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 morphologyon building energy consumption

Nyuk Hien Wong a, Steve Kardinal Jusuf b,⇑, Nedyomukti Imam Syafii c, Yixing Chen a,Norwin Hajadi a, Haripriya Sathyanarayanan a, Yamini Vidya Manickavasagam a

a Department of Building, National University of Singapore, Singaporeb Center for Sustainable Asian Cities, National University of Singapore, Singapore

c Institute of High Performance Computing, Singapore

Received 14 June 2010; received in revised form 30 October 2010; accepted 1 November 2010Available online 3 December 2010

Communicated by: Associate Editor Matheos Santamouris

Abstract

Empirical models of minimum (Tmin), average (Tavg) and maximum (Tmax) air temperature for Singapore estate have been developedand validated based on a long-tem field measurement. There are three major urban elements, which influence the urban temperature atthe local scale. Essentially, they are buildings, greenery and pavement. Other related parameters identified for the study, such as greenplot ratio (GnPR), sky view factor (SVF), surrounding building density, the wall surface area, pavement area, albedo are also evaluatedto give a better understanding on the likely impact of the modified urban morphology on energy consumption.

The objective of this research is to assess and to compare how the air temperature variation of urban condition can affect the buildingenergy consumption in tropical climate of Singapore. In order to achieve this goal, a series of numerical calculation and building simulationare utilized. A total of 32 cases, considering different urban morphologies, are identified and evaluated to give better a understanding on theimplication 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 upto 2 �C. The results also strongly indicate an energy saving of 4.5% if the urban elements are addressed effectively.� 2010 Elsevier Ltd. All rights reserved.

Keywords: Impact; Urban morphology; Building energy consumption; Energy simulation; Singapore

1. Introduction

Urbanization in the recent years has significantlyincreased the necessity for the city to further develop itselfto accommodate the incoming inhabitants, in which ame-liorate the urban microclimate. The development may leadto the increase of urban heat island (UHI) intensity, a phe-nomenon which air temperature in densely built urban area

are higher than the temperature of the surrounding ruralarea. Higher urban temperature has a serious impact onthe electricity demand for air conditioning of buildings, italso increase the smog production, while contributing toincreased emission of pollutants from power plants, includ-ing carbon dioxide, sulfur dioxide, nitrous oxides and othersuspended particulates. UHI, however, could be found inevery town and city all over the world (Oke and Eas,1971; Landsberg, 1981; Padmanabhamurty, 1990/1991;Sani, 1990/1991; Eliasson, 1996; Giridharan et al., 2007).The heat island intensity could easily reach up to 10 �C,which has been observed in India. Furthermore, it is clearthat this increase of urban air temperature will alsoincrease the energy consumption by increasing the cooling

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

doi:10.1016/j.solener.2010.11.002

⇑ Corresponding author. Address: Center for Sustainable Asian Cities,National University of Singapore, 4 Architecture Drive, Singapore117566, Singapore. Tel.: +65 6516 4691.

E-mail address: [email protected] (S.K. Jusuf).

www.elsevier.com/locate/solener

Available online at www.sciencedirect.com

Solar Energy 85 (2011) 57–71


load. In city of Athens, where the mean heat island inten-sity are found exceeding 10 �C, the cooling load of buildingin the urban area found to be doubled, the peak electricityload for cooling may be tripled because of the higher ambi-ent temperatures (Santamauris et al., 2001). Study by Kol-okotroni et al. (2005) on London UHI found that duringtypical hot week the rural reference office has 84% energydemand for cooling as compared to a similar urban officebased in the same location. Another study utilizing mea-sured air temperature data and building energy simulationat 24 different locations within London UHI found thaturban cooling load is up to 25% higher than the rural loadover the year; however the annual heating load is reducedby 22% (Kolokotroni et al., 2007). Other urban modifica-tions, nevertheless, also have been found altering the tem-perature in urban area and yet modify the energyconsumption. Study in USA (Akbari et al., 1992) foundthat large number of trees and urban parks able to reducelocal air temperature by 0.5–5 �C. Each 1 �C drop in airtemperature could lower the peak electric demand for cool-ing by 2–4%. Hence, urban elements such as trees or parkshave significant impact and energy consumption. Shashua-Bar et al. (2010) studied the variables that influence the var-iability of air temperature in urban streets that includetrees, building configuration, albedo of the surroundingsand street ventilation. Some studies also investigated theselection of building materials which influences not onlythe outdoor space but also the building energy consump-tion (Taha, 1997; Akbari et al., 2001; Doulos et al.,2004). These variables are in the control of the urban plan-ners and architects. Hence, the urban air temperature canbe attenuated with a proper design and modeling.

