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

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Solar Energy 85 (2011) 57–71 www.elsevier.com/locate/solener

Evaluation of the impact of the surrounding urban morphology on 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

b

Department of Building, National University of Singapore, Singapore 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 2010 Available online 3 December 2010 Communicated Communicated by: Associate Associate Editor Matheos Santamouris Santamouris

Abstract

Empirical models of minimum (T min min), average (T avg avg) and maximum (T max max) 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 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 to give a better better understand understanding ing on the likely impact impact of the modified modified urban morphology morphology on energy energy consumptio consumption. n. The objective of this research is to assess and to compare how the air temperature variation of urban condition can affect the building energy energy consum consumpti ption on in tropic tropical al climat climatee of Singap Singapore ore.. In order order to achiev achievee this this goal, goal, a series series of numeri numerical cal calcul calculati ation on and buildi building ng simula simulatio tion n are utiliz utilized. ed. A total total of 32 cases, cases, consid consideri ering ng differe different nt urban urban morpho morpholog logies ies,, are identi identified fied and evalua evaluated ted to give better better a unders understan tandin dingg on the 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 to 2 °C. The results also strongly indicate an energy saving of 4.5% if the urban elements are addressed effectively. Ó

2010 Elsevier Ltd. All rights reserved.

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

1. Introduction Introduction

Urba Urbaniz nizat atio ion n in the the rece recent nt year yearss has has sign signific ifican antly tly increased the necessity for the city to further develop itself  to accommodate the incoming inhabitants, in which ameliorate the urban microclimate. microclimate. The development development may lead to the increase of urban heat island (UHI) intensity, a phenomenon which air temperature in densely built urban area

⇑ Corresponding author. Address: Center for Sustainable Asian Cities, Nationa Nationall Univers University ity of Singapor Singapore, e, 4 Archite Architectur cturee Drive, Drive, Singapo Singapore re 117566, Singapore. Tel.: +65 6516 4691. E-mail address: [email protected] (S.K. Jusuf).

0038-092X/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.solener.2010.11.002 doi:10.1016/j.solener.2010.11.002

are higher than the temperature of the surrounding rural area. Higher urban temperature has a serious impact on the electricity demand for air conditioning of buildings, it also increase the smog production, while contributing to increased emission of pollutants from power plants, including carbon dioxide, sulfur dioxide, nitrous oxides and other suspended particulates. UHI, however, could be found in ever everyy town town and and city city all all over over the the world world (Ok ( Okee an and d Ea Eas, s, 1971; 197 1; Lan Landsb dsberg erg,, 198 1981; 1; Pad Padman manabh abhamu amurty, rty, 199 1990/1 0/1991 991;; Sani, 1990/1991; Eliasson, 1996; Giridharan et al., 2007). 2007 ). The heat island intensity could easily reach up to 10 °C, which has been observed in India. Furthermore, it is clear that that this this incr increas easee of urba urban n air air temp temper erat atur uree will will also also increase the energy consumption by increasing the cooling

