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Energy performance of residential buildings in Singapore K.J. Chua*, S.K. Chou Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117576, Singapore

a r t i c l e i n f o

a b s t r a c t

Article history: Received 25 May 2009 Received in revised form 27 October 2009 Accepted 29 October 2009 Available online 20 November 2009

Energy consumption of buildings takes up about a third of Singapore’s total electricity production. In this paper, we present a pioneering study to investigate the energy performance of residential buildings. Beginning with an energy survey of households, we established the air-conditioning usage patterns and modelled residential buildings for computer simulations. An ETTV equation for residential buildings was developed. Employing this equation, we demonstrated how to achieve improved energy efﬁciency in residential buildings. Two types of residential buildings, namely, point block and slab block, were modelled and parametric runs performed. ETTV impacts the energy consumption of residential buildings and thus lowering the ETTV will result in reduced building heat load. Results from the developed equation showed that a unit decrease in ETTV resulted in 4% and 3.5% reduction in annual cooling energy for point block and slab block residential buildings, respectively. In addition, a set of simple energy and load estimating equations were developed using computer simulation and local climatic data. These equations provided a means of estimating the annual cooling energy consumption of residential buildings in Singapore. Ó 2009 Elsevier Ltd. All rights reserved.

Keywords: Envelope thermal transfer value Residential buildings Energy performance Cooling energy requirement

1. Introduction Recently, global warming has been thrust back into the limelight with the completion of a comprehensive updated study on the phenomenon. Over 2000 scientists from more than 100 countries have arrived at the conclusion that humans are ‘‘very likely’’ the cause of global warming [1]. The United Nations Inter-governmental Panel on Climate Change predicted that by the year 2100, global temperatures will rise by 4.0 degree Celsius and sea levels will increase by 59 cm. Singapore has taken a pro-active role in conserving the environment via judiciously calibrating legislations to reduce energy usage in all sectors. The building sector consumes about a third of Singapore’s total electricity production. In January 2005, the Building Construction Authority (BCA) launched an initiative called the Green Mark Incentive Scheme (GMIS) with the primary aim of promoting energy efﬁciency in buildings. Buildings that are designed to achieve efﬁciency in the use of energy and water, provide a healthy indoor living environment and minimise wastage of natural resources are termed ‘‘green’’ buildings. [2]. In Singapore, energy performance standards are constantly being revised to take advantage of advances in materials and technology to improve envelope and building system performance. As can be seen by the major milestones on building energy

* Corresponding author. Tel.: þ65 6516 2558; fax: þ65 779 1459. E-mail address: [email protected] (K.J. Chua). 0360-5442/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.energy.2009.10.039

performance and standards development shown in Table 1, Singapore’s building standards follow a gradual evolutionary path beginning in 1979. Over the years, standards and codes of practice have been revised and mandated by law with successive revisions followed by public seminars to demonstrate beneﬁts and teach compliance procedures. It was only in 2004 that the fully enhanced Envelope Thermal Transfer Value (ETTV), applied to non-residential buildings, came into force. In 2006, a major effort to revise the collection of codes of practice relating to building services and equipment was completed. With the increasing use of air-conditioning in these buildings, it became necessary to review and enhance the current regulatory control to include these non airconditioned buildings. This is to ensure that their building envelope designs are suitable to be operated under air-conditioned environment. The ETTVres was introduced in 2008. According to Hamza and Greenwood [3], building energy model has become a major part of the building bidding contractual documents. Contractors’ bid for a project will be rejected if the building energy model does not demonstrate compliance to energy standards. In Singapore, new energy standards are constantly being revised and enforced at building plan approval stage through the submission of ETTV calculation drafts to the BCA. Residential buildings in Singapore are increasingly being air-conditioned as they experience larger amount of external heat gain due to global warming effects [4]. Therefore, there is a need to limit the heat gain into building space via its envelope. While extensive research has been conducted to develop energy estimating methodologies and

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Table 1 Major milestones on building energy performance and standards development. Year

Milestones

1979

Prescriptive Standards incorporated in the Building Control Regulations Launch of CP 24 (Energy Efﬁciency Standard Building Services and Equipment) Revision of OTTV criterion Compliance of CPs 13 (Mechanical Ventilation and Air-Conditioning in Buildings), 24 and 38 (Artiﬁcial Lightings in Buildings) Commencement of BCA-funded project on energy efﬁciency index for non-residential buildings Launch of ETTV and completion of EEI project Launch of SS530-Energy Efﬁciency Standard for Building Services and Equipment