In Singapore, with the present trend of having buildinggoing higher and closer to one another, as well as the exten-sive usage of air conditioning, UHI are likely to occur(Wong and Chen, 2009; Jusuf et al., 2007). The tempera-ture increases in urban area can lead to significant use ofair conditioning. UHI studies in Singapore shows a possi-ble increase of urban air temperature of 1 �C. If the trendkeep on continue, within 50 year, the energy consumptionfor cooling will increase in order of 33 GW h per annum

for the whole island (Tso, 1994). More recent study foundthat outdoor air temperature determines the energy savingsof buildings. According to some studies, every 1 �C of out-door air temperature reduction saves 5% of building energyconsumption (Chen and Wong, 2006; Wong et al., 2009).

It becomes increasingly important to study urban micro-climatic environments and to apply these findings toimprove the people’s comfort and to decrease the energyconsumption in the urban areas. The present paper illus-trates the result of an urban study carried out in Singaporeaiming to assess and discuss the impact of the urban mor-phology on the energy consumption of The PIXEL at BuonaVista. Computer simulation software, TAS and Singaporeurban air temperature prediction model, STEVE tool areutilized to evaluate the impact of increased ambient temper-ature on the cooling performance of building (see Fig. 1).

2. Methodologies

2.1. Object of study

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

2.2. The case studies

A base case (Fig. 2) and 32 cases (Fig. 3) were employedto have better understanding on the impact of different sur-rounding urban morphologies to the energy consumptionof the building. These various cases will address the opti-mum solution between greenery, building area and buildingdensity to address the impact of the temperature on energyconsumption of the PIXEL building. Table 1 shows thedifferent parameters used as the case studies.

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

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

58 N.H. Wong et al. / Solar Energy 85 (2011) 57–71


the STEVE tool. As mentioned, STEVE tool predicts theair temperature of a point based on the 50 m radius. Asto cover up the area of study, four circles of 50 m radiuswere developed for the STEVE model. Named as A, B, Cand D, each of these circles will have its own calculation,see Fig. 2. The final predicted air temperature, which isthe average of four predicted air temperature (A, B, Cand D), will be used in the TAS software as the boundarycondition to develop cooling load and energy consumptiondemand of the building.

Given the same methodology as the Base Case, the first12 Cases (Case 1–Case 12) are trying to find out the role ofthe individual parameter on the urban air temperature andthe building cooling load. These basic parameters are thesurrounding greenery, quantified as Green Plot Ratio –GnPR (Case 1–Case 4), the surrounding building height– HEIGHT (Case 5–Case 8) and surrounding building den-sity – DENSITY (Case 9–Case 12). The next 12 cases (Case13–Case 24) are basically the combination of two of theseparameters. Combinations are worked out based on uni-form, random and stratum effects. Combination ofHEIGHT and DENSITY, for example, has impacts onthe greenery and sky view factors (SVF). The last eightcases (Case 25–Case 32), are combination of three of them.These eight cases of possible combination were having

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

2.3. STEVE tool

Screening Tool for Estate Environment Evaluation,STEVE tool (Jusuf and Wong, 2009), is a web based appli-cation that is specific to an estate and it calculates the Tmax,Tavg and Tmin of a point interest of an estate. A set of threeequations shown in Eq. (1) gives the correlation betweenthe urban morphology parameters (building, pavementand greenery) and estate air temperature. These predictionmodels were based on the empirical data collected over aperiod of close to 3 years as part of the development ofan assessment method to evaluate the impact of estatedevelopment (in this case, NUS Kent Ridge Campus andOne North), which includes the assessment method ofexisting greenery condition (Wong and Jusuf, 2008a) andgreenery condition for a proposed master plan in an estatedevelopment (Wong and Jusuf, 2008b). The greeneryassessment used Green Plot Ratio (GnPR) method. TheGnPR is derived from the average of greenery on a lot,using the leaf area index (LAI), in proportion to the totallot area (Ong, 2003). The higher the GnPR value, the den-ser the greenery condition in a built environment.