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load. In city of Athens, where the mean heat island intensity are found exceeding 10 °C, the cooling load of building in the urban area found to be doubled, the peak electricity load for cooling may be tripled because of the higher ambient temperatures temperatures (Santamauris et al., 2001). 2001). Study by Kolokotroni et al. (2005) on London UHI found that during typical hot week the rural reference office has 84% energy demand for cooling as compared to a similar urban office based in the same location. Another study utilizing measured air temperature temperature data and building energy simulation at 24 different locations within London UHI found that urban cooling load is up to 25% higher than the rural load over the year; however the annual heating load is reduced by 22% (Kolokotroni (Kolokotroni et al., 2007). 2007). Other urban modifications, nevertheless, also have been found altering the temperat eratur uree in urba urban n area rea and and yet modi modify fy the the ener energy gy consumption. Study in USA (Akbari ( Akbari et al., 1992) 1992) found that large number of trees and urban parks able to reduce local air temperature by 0.5–5 °C. Each 1 °C drop in air temperature could lower the peak electric demand for cooling by 2–4%. Hence, urban elements such as trees or parks have significant impact and energy consumption. ShashuaBar et al. (2010) studied the variables that influence the variabilit iabilityy of air temperat temperature ure in urban urban street streetss that that includ includee trees, building configuration, albedo of the surroundings and street ventilation. Some studies also investigated the selection of building materials which influences not only the outdoor space but also the building energy consumption tion (Ta Taha ha,, 19 1997 97;; Ak Akba bari ri et al al., ., 20 2001 01;; Do Doul ulos os et al al., ., 2004). 2004 ). These variables are in the control of the urban planners and architects. Hence, the urban air temperature can be attenuated with a proper design and modeling. In Singapore, with the present trend of having building going higher and closer to one another, as well as the extensive sive usage usage of air conditioni conditioning, ng, UHI are likely to occur occur (Wong and Chen, 2009; Jusuf et al., 2007). 2007 ). The temperature increases in urban area can lead to significant use of  air conditioning. UHI studies in Singapore shows a possible increase of urban air temperature of 1 °C. If the trend keep on continue, within 50 year, the energy consumption for cooling will increase in order of 33 GW h per annum

for the whole island (Tso, (Tso, 1994). 1994). More recent study found that outdoor air temperature determines the energy savings of buildings. According to some studies, every 1 °C of outdoor air temperature reduction saves 5% of building energy consumption (Chen (Chen and Wong, 2006; Wong et al., 2009 ). It becomes increasingly important to study urban microclima climatic tic envir environ onmen ments ts and and to appl applyy thes thesee findi finding ngss to improve the people’s comfort and to decrease the energy consumption in the urban areas. The present paper illustrates the result of an urban study carried out in Singapore aiming to assess and discuss the impact of the urban morphol pholog ogyy on the the ener energy gy cons consum umpt ptionof ionof The The PIXEL PIXEL at Buon Buonaa Vista. Computer simulation software, TAS and Singapore urban air temperature prediction model, STEVE tool are utilized utilized to evaluate evaluate the impact impact of increased increased ambient ambient tempertemperature on the cooling performance of building (see Fig. 1). 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 in the one-north estate, Singapore. The facility is also strategically located in the midst of landscaped greeneries with park opposite its main entrance.  2.2. The case studies

A base case (Fig. (Fig. 2) 2) and 32 cases (Fig. ( Fig. 3) 3) were employed employed to have better understanding on the impact of different surrounding urban morphologies to the energy consumption of the building. These various cases will address the optimum solution between greenery, building area and building density density to address the impact of the temperature temperature on energy consumption of the PIXEL building. Table 1 shows the different parameters used as the case studies. The base-case model consists of building and greenery, which represent the actual surrounding urban morphology of the PIXEL building. A typical hot day condition on 21st May 2008 was chosen as the background temperature for

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

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

the STEVE tool. As mentioned, STEVE tool predicts the 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 were developed for the STEVE model. Named as A, B, C and D, each of these circles will have its own calculation, see Fig. 2. 2. The final predicted air temperature, which is the average average of four four predict predicted ed air temperatu temperature re (A, B, C and D), will be used in the TAS software as the boundary condition to develop cooling load and energy consumption demand of the building. Given the same methodology as the Base Case, the first 12 Cases (Case 1–Case 12) are trying to find out the role of  the individual parameter parameter on the urban air temperature temperature and the building cooling load. These basic parameters are the surrounding surrounding greenery, greenery, quantified quantified as Green Green Plot Plot Ratio Ratio –  GnPR (Case 1–Case 4), the surrounding building height  – HEIGHT (Case 5–Case 8) and surrounding building density – DENSITY (Case 9–Case 12). The next 12 cases (Case 13–Case 24) are basically the combination of two of these parameters. Combinations are worked out based on uniform form,, rand random om and and stra stratu tum m effec effects ts.. Comb Combin inat atio ion n of  HEIGHT HEIGHT and DENSITY, DENSITY, for example, example, has impacts impacts on the greenery and sky view factors (SVF). The last eight cases (Case 25–Case 32), are combination of three of them. These These eight eight cases cases of possib possible le combin combinatio ation n were having having

similar methodology as the base case in order to have a fair comparison.   2.3. STEVE tool 

