nenegagatitive ve imimpapactct. . BuBut, t, it it is is babalalancnced ed by by ththee

GnPR. The increase of building density mainly contributes GnPR. The increase of building density mainly contributes to the

to the T T maxmax. The results further suggest that GnPR is still. The results further suggest that GnPR is still

the main governing factor to moderate the temperature. the main governing factor to moderate the temperature.

In terms of total load and energy consumption, the In terms of total load and energy consumption, the com-binatio

bination of n of GnPR and DENSITY study shows a GnPR and DENSITY study shows a reducreductiontion on the cooling load (

on the cooling load (Fig. 21Fig. 21). There is a reduction of cool-). There is a reduction of cool-ing load up to 10.74% in Max GnPR and Min Density as ing load up to 10.74% in Max GnPR and Min Density as co

compmparared ed to to ththe e babase se cacasese. . ThThe e grgrapaph h alsalso o shshowows s ththatat Gn

GnPR PR hahas s ththe e momost st ininflufluencence e in in teterms rms of of cocoolioling ng loloadad reduct

reduction as ion as compacompared to red to DENSITY. The min DENSITY. The min DENSITYDENSITY,, however, is found giving more positive impact than the however, is found giving more positive impact than the max DENSITY.

max DENSITY.

3.7. Varying all three variable together; GnPR, HEIGHT  3.7. Varying all three variable together; GnPR, HEIGHT  and DENSITY 

and DENSITY  Fi

Fig. g. 2222 shoshows ws the the pospossibsible le comcombinbinatioation n of of varvaryinyingg GnPR, HEIGHT and DENSITY. Examining the diurnal GnPR, HEIGHT and DENSITY. Examining the diurnal graph

graph, , almost all almost all combincombination cases have ation cases have a a similar patternsimilar pattern.. With the

With the modificmodification of ation of the surroundinthe surrounding g conditcondition, whichion, which is governed by the combination of different variables, tends is governed by the combination of different variables, tends to give a negative impact to the environment. As found to give a negative impact to the environment. As found fro

from m the other the other comcombinbinatioation n cascases, es, as as comcomparpared ed to to thethe 4133 4133 3705 3705 3972 3972 3690 3690 4142 4142 428 428 161 161 443 443 -9 -9 -500 -500 0 0 500 500 1000 1000 1500 1500 2000 2000 2500 2500 3000 3000 3500 3500 4000 4000 4500 4500 b baasse e ccaassee ccaasse e 1177 ccaasse e 1188 ccaasse e 1199 ccaasse e 2200     K     K     W     W     h

    h total loadtotal load

total load reduction

total load reduction

Fig. 18. Total load and total load reduction of Case 17–Case 20. Fig. 18. Total load and total load reduction of Case 17–Case 20.

Fig. 19.

Fig. 19. Diurnal temperDiurnal temperature distributature distributions Case 21–Case 24.ions Case 21–Case 24. Fig. 17.

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Met data, all of the comparison cases are found slightly Met data, all of the comparison cases are found slightly warmer during the day and cooler during the night. warmer during the day and cooler during the night. How-ever, as compared to the Base Case, most of the cases are ever, as compared to the Base Case, most of the cases are found cooler up to 1.1

found cooler up to 1.1°°C during daytime. However, casesC during daytime. However, cases

with dense greenery (max GnPR) and surrounded by more with dense greenery (max GnPR) and surrounded by more building (max DENSITY) – Case 25 and Case 26; tend to building (max DENSITY) – Case 25 and Case 26; tend to have a lower temperature during the day and warmer have a lower temperature during the day and warmer dur-ing the night as compared to the other cases. Both of these ing the night as compared to the other cases. Both of these par

parameameterters s likelikely ly havhave e the most the most impaimpact ct on on the diurnathe diurnall temperature distribution. Interestingly, other cases (Cases temperature distribution. Interestingly, other cases (Cases

27–

27–30)30), , with with othother er pospossiblsible e comcombinbinatioations, ns, do do not not shoshoww any differences on the graph. Even, Case with max GnPR any differences on the graph. Even, Case with max GnPR combined with min DENSITY and max HEIGHT (Case combined with min DENSITY and max HEIGHT (Case 29

29) ) or or mamax x GnGnPR, min PR, min DEDENSNSITITY Y anand d min HEIGmin HEIGHTHT (Ca