Fig. 2. Base Case. Note: Pavement (Pave); Green Plot Ratio (GnPR); Average Height (Avg Ht); building (BDG); sky view factor (SVF).

N.H. Wong et al. / Solar Energy 85 (2011) 57–71 59


T min ð�CÞ ¼ 4:061þ 0:839 Ref T min ð�CÞ þ 0:004 PAVE ð%Þ � 0:193 GnPR� 0:029 HBDG þ 1:339E� 06 WALL ðm2ÞT avg ð�CÞ ¼ 2:347þ 0:904 Ref T avg ð�CÞ þ 5:786E� 05 SOLARtotal ðW=m2Þ þ 0:007 PAVE ð%Þ

� 0:06 GnPR� 0:015 HBDG þ 1:311 E� 05WALL ðm2Þ þ 0:633 SVF

T max ð�CÞ ¼ 7:542þ 0:684 Ref T max ð�CÞ þ 0:003 SOLARmax ðW=m2Þ þ 0:005 PAVE ð%Þ� 0:016 HBDG þ 6:777E� 06 WALL ðm2Þ þ 1:467 SVFþ 1:466 ALB ð1Þ

Fig. 3. 32 Study cases.



Daily minimum (Tmin), average (Tavg) and maximum(Tmax) temperature of each point of measurements werecalculated as dependent variable of the air temperature pre-diction model. The independent variables of the models canbe categorized into:

1. Climate predictors: daily minimum (Tmin-r), average(Tavg-r) and maximum (Tmax-r) temperature at meteoro-logical station; average of daily solar radiation

(SOLAR). For the SOLAR predictor, average of dailysolar radiation total (SOLARtotal) was used in Tavg

Fig. 3 (continued)



models, while average of solar radiation maximum ofthe day (SOLARmax) was used in the Tmax model.SOLAR predictor is not applicable for Tmin model.

2. Urban morphology predictors: percentage of pavement

area over R 50 m surface area (PAVE), average height

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

(WALL), Green Plot Ratio (GnPR), sky view factor

(SVF) and average surface albedo (ALB).

Each set point covers a surface area within a radius of50 m. Interpolation is carried out to obtain the estate airtemperature distribution of the estate. By changing theurban morphology parameters, STEVE tool can providethe urban ambient temperatures to TAS software as theboundary conditions.

For the Climate Predictors, this simulation study isusing the weather data on 21st May 2008 from National

University of Singapore (NUS) meteorological station,located at the rooftop of Faculty of Engineering, about2 km from the Pixel building, with details as follow:

a. Tmin-r = 27.24 �C.b. Tavg-r = 28.97 �C.c. Tmax-r = 31.14 �C.d. SOLARtotal = 5058.39 W/m2.e. SOLARmax = 764 W/m2.

2.4. TAS simulation

TAS (www.edsl.net) has the capability of performingdynamic thermal and it allows the designers to accuratelypredict the energy consumption. Based on the boundaryconditions calculated by STEVE tool, TAS software are

Table 1Study cases input values.