Screen Screening ing Tool Tool for Estate Estate Enviro Environmen nmentt Eva Evalua luation tion,, STEVE tool (Jusuf (Jusuf and Wong, 2009), 2009 ), is a web based application that is specific to an estate and it calculates the T max max, and of a point interest of an estate. A set of three T avg T  avg min min equations shown in Eq. (1) gives the correlation between the urban urban morpho morpholog logyy parame parameter terss (buildi (building, ng, paveme pavement nt and greenery) and estate air temperature. temperature. These prediction prediction models were based on the empirical data collected over a period of close to 3 years as part of the development of  an assess assessmen mentt method method to evalua evaluate te the impact of estate estate development (in this case, NUS Kent Ridge Campus and One North) North),, which which includ includes es the assessm assessment ent method method of  existing greenery condition (Wong ( Wong and Jusuf, 2008a) 2008a ) and greenery condition for a proposed master plan in an estate develo developmen pmentt (Wo Wong ng an and d Ju Jusu suf, f, 20 2008 08b b). The The gree greene nery ry assessment used Green Plot Ratio (GnPR) method. The GnPR is derived from the average of greenery on a lot, using the leaf area index (LAI), in proportion to the total lot area (Ong, (Ong, 2003). 2003). The higher the GnPR value, the denser the greenery condition in a built environment.

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Fig. 3. 32 Study cases. T  min

ð CÞ ¼ 4:061 þ 0:839 Ref  T  min ð CÞ þ 0:004 PAVE ð%Þ À 0:193 GnPR À 0:029 HBDG þ 1:339E À 06 WALL ðm2 Þ

T  avg

ð CÞ ¼ 2:347 þ 0:904 Ref  T  avg ð CÞ þ 5:786E À 05 SOLARtotal ðW=m2 Þ þ 0:007 PAVE ð%Þ À 0:06 GnPR À 0:015 HBDG þ 1:311 E À 05WALL ðm2 Þ þ 0:633 SVF

T  max

ð CÞ ¼ 7:542 þ 0:684 Ref  T  max ð CÞ þ 0:003 SOLARmax ðW=m2 Þ þ 0:005 PAVE ð%Þ À 0:016 HBDG þ 6:777E À 06 WALL ðm2 Þ þ 1:467 SVF þ 1:466 ALB

ð1Þ

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Fig. 3 (continued )

Daily Daily minimu minimum m (T min maximum min), average (T avg avg) and maximum (T max max) temperature of each point of measurements were calculated as dependent variable of the air temperature prediction model. The independent variables of the models can be categorized into:

1. Climate Climate predic predictor tors: s: daily mini minimum mum (T min-r min-r), average (T avg-r avg-r) and maximum (T max-r max-r) temperature at meteorologica logicall statio station; n; avera verage ge of dai daily sola solarr rad radiati iation on (SOLAR). For the SOLAR predictor, average of daily solar solar radiati radiation on total total (SOLAR (SOLARtotal) was was used used in T avg avg

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Table 1 Study cases input values. Study cases

Surroundings greenery (GnPR)