(Case se 30) show a 30) show a simisimilar diurnlar diurnal al trentrend d as as comcomparpared ed toto cases with min GnPR (Cases 27, 28, 31 and 32).

cases with min GnPR (Cases 27, 28, 31 and 32). Fig. 23

Fig. 23 shows the daily average of each case in term of shows the daily average of each case in term of  T 

T maxmax,, T T avgavg andand T T minmin. The graph illustrates that cases with. The graph illustrates that cases with

de

densnsee grgreeeeneneryry (C(Casaseses 2525,, 2626,, 2929 anandd 3030)) tetendnd toto hahaveve aa lolowewerr te

tempmperaeratuturere asas cocompmparareded toto ototheherr cacaseses.s. InIn sosomeme exextetent,nt, susur- r-rounding building density and height might have positive rounding building density and height might have positive impact to the ambient temperature due its shading effect, impact to the ambient temperature due its shading effect, but this effect is very limited and not too obvious. Case 32 but this effect is very limited and not too obvious. Case 32 with sparse greenery and surrounded by high-density with sparse greenery and surrounded by high-density build-ings are found to be the hottest among all. Interestingly, ings are found to be the hottest among all. Interestingly, when dense greenery is introduced (Case 25), the ambient when dense greenery is introduced (Case 25), the ambient te

tempmperaeratuturere fofounundd toto bebe lolowewer.r. ThThisis reresusultlt coconfinfirmsrms ththee finfind- d-ing by

ing by Chen and Wong (2006)Chen and Wong (2006) that greenery can help miti-that greenery can help miti-gate the temperature in urban area.

gate the temperature in urban area. Fig. 20Fig. 20 also illustratesalso illustrates that the relationship between the variables shows the that the relationship between the variables shows the tem-perature is strongly influenced by the GnPR as compared perature is strongly influenced by the GnPR as compared to other two

to other two parameparameters (HEIGTH + ters (HEIGTH + DENSITY)DENSITY)..

4133 4133 3725 3725 4118 4118 3689 3689 4069 4069 408 408 15 15 444 444 64 64 0 0 500 500 1000 1000 1500 1500 2000 2000 2500 2500 3000 3000 3500 3500 4000 4000 4500 4500 b baasse e ccaassee ccaasse e 2211 ccaasse e 2222 ccaasse e 2233 ccaasse e 2244     K     K     W     W     h

    h total loadtotal load

total load reduction

total load reduction

Fig. 21.

Fig. 21. Total load and total load reductiTotal load and total load reduction of Case 21–Case 24.on of Case 21–Case 24.

Fig. 22.

Fig. 22. Diurnal temperaDiurnal temperature distributture distributions Case 25–Case 32.ions Case 25–Case 32. Fig. 20.

(15)

Fig

Fig..2424showshowss thethe coocoolingloadlingload andand thethe potpotententialial ofof savsavingingss of each combination of GnPR, DENSITY and HEIGT of each combination of GnPR, DENSITY and HEIGT cases as compared to the base case. Interestingly, cases with cases as compared to the base case. Interestingly, cases with max GnPR (Cases 25, 26, 29 and 30) tend to have a lower max GnPR (Cases 25, 26, 29 and 30) tend to have a lower cooling load. As compared to the base case, cases with cooling load. As compared to the base case, cases with max GnPR are able to reduce the total load from 3.60% max GnPR are able to reduce the total load from 3.60% (35

(357 k7 kW hW h)) upup toto 4.44.40%0% (44(441 k1 kW hW h).). TheThe nexnextt govgovernerninging var var--ia

iablblee ththatat isis liklikelyely hahass aa poposisitivtivee impimpactact inin lolowerweriningg ththee cocool ol--ing load is HEIGHT. Case 27 and Case 28, which have ing load is HEIGHT. Case 27 and Case 28, which have similar max HEIGHT, are able to reduce the cooling load similar max HEIGHT, are able to reduce the cooling load up to 0.50% (72 kW h) and 0.77% (104 kW h) respectively. up to 0.50% (72 kW h) and 0.77% (104 kW h) respectively. Lo

Lookokining g at at ththe e ototheher r cacaseses, s, whwhicich h hahave ve sisimimilalar r mamaxx HEIGHT (Case 25 and Case 29), the combination with HEIGHT (Case 25 and Case 29), the combination with ma

maxx GnGnPRPR shshowowss momorere popositsitivivee imimpapactscts inin tetermsrms ofof sasavivingngs.s. Howeve