Studycases

Surroundingsgreenery (GnPR)

Surroundingsbuildings height

Surroundingsbuildings density

Remarks

BaseCase

1.45 Existing Existing Actual condition

GnPR Case 1 1 Existing ExistingCase 2 2 Existing ExistingCase 3 3 Existing ExistingCase 4 4 Existing Existing

Height Case 5 Existing 15 m 11 blocksCase 6 Existing 30 m 11 blocksCase 7 Existing 45 m 11 blocksCase 8 Existing 60 m 11 blocks

Density Case 9 Existing 15 m 11 blocks Case 9 is the same with Case 5. It is representedfor group comparison

Case 10 Existing 15 m 14 blocksCase 11 Existing 30 m 11 blocks Case 11 is the same with Case 6. It is represented

for group comparisonCase 12 Existing 30 m 14 blocks

Height anddensity

Case 13 Existing 15 m,30 m,45 m,60 m 29 blocks

Case 14 Existing 15 m,30 m,45 m,60 m 29 blocks Different configurationCase 15 Existing 60 m 29 blocksCase 16 Existing 15 m,30 m,45 m,60 m 11 blocks

GnPR and height Case 17 4 60 m 11 blocksCase 18 0.6 60 m 11 blocksCase 19 4 15 m 11 blocksCase 20 0.6 15 m 11 blocks

GnPR anddensity

Case 21 4 30 m 29

Case 22 0.6 30 m 29Case 23 4 30 m 11Case 24 0.6 30 m 11

GnPR, heightand density

Case 25 4 60 m 29

Case 26 4 15 m 29Case 27 0.6 60 m 29Case 28 0.6 60 m 11Case 29 4 60 m 11Case 30 4 15 m 11Case 31 0.6 15 m 29Case 32 0.6 15 m 11



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

The building materials that are used to develop thePIXEL model are as follows:

1. External wall: brick 200 mm.2. Internal wall: brick 100 mm.3. Ceiling/floor: concrete 250 mm.4. Ground: concrete 175 mm and soil 1000 mm.5. Window: single blue glass 8 mm.6. Door: wood 40 mm.

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

1. Air conditioning is on from 08.00 to 22.00 h (extendedoffice hours).

2. Thermostat setting:a. Temperature upper limit: 24 �C and lower limit:

21 �C.b. RH upper limit: 70% and lower limit: 60%.

3. Infiltration: 0.3ACH.4. Internal heat load was omitted to get the energy saving

that considering the air temperature heat load.

3. Result and discussion

The result data from the STEVE tool and TAS softwarehave been examined in order to assess the ambient air tem-perature around the PIXEL building and its energy con-sumption based on given different urban morphologies.Table 2 shows the predicted Tmax, Tavg and Tmin of thebase-case model, which are deviate from the background

air temperature measured at NUS meteorological station(Met Data) and calculated data from STEVE tool (BaseCase). The deviations are mainly due to the urban mor-phology condition surrounding the Pixel. On the sameday, with the result from the STEVE tool, total coolingload of 4133 kW h was derived from TAS software.

3.1. Varying the GnPR values

Fig. 4 shows comparison between the base case and thefirst four cases. With GnPR ranging from 1 to 4, the Metdata is found to have the lowest temperature during theday and the hottest temperature during the night compareto the other cases. However, the calculated data fromSTEVE tool are likely found the opposite. The base casecalculated data (Base Case) is found to be warmer as com-pared to the cases with more GnPR value (Cases 2, 3, 4).The temperature differences are found to be up to 1.3 �Cduring the daytime. Furthermore, the calculated result alsoshows that there is a likely reduction of around 0.20 �C onthe Tmin with the increase of GnPR of 1. A high GnPRmeans there are more greenery around the vicinity. Thegreenery provides cooling effect not only from its evapo-transpiration process but also from its shading. On everyGnPR increase of 1, it reduces the SVF value by 0.2 whichin turn, it reduces the Tmax by 0.29 �C, see Fig. 5 (Wongand Jusuf, 2010).

Table 2Predicted air temperature and weather data.

Predicted Tmax, Tavg and Tmin ofbase-case model in the pixel (�C)

Weather data on 21stMay 2008 (�C)

Tmax 32.83 31.14Tavg 29.56 28.97Tmin 26.79 27.24

Fig. 4. Diurnal temperature distributions Case 1–Case 4.

Fig. 5. Tmax, Tavg and Tmin of Case 1–Case 4.