Surroundings buildings buildings height

Surroundings buildings density

Remarks

Base Case Case Case Case Case

1.45

Existing

Existing

Actual condition

1 2 3 4

1 2 3 4

Existing Existing Existing Existing

Existing Existing Existing Existing

Height

Case Case Case Case

5 6 7 8

Existing Existing Existing Existing

15 m 30 m 45 m 60 m

11 11 11 11

Density

Case 9

Existing

15 m

11 blocks

Case 10 Case 11

Existing Exxisting E

15 m 30 m

14 blocks 11 blocks

Case 12

Existing

30 m

14 blocks

Case 13

Existing

15 m,30 m,45 m,60 m

29 blocks

Case 14 Case 15 Case 16

E xi xisting Existing Existing

15 m,30 m,45 m,60 m 60 m 15 m,30 m,45 m,60 m

29 2 9 blocks 29 blocks 11 blocks

GnPR and height

Case 17 Case 18 Case 19 Case 20

4 0.6 4 0.6

60 m 60 m 15 m 15 m

11 blocks 11 blocks 11 blocks 11 blocks

GnPR and density

Case 21

4

30 m

29

Case 22 Case 23 Case 24

0.6 4 0.6

30 m 30 m 30 m

29 11 11

Case 25

4

60 m

29

Case Case Case Case Case Case Case

4 0.6 0.6 4 4 0.6 0.6

15 m 60 m 60 m 60 m 15 m 15 m 15 m

29 29 11 11 11 29 11

GnPR

Height and density

GnPR, height and density

26 27 28 29 30 31 32

models, while average of solar radiation maximum of  the the day day (SOLA (SOLAR Rmax) was was used sed in the the T max max model. SOLAR predictor is not applicable for T min min model. 2. Urban morphology predictors: percentage of pavement area area over over R 50 m sur surfac facee area area (PAVE), average height to building area ratio (HBDG), total wall surface area (WALL), Green Green Plot Plot Ratio Ratio (GnPR), sky view view factor factor (SVF) and average surface albedo (ALB). Each set point covers a surface area within a radius of  50 m. Interpolation is carried out to obtain the estate air temper temperatu ature re distrib distributio ution n of the estate estate.. By changi changing ng the urban morphology parameters, STEVE tool can provide the urban ambient temperatures to TAS software as the boundary conditions. For For the the Climate this simul simulat ation ion stud studyy is Climate Predictors Predictors, this using the weather data on 21st May 2008 from National

blocks blocks blocks blocks Case 9 is the same with Case 5. It is represented for group comparison Case 11 is the same with Case 6. It is represented for group comparison

Different configuration

Universi University ty of Singap Singapore ore (NUS) (NUS) meteoro meteorolog logical ical statio station, n, located at the rooftop of Faculty of Engineering, about 2 km from the Pixel building, with details as follow: a. T min-r min-r = 27.24 °C. b. T avg-r avg-r = 28.97 °C. c. T max-r max-r = 31.14 °C. d. SOLARtotal = 5058.39 W/m 2. e. SOLARmax = 764 W/m 2.

  2.4. TAS simulation

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

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able to generate cooling load and energy consumption prediction of the PIXEL building. The The build building ing ma mate teri rials als that that are are used used to deve develo lop p the the PIXEL model are as follows: 1. 2. 3. 4. 5. 6.

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

The boundary conditions used in this TAS simulation for all of the case studies are as follows: 1. Air conditioning is on from 08.00 to 22.00 h (extended office hours). 2. Thermostat setting: a. Tempera Temperatur turee upper upper limit: limit: 24 °C and and lowe lowerr limit: limit: 21 °C. b. RH upper limit: 70% 70% and lower limit: 60%. 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

air temperature measured at NUS meteorological station (Met Data) and calculated data from STEVE tool (Base Case). The deviations are mainly due to the urban morpholog phologyy condit condition ion surrou surroundi nding ng the Pixel. Pixel. On the same same day, with the result from the STEVE tool, total cooling load of 4133 kW h was derived from TAS software. 3.1. Varying the GnPR values