However, the r, the max GnPR cases will max GnPR cases will have the highest reduc-have the highest reduc-tion in terms of total cooling load, if the HEIGHT is at the tion in terms of total cooling load, if the HEIGHT is at the minimu

minimum m (Case 26 and (Case 26 and Case 30). The Case 30). The graph also illustratgraph also illustrateses that, DENSITY has the least significant impact in lowering that, DENSITY has the least significant impact in lowering the total cooling load. The DENSITY, however, further the total cooling load. The DENSITY, however, further increases the cooling load due to the increase of its value. increases the cooling load due to the increase of its value. 4.

4. ConclusConclusionsions

Urban areas have a large variety of forms and surface Urban areas have a large variety of forms and surface characteristic. Basically, the microclimate of these areas is characteristic. Basically, the microclimate of these areas is influenced by several urban elements, such as the urban influenced by several urban elements, such as the urban geometry, the greenery, and the properties of surfaces. It geometry, the greenery, and the properties of surfaces. It

is clear that urban area without a proper use of these is clear that urban area without a proper use of these ele-men

ments ts is is likelikely ly concontritributbuted ed to to disdiscomcomforfort t and and incinconvonve- e-nience to the people.

nience to the people.

Based on the study, the paper conclude a few essential Based on the study, the paper conclude a few essential things, as follows:

things, as follows: 1.

1. UHI is a UHI is a growing concgrowing concern globallyern globally, requiring immediat, requiring immediatee address at all levels (macro and micro).

address at all levels (macro and micro).

2. Urban morphology has a strong role in determining the 2. Urban morphology has a strong role in determining the variations that you can have in the temperature at the variations that you can have in the temperature at the micro

micro level level (micro(microclimate)climate).. 3. V

3. Varariaiablebles s susuch ch as as GnGnPRPR, , HEIHEIGHT GHT anand d DENDENSISITYTY show a high degree of impact in altering the temperature show a high degree of impact in altering the temperature or microclimate of any location.

or microclimate of any location.

4. The degree of impact on the air temperature can be up 4. The degree of impact on the air temperature can be up

0.9–1.2 0.9–1.2°°CC

5. Each of the identified variables has a varying degree of  5. Each of the identified variables has a varying degree of 

imp

impactact. . The higheThe highest st impimpact is act is GnPGnPR R due to due to shashadindingg effect of trees followed by HEIGHT and DENSITY. effect of trees followed by HEIGHT and DENSITY. 6.

6. The effect of GnPR overridThe effect of GnPR overrides the effect of the other vari-es the effect of the other vari-ables – height and density in all the identified cases, ables – height and density in all the identified cases, pr

provovining g ththe e imimpoportartancnce e of of grgreeneenerery y in in altaltererining g ththee microclimate condition.

microclimate condition.

7. The cooling load reduction due to the impact of the 7. The cooling load reduction due to the impact of the

vari-ables is in the range of 5–10% if addressed effectively ables is in the range of 5–10% if addressed effectively 8. These savings are achieved by only altering the urban 8. These savings are achieved by only altering the urban

morphology. morphology.

Fig. 23.

Fig. 23. T T maxmax,,T T avgavgandandT T minminof Case 25–Case 32.of Case 25–Case 32.

4133 4133 3693 3693 4061 4061 40294029 3707 3707 36923692 4233 4233 4146 4146 3776 3776 104 104 72 72 -13 -13 -100 -100 441 441 426 426 440 440 357 357 -500 -500 0 0 500 500 1000 1000 1500 1500 2000 2000 2500 2500 3000 3000 3500 3500 4000 4000 4500 4500 base base case case c a c as e s e 2 52 5 c ac as e s e 2 62 6 c ac as e s e 2 72 7 c ac as e s e 2 82 8 c ac as e s e 2 92 9 c ac as e s e 3 03 0 c ac as e s e 3 13 1 c ac as e s e 3 23 2     K     K     W     W     h

    h total loadtotal load

total load reduction

total load reduction

Fig. 24. Total load and total load reduction of Case 25–Case 32. Fig. 24. Total load and total load reduction of Case 25–Case 32.

Figure

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