The increase in GnPR has notable impact in cooling loadas shown on Fig. 6. There is a reduction of 2–6% in terms ofcooling load between the simulated cases based on varyingthe GnPR, with GnPR value of 4 have the highest coolingload reduction of 6% (267 kW h) when compared to basecase. It also can be said that the introduction of more green-ery results in a lower cooling load.

3.2. Varying the surrounding building height values

As shown in Fig. 7, compared to the Met data, the tem-peratures become warmer when new building blocks areadded around the PIXEL building. During the day, thetemperature difference could easily reach 0.84 �C. Interest-ingly, the Base Case shows a different result. During thehottest hour of the day, additional building tends to lowerthe temperature. Furthermore, as illustrated in Fig. 8, the“highest surrounding buildings” case (Case 8) is havingthe lowest temperature as compared to the other case.One possible reason is the increase of surrounding buildingheight reduces the SVF, which provides more shading to itssurrounding environment (Wong and Jusuf, 2010). If only

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

In terms of total load (Fig. 9), the surrounding buildingheight seems has positive impact on cooling load reduction.There is a reduction of cooling load up to 4.70% in MaxHeight (60 m) when compared to the Base Case. Interestingly,there is a small difference between 60 m and 45 m (0.2% incooling load). A likely reason is once effective shading hasbeen achieved through particular surrounding height, the fur-ther increase in height only contributes to increased wall sur-face area. Thus, only slightly increase the air temperature,which in turn have almost the same total cooling load.

3.3. Varying the surrounding building density

To further understand the impact of surrounding build-ing on the local air temperature condition, Case 9–Case 12are being developed and assessed. Similar to the finding onprevious cases study (Cases 5–8), by changing the sur-rounding building condition by mean of increasing its den-sity, the temperatures around the PIXEL are significantlyhigher during the day as compared to the Met data but

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lower as compared to the Base Case, as shown in Fig. 10.The temperature differences are found in the range of1.1–1.6 �C if compared to Met data and up to 0.6 �C ascompared to the Base Case. However, during the night,the presence of new additional buildings tends to raisethe temperature by increasing the pavement surface areaand reducing the greenery area, in which increases the ther-mal capacity (Chen and Wong, 2006). Although, theincrease was found not to significant if only the surround-ing building density are considered. Case with less density

(Case 9 and Case 11) has the lowest temperature (Tmax,Tavg and Tmin) as compared to the other cases with moredensity combinations. It is clear that increasing the sur-rounding buildings density reduces GnPR and likely showa negative impact to the air temperature, but there is atrend where a lower surrounding building density resultsin a lower air temperature (Fig. 11).

Case with less surrounding building density (additional11 units of surroundings buildings – Case 9 and Case 11)has positive impact on the cooling load up to 2.7%(112 kW h) as compared to the Base Case, in terms of cool-ing load reduction, as shown in Fig. 12. The figure alsoillustrates that more density (additional 14 units of sur-rounding buildings – Cases 10 and 12) has lesser impacton the cooling load in terms of reduction. The negativevalue, however, shows that at more density condition (Case10), the cooling load is higher than the Base Case. Thenotable increase, nonetheless, is only 0.6% (29 kW h) ascompared to the Base Case. Only after the surroundingbuildings heights are increased, the benefit of surroundingbuildings can be spotted. As found on earlier cases, sur-rounding buildings help reducing the cooling load byincreasing the wall and surface area (PAVE) and decreas-ing the SVF, thus increasing the shadowing effect.

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3.4. Varying combination of surrounding building height and

density

The first combination case being studied is the combina-tion of surrounding building height and density. Buildingshave always related to increase in wall area, pavement area(PAVE) and decrease in SVF. As expected, the combina-

tion of two parameters related to the surrounding buildingsis found warmer than the Met data and cooler than the cal-culated data (Base Case) during the day (Fig. 13). Com-pared to Base Case, the temperature different are in therange of 0.7–0.9 �C. Looking at Fig. 14, even though thedifference is not too obvious, the graphs suggest that thecombination of max HEIGHT and max DENSITY hasthe lowest Tmax and Tavg, which is likely to happen duringthe day. Possible reason is that there is a decrease in theSVF, which means, it increases the shading area.