Fig. 4 shows comparison between the base case and the first four cases. With GnPR ranging from 1 to 4, the Met data is found to have the lowest temperature during the day and the hottest temperature during the night compare to the the othe otherr case cases. s. Howe Howeve ver, r, the the calcu calculat lated ed data data from from STEVE tool are likely found the opposite. The base case calculated data (Base Case) is found to be warmer as compared to the cases with more GnPR value (Cases 2, 3, 4). The temperature differences are found to be up to 1.3 °C during the daytime. Furthermore, the calculated result also shows that there is a likely reduction of around 0.20 °C on the T min min with the increase of GnPR of 1. A high GnPR means there are more greenery around the vicinity. The greenery provides cooling effect not only from its evapotranspiration process but also from its shading. On every GnPR increase of 1, it reduces the SVF value by 0.2 which in turn, it reduces the T max max by 0.29 °C, see Fig. 5 (Wong and Jusuf, 2010). 2010 ).

The result data from the STEVE tool and TAS software have been examined in order to assess the ambient air temperature around the PIXEL building and its energy consumptio sumption n based based on given given differe different nt urban urban morpho morpholog logies. ies. Table 2 shows the predicted T max max, T avg avg and T min min of the base-case model, which are deviate from the background Table 2 Predicted air temperature and weather data.

T max max T avg avg T min min

Predicted T max max, T avg avg and T min min of  base-case model in the pixel (°C)

Weather data on 21st May 2008 (°C)

32.83 29.56 26.79

31.14 28.97 27.24

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Fig. 5. T max max, T avg avg and T min min of Case 1–Case 4.

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

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The The incr increa ease se in GnPR GnPR has has notab notable le impact impact in cool coolin ingg load load as shown on Fig. 6. 6. There is a reduction of 2–6% in terms of  cooling load between the simulated cases based on varying the GnPR, with GnPR value of 4 have the highest cooling load reduction of 6% (267 kW h) when compared to base case. It also also can be said that that the introduction introduction of of more greengreenery results in a lower cooling load. 3.2. Varying the surrounding building height values

As shown in Fig. 7, 7, compared to the Met data, the temperatures become warmer when new building blocks are added around the PIXEL building. During the day, the temperature temperature difference could easily reach 0.84 °C. InterestInterestingly, the Base Case shows a different result. During the hottest hour of the day, additional building tends to lower the temperature. Furthermore, as illustrated in Fig. 8, 8, the highest highest surrounding surrounding buildings buildings case (Case 8) is having the lowest temperatu temperature re as compare compared d to the other case. case. One possible reason is the increase of surrounding building height reduces the SVF, which provides more shading to its surrounding environment (Wong ( Wong and Jusuf, 2010). 2010 ). If only “

”

building height is being considered, a taller building seems gives more benefit to its surrounding environment. In terms of total load (Fig. (Fig. 9), 9), the surrounding building height seems has positive impact on cooling load reduction. There is a reduction of cooling load up to 4.70% in Max Height Height (60 m) whencomp when compared aredto to the Base Case. Case. Interest Interestingl ingly, y, there is a small difference between 60 m and 45 m (0.2% in cooling load). A likely reason is once effective shading has been achieve achieved d through through particu particular lar surround surrounding ing height, height, the further increase in height only contributes to increased wall surface 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 understand the impact of surrounding surrounding building on the local air temperature condition, Case 9–Case 12 are being developed and assessed. Similar to the finding on previo previous us cases cases study study (Cases (Cases 5–8 5–8), ), by changi changing ng the surrounding building condition by mean of increasing its density, the temperatures around the PIXEL are significantly higher during the day as compared to the Met data but

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case 1

case 2

case 3

case 4

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

Fig. 7. Diurnal temperature temperature distributions distributions Case 5–Case 8.

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(Case 9 and Case 11) has the lowest temperature ( T max max, and ) as compared to the other cases with more T avg T  avg min min

Fig. 8. T max max, T avg avg and T min min of Case 5–Case 8.

lower as compared to the Base Case, as shown in Fig. 10. 10. The The temp tempera eratu ture re differ differen ences ces are are foun found d in the the rang rangee of  1.1–1.6 °C if compared to Met data and up to 0.6 °C as compared to the Base Case. However, during the night, the presen presence ce of new additio additional nal buildin buildings gs tends tends to raise raise the temperature by increasing the pavement surface area and reducing the greenery area, in which increases the thermall capa ma capaci city ty (Ch Chen en an and d Wo Wong ng,, 20 2006 06). ). Altho Althoug ugh, h, the the increase was found not to significant if only the surrounding building density are considered. Case with less density

density combinations. It is clear that increasing the surrounding buildings density reduces GnPR and likely show a negative impact to the air temperature, but there is a trend where a lower surrounding building density results in a lower air temperature (Fig. (Fig. 11). 11). Case with less surrounding building density (additional 11 units of surroundings buildings – Case 9 and Case 11) has has posi positi tive ve impa impact ct on the the cool coolin ingg load load up to 2.7% 2.7% (112 kW h) as compared to the Base Case, in terms of cooling load reduction, as shown in Fig. 12. 12. The figure also illustrates that more density (additional 14 units of surrounding buildings – Cases 10 and 12) has lesser impact on the cooling load in terms of reduction. The negative value, however, shows that at more density condition (Case 10), the cooling load is higher than the Base Case. The notable increase, nonetheless, is only 0.6% (29 kW h) as compared to the Base Case. Only after the surrounding buildings heights are increased, the benefit of surrounding buildings can be spotted. As found on earlier cases, surroun roundi ding ng buil buildin dings gs help help redu reduci cing ng the the cooli cooling ng load load by increasing the wall and surface area (PAVE) and decreasing the SVF, thus increasing the shadowing effect.

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2000

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112

c as e 5

c as e 6

186

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0 bas e c as e

c as e 7

c as e 8

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

Fig. 10. Diurnal temperature temperature distributions distributions Case 9–Case 12.

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Fig. 11. T max max, T avg avg and T min min of Case 9–Case 12.

3.4. Varying combination of surrounding building height and  density

The first combination case being studied is the combination of surrounding building height and density. Buildings have 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 buildings is found warmer than the Met data and cooler than the calculated data (Base Case) during the day (Fig. ( Fig. 13). 13). Compared to Base Case, the temperature different are in the range of 0.7–0.9 °C. Looking at Fig. 14, 14, even though the difference is not too obvious, the graphs suggest that the combination of max HEIGHT and max DENSITY has the lowest T max max and T avg avg, which is likely to happen during the day. Possible reason is that there is a decrease in the SVF, which means, it increases the shading area. As shown in Fig. 15, 15, the surrounding building height and density have positive impact on the cooling load in terms of total load reduction. The graph also shows that 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 base case. 3.5. Varying greenery density (GnPR) and surrounding  building height (HEIGHT)

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

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case 9

case 10

case 11

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case 12

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

Fig. 13. Diurnal temperature temperature distributions distributions Case 13–Case 16.

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Fig. 14. T max max, T avg avg and T min min of Case 13–Case 16.

building height. The graph shows that the combination of  max GnPR and max HEIGHT have the lowest T max max and . However, the lowest is found at the case with T min T  min avg avg the the comb combin inati ation on of ma maxx GnPR GnPR and and min HE HEIG IGHT. HT. The The temtemperature perature differences, compared compared to the Base Case, are found up to 1.2 °C. The possible possible reason is the increase of building height reduces the SVF (shading effect). Once the canyon is completely shaded by the surrounded buildings, increasing the HEIGHT will not give any positive impact. The T avg avg starts to increase due to the increase of Wall areas. The high GnPR, however, helps to balance the negative impact.