As shown in Fig. 15, the surrounding building heightand density have positive impact on the cooling load interms of total load reduction. The graph also shows thatthere is a reduction of cooling load up to 4.76% in Heightof 15–60 M + Max Density (Case 14) as compared to thebase case.

3.5. Varying greenery density (GnPR) and surrounding

building height (HEIGHT)

Figs. 16 and 17 show the temperature distribution basedon the combination of modified GnPR and surrounding

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building height. The graph shows that the combination ofmax GnPR and max HEIGHT have the lowest Tmax andTmin. However, the lowest Tavg is found at the case withthe combination of max GnPR and min HEIGHT. The tem-perature differences, compared to the Base Case, are foundup to 1.2 �C. The possible reason is the increase of buildingheight reduces the SVF (shading effect). Once the canyon iscompletely shaded by the surrounded buildings, increasingthe HEIGHT will not give any positive impact. The Tavg

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

Between the Case 17 and Case 19, where the GnPR is atthe maximum and the HEIGHT varies from maximum tominimum, there is no significant change in all the temper-atures. Hence, it can be said that the main governing factorfor the reduction of temperature is still the greenery(GnPR).

Fig. 18 shows the total load of combination of GnPRand HEIGHT. Basically, the graph suggests that GnPRtends to give a positive impact and HEIGHT gives a neg-ative impact in terms of total cooling reduction. Regardlessthe HEIGTH variation, Case 17 and Case 19, which havemax GnPR, show slightly more than 10% (428 kW h and443 kW h respectively) total cooling load reduction. How-ever, further investigation shows that max HEIGHT seemsto have more benefit than min HEIGHT as shown in Case18 and Case 20.

3.6. Varying greenery density (GnPR) and surrounding

building density (DENSITY)

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



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and HEIGHT are combined. The lowest temperatures arefound when max GnPR is combined with min DENSITY,with temperature difference up to 1.2 �C compared to theBase Case, during daytime. The result supports earlier find-ing that greenery give positive impact to the environmentdue to its shading and DENSITY, which related to build-ing pavement area, gives negative impact due to increaseof wall surface area and pavement.

Similar to the HEIGHT, the change in DENSITY seemsto show a negative impact. But, it is balanced by the

GnPR. The increase of building density mainly contributesto the Tmax. The results further suggest that GnPR is stillthe main governing factor to moderate the temperature.

In terms of total load and energy consumption, the com-bination of GnPR and DENSITY study shows a reductionon the cooling load (Fig. 21). There is a reduction of cool-ing load up to 10.74% in Max GnPR and Min Density ascompared to the base case. The graph also shows thatGnPR has the most influence in terms of cooling loadreduction as compared to DENSITY. The min DENSITY,however, is found giving more positive impact than themax DENSITY.

3.7. Varying all three variable together; GnPR, HEIGHT

and DENSITY

Fig. 22 shows the possible combination of varyingGnPR, HEIGHT and DENSITY. Examining the diurnalgraph, almost all combination cases have a similar pattern.With the modification of the surrounding condition, whichis governed by the combination of different variables, tendsto give a negative impact to the environment. As foundfrom the other combination cases, as compared to the

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Met data, all of the comparison cases are found slightlywarmer during the day and cooler during the night. How-ever, as compared to the Base Case, most of the cases arefound cooler up to 1.1 �C during daytime. However, caseswith dense greenery (max GnPR) and surrounded by morebuilding (max DENSITY) – Case 25 and Case 26; tend tohave a lower temperature during the day and warmer dur-ing the night as compared to the other cases. Both of theseparameters likely have the most impact on the diurnaltemperature distribution. Interestingly, other cases (Cases

27–30), with other possible combinations, do not showany differences on the graph. Even, Case with max GnPRcombined with min DENSITY and max HEIGHT (Case29) or max GnPR, min DENSITY and min HEIGHT(Case 30) show a similar diurnal trend as compared tocases with min GnPR (Cases 27, 28, 31 and 32).