Between the Case 17 and Case 19, where the GnPR is at the maximum and the HEIGHT varies from maximum to minimum, there is no significant change in all the temperatures. Hence, it can be said that the main governing factor for for the the redu reduct ction ion of temp tempera eratu ture re is still still the the gree greene nery ry (GnPR). Fig. 18 shows the total load of combination of GnPR and HEIGHT. Basically, the graph suggests that GnPR tends to give a positive impact and HEIGHT gives a negative impact in terms of total cooling reduction. Regardless the HEIGTH variation, Case 17 and Case 19, which have max GnPR, show slightly more than 10% (428 kW h and 443 kW h respectively) total cooling load reduction. However, further investigation shows that max HEIGHT seems to have more benefit than min HEIGHT as shown in Case 18 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 GnPR and DENSITY values, the temperature distribution seems to have similar trend to the previous cases when the GnPR

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Fig. 15. Total load and total load reduction reduction of Case 13–Case 16.

Fig. 16. Diurnal temperature temperature distributions distributions Case 17–Case 20.

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GnPR. The increase of building density mainly contributes to the T max max. The results further suggest that GnPR is still the main governing factor to moderate the temperature. In terms of total load and energy consumption, the combination bination of GnPR and DENSITY study shows a reduction reduction on the cooling load (Fig. (Fig. 21). 21). There is a reduction of cooling load up to 10.74% in Max GnPR and Min Density as comp compar ared ed to the the base base case case.. The The grap graph h also also show showss that that GnPR GnPR has has the the most most influ influenc encee in terms terms of cooli cooling ng load load reduction reduction as compared compared to DENSITY. The min DENSITY, DENSITY, however, is found giving more positive impact than the max DENSITY.

Fig. 17. T max max, T avg avg and T min min of Case 17–Case 20.

and HEIGHT are combined. The lowest temperatures are found when max GnPR is combined with min DENSITY, with temperature difference up to 1.2 °C compared to the Base Case, during daytime. The result supports earlier finding that greenery give positive impact to the environment due to its shading and DENSITY, which related to building pavement area, gives negative impact due to increase of wall surface area and pavement. Similar to the HEIGHT, the change in DENSITY seems to show show a nega negati tive ve impa impact ct.. But, But, it is bala balanc nced ed by the the

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

Fig. Fi g. 22 shows shows the possib possible le combin combinatio ation n of varyin varyingg GnPR, HEIGHT and DENSITY. Examining the diurnal graph, graph, almost all combination combination cases have a similar pattern. pattern. With the modification modification of the surrounding surrounding condition, condition, which is governed by the combination of different variables, tends to give a negative impact to the environment. As found from from the other combin combinatio ation n cases, cases, as compar compared ed to the

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

Fig. 19. Diurnal temperature temperature distributions distributions Case 21–Case 24.

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Fig. 20. T max max, T avg avg and T min min of Case 21–Case 24.

Met data, all of the comparison cases are found slightly warmer during the day and cooler during the night. However, as compared to the Base Case, most of the cases are found cooler up to 1.1 °C during daytime. However, cases with dense greenery (max GnPR) and surrounded by more building (max DENSITY) – Case 25 and Case 26; tend to have a lower temperature during the day and warmer during the night as compared to the other cases. Both of these parame parameter terss likely likely have have the most impact impact on the diurnal diurnal temperature distribution. Interestingly, other cases (Cases

27–30),, with other 27–30) other possibl possiblee combin combinatio ations, ns, do not show show any differences on the graph. Even, Case with max GnPR combined with min DENSITY and max HEIGHT (Case 29) 29) or ma maxx GnPR, GnPR, min DE DENS NSIT ITY Y and and min HEIGHT HEIGHT (Case (Case 30) show a similar similar diurnal diurnal trend trend as compar compared ed to cases with min GnPR (Cases 27, 28, 31 and 32). Fig. 23 shows the daily average of each case in term of  T max max, T avg avg and T min min. The graph illustrates that cases with dens densee gree greene nery ry (Cas (Cases es 25, 25, 26, 26, 29 and and 30) 30) tend tend to have have a lowe lowerr temp tempera eratu ture re as comp compar ared ed to othe otherr case cases. s. In some some exte extent, nt, sursurrounding building density and height might have positive impact to the ambient temperature due its shading effect, but this effect is very limited and not too obvious. Case 32 with sparse greenery and surrounded by high-density buildings are found to be the hottest among all. Interestingly, when dense greenery is introduced (Case 25), the ambient temp tempera eratu ture re foun found d to be lowe lower. r. This This resu result lt confi confirms rms the the findfinding by Chen and Wong (2006) that greenery can help mitigate the temperature in urban area. Fig. 20 also illustrates that the relationship between the variables shows the temperature is strongly influenced by the GnPR as compared to other two parameters parameters (HEIGTH + DENSITY). DENSITY).