Fig. 23 shows the daily average of each case in term ofTmax, Tavg and Tmin. The graph illustrates that cases withdense greenery (Cases 25, 26, 29 and 30) tend to have a lowertemperature as compared to other cases. In some extent, sur-rounding building density and height might have positiveimpact to the ambient temperature due its shading effect,but this effect is very limited and not too obvious. Case 32with sparse greenery and surrounded by high-density build-ings are found to be the hottest among all. Interestingly,when dense greenery is introduced (Case 25), the ambienttemperature found to be lower. This result confirms the find-ing by Chen and Wong (2006) that greenery can help miti-gate the temperature in urban area. Fig. 20 also illustratesthat the relationship between the variables shows the tem-perature is strongly influenced by the GnPR as comparedto other two parameters (HEIGTH + DENSITY).

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Fig. 24 shows the cooling load and the potential of savingsof each combination of GnPR, DENSITY and HEIGTcases as compared to the base case. Interestingly, cases withmax GnPR (Cases 25, 26, 29 and 30) tend to have a lowercooling load. As compared to the base case, cases withmax GnPR are able to reduce the total load from 3.60%(357 kW h) up to 4.40% (441 kW h). The next governing var-iable that is likely has a positive impact in lowering the cool-ing load is HEIGHT. Case 27 and Case 28, which havesimilar max HEIGHT, are able to reduce the cooling loadup to 0.50% (72 kW h) and 0.77% (104 kW h) respectively.Looking at the other cases, which have similar maxHEIGHT (Case 25 and Case 29), the combination withmax GnPR shows more positive impacts in terms of savings.However, the max GnPR cases will have the highest reduc-tion in terms of total cooling load, if the HEIGHT is at theminimum (Case 26 and Case 30). The graph also illustratesthat, DENSITY has the least significant impact in loweringthe total cooling load. The DENSITY, however, furtherincreases the cooling load due to the increase of its value.

4. Conclusions

Urban areas have a large variety of forms and surfacecharacteristic. Basically, the microclimate of these areas isinfluenced by several urban elements, such as the urbangeometry, the greenery, and the properties of surfaces. It

is clear that urban area without a proper use of these ele-ments is likely contributed to discomfort and inconve-nience to the people.

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

1. UHI is a growing concern globally, requiring immediateaddress at all levels (macro and micro).

2. Urban morphology has a strong role in determining thevariations that you can have in the temperature at themicro level (microclimate).

3. Variables such as GnPR, HEIGHT and DENSITYshow a high degree of impact in altering the temperatureor microclimate of any location.

4. The degree of impact on the air temperature can be up0.9–1.2 �C

5. Each of the identified variables has a varying degree ofimpact. The highest impact is GnPR due to shadingeffect of trees followed by HEIGHT and DENSITY.

6. The effect of GnPR overrides the effect of the other vari-ables – height and density in all the identified cases,proving the importance of greenery in altering themicroclimate condition.

7. The cooling load reduction due to the impact of the vari-ables is in the range of 5–10% if addressed effectively

8. These savings are achieved by only altering the urbanmorphology.


4133

3693

4061 4029

3707 3692

4233 4146

3776

10472-13-100

441426440357

-500

0

500

1000

1500

2000

2500

3000

3500

4000

4500

basecase

case 25 case 26 case 27 case 28 case 29 case 30 case 31 case 32

KW

h total load





9. The savings can be further improved by addressing theissue of heat gain at building level. This can be donethrough the adoption of effective fac�ade systems andshading devices for the envelope coupled with otherenergy saving technologies in the building system espe-cially the mechanical system.

Although 5% may seem a small percentage of energysaving, a note should be taken that this saving is only fromone building. This energy saving can be compounded whenwe see it at the macro level with all of the buildings havethe potential to save energy by 5% due to only by a propermaster plan design. When the building design aspect is alsoaddressed, we can expect to get a higher energy savingpotential.

Acknowledgment

The authors would like to acknowledge World FutureFoundation for supporting this research.

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

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