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

Fig. 22. Diurnal temperature temperature distributions distributions Case 25–Case 32.

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Fig. 23. T max max, T avg avg and T min min of Case 25–Case 32.

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

Fig. 24 shows Fig. shows the coolingload coolingload and the potent potential ial of saving savingss of each combination of GnPR, DENSITY and HEIGT cases as compared to the base case. Interestingly, cases with max GnPR (Cases 25, 26, 29 and 30) tend to have a lower cooling load. As compared to the base case, cases with max GnPR are able to reduce the total load from 3.60% (3577 kW (35 kW h) h) up to 4.40% 4.40% (44 (4411 kW kW h). h). The next next govern governing ing varvariabl iablee that that is likely likely has has a posi positiv tivee impact impact in lower lowerin ingg the the cool cool-ing load is HEIGHT. Case 27 and Case 28, which have similar max HEIGHT, are able to reduce the cooling load up to 0.50% (72 kW h) and 0.77% (104 kW h) respectively. Look Lookin ingg at the the othe otherr case cases, s, whic which h have have simi simila larr ma maxx HEIGHT (Case 25 and Case 29), the combination with maxx GnPR ma GnPR show showss more more posit positiv ivee impa impacts cts in terms terms of savi saving ngs. s. However, However, the max GnPR cases will have the highest reduction in terms of total cooling load, if the HEIGHT is at the minimum minimum (Case 26 and Case 30). The graph also illustrates illustrates that, DENSITY has the least significant impact in lowering the total cooling load. The DENSITY, however, further increases the cooling load due to the increase of its value. 4. Conclusions Conclusions

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

is clear that urban area without a proper use of these elements ments is likely likely contri contribut buted ed to discom discomfor fortt and inconv inconveenience to the people. Based on the study, the paper conclude a few essential things, as follows: 1. UHI is a growing concern concern globally, globally, requiring immediate immediate address at all levels (macro and micro). 2. Urban morphology has a strong role in determining the variations that you can have in the temperature at the micro level (microclimate) (microclimate).. 3. Var Varia iable bless such such as GnPR GnPR,, HEIGHT HEIGHT and and DENSI DENSITY TY show a high degree of impact in altering the temperature or microclimate of any location. 4. The degree of impact on the air temperature can be up 0.9–1.2 °C 5. Each of the identified variables has a varying degree of  impact impact.. The highest highest impact impact is GnPR GnPR due to shadin shadingg effect of trees followed by HEIGHT and DENSITY. 6. The effect of GnPR overrides overrides the effect of the other variables – height and density in all the identified cases, prov provin ingg the the impo importa rtanc ncee of green greener eryy in alter alterin ingg the the microclimate condition. 7. The cooling load reduction due to the impact of the variables is in the range of 5–10% if addressed effectively 8. These savings are achieved by only altering the urban morphology.

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9. The savings can be further improved by addressing the issue of heat gain at building level. This can be done through the adoption of effective fac ade systems and shadin shadingg device devicess for the envelo envelope pe couple coupled d with other energy saving technologies in the building system especially the mechanical system. ß

Although 5% may seem a small percentage of energy saving, a note should be taken that this saving is only from one building. This energy saving can be compounded when we see it at the macro level with all of the buildings have the potential to save energy by 5% due to only by a proper master plan design. When the building design aspect is also addres addressed, sed, we can expect to get a higher higher energy saving saving potential. Acknowledgment

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