1982,1983 (revised 1999) Mid 80s 1999

2001 2004 2006

tools for large commercial buildings, few studies are known to have been made for residential buildings. While there is a mandated ETTV limit for commercial buildings, there is none for residential buildings. Therefore, this work seeks to establish the thermal performance of residential building envelopes and understand the key parameters that affect residential building energy performance when air-conditioning is used in them. The need to promote energy efﬁciency coupled with the need to understand the impact of envelopes on air-conditioned residential building energy consumption justify our efforts to establish an ETTV methodology for residential buildings in Singapore. For residential buildings, a portion of electricity is consumed to operate the heating, ventilation and air-conditioning (HVAC) system. In a tropical climate such as that of Singapore, where it is practically summer all year round, only cooling is needed. Electricity is also consumed by other equipment such as lighting and vertical transportation such as lifts. The estimation of energy consumption due to heating and cooling equipment is more difﬁcult because of the complex interaction between the cooling load and the air-conditioning cooling system. Moreover, this interaction is subjected to the dynamic inﬂuences of parameters such as occupancy patterns, building operating schedules and climatic conditions. Many computer software packages have been developed to estimate the energy characteristics of a building. Earlier works have extended the overall thermal transfer value (OTTV) concept to correlate the OTTV of building envelope designs with other key building design parameters through DOE-2 computer simulations [5–7]. Yik and Wan [8] conducted an evaluation of the appropriateness of using OTTV to regulate energy performance of air-conditioned buildings. Major deﬁciencies of OTTV have been discussed in their work. Data were also presented to demonstrate the fact that OTTV may not truly reﬂect the thermal performance of building envelope because of several inherent deﬁciencies [8]. A major review of the OTTV formula was carried out to derive a new formula that could provide a more accurate indicator of the thermal performance of building envelope [8]. In this present work, we present the ETTV parameter. ETTV presents a more accurate measure of the thermal performance of building envelope. The basic difference between OTTV and ETTV is the relative contributions of the three components of heat gain through the building envelope [9]. The ETTV expression provides a truer annual value of heat transmission and therefore lends itself readily to energy calculations. The ETTV value, unlike the OTTV, has a direct correspondence to heat gain through the envelope and provides a better feel of the measures implemented to reduce heat gain. The present work is driven by the need to develop a set of parameters that can operate as an energy label for the building. The ETTV addresses that need for an envelope label and the ETTV-Ec combination accomplishes that task for a whole building energy performance label. The

methodology adopted with Singapore’s ETTV was to re-assess the accuracy of the old OTTV equation with appropriate coefﬁcients so as to be useful as an input parameter for energy estimation. The criteria for accuracy are: (1) that the ETTV, as an energy label, is capable to correlating the heat gain through the building envelope; (ii) that the heat gain is proportionately reﬂected by the ETTV value; (iii) that the new ETTV can be used an input parameter in the energy estimation equations, Ec. In Singapore, DOE-2.1E is used extensively in the energy analysis of buildings. A quick energy estimate program, Building Energy Standards X (BEST X), has been developed by the National University of Singapore (NUS) in close consultation with the BCA, professional engineers and architects. It is a design tool that can be employed by engineers, architects and building services professionals to achieve compliance with prescriptive and energy performance standards relating to air-conditioned buildings [10]. Although large residential building blocks present a broad set of challenges in implementing energy saving measures, they also bring about enormous potential opportunities. Residential buildings allow for economies of scale in energy audits and implementation. Energy savings can be appreciable and substantial. In the pursuit of green residential buildings with high energy efﬁciency, many questions arise. How does one residential building design compare with another in terms of minimising external heat load? What are the important building design parameters that signiﬁcantly impact residential building energy efﬁciencies? Can there be a simple methodology for analysing potential energy saving strategies for residential buildings? The present work intends to address these issues for buildings in climates requiring air-conditioning. Therefore, the speciﬁc objectives of this work are: (i) to determine, through computer simulations, the ETTV equation and its respective coefﬁcients for residential buildings; (ii) to investigate the effects of building aspect ratio and orientation on heat gain for two types of residential buildings, namely, the multistorey point block and multi-storey slab block; and (iii) to study the extent of inﬂuence shading effects due to opaque walls and corridors has on heat gain. 2. Methodology According to Chow and Chan [11], the use of a single energy performance index like OTTV alone cannot ensure energy efﬁcient and cost effective building designs; the HVAC systems and equipment, building energy management as other energy saving options should also be considered. The following sections present two energy performance indices, namely, ETTV and Ec. One accounts for the performance of envelopes to reduce solar heat gain while the other accounts for the energy consumption of the air-conditioning system in the building. The following section outlines the methodology employed to evaluate the energy performance of buildings. 2.1. Envelope thermal transfer value (ETTV) ETTV is a measure of the average heat gain into a building through its envelopes. It was reﬁned from the original OTTV equation, which did not accurately account for the relative components of heat gain. Chou and Lee [12] and Chou and Chang [13] ﬁrst mooted the idea of developing a more accurate building index that takes into account three heat gain components through the building envelope, namely, heat conduction through opaque walls, heat conduction through windows and solar radiation through windows. Thus, the ETTV equation takes into consideration three key components that contribute signiﬁcantly to the heat gain through the building envelope. In commercial practice, averaging the sum of hourly internal loads due to occupants, lights and

K.J. Chua, S.K. Chou / Energy 35 (2010) 667–678

equipment over the speciﬁc operating hours of the building will yield the building’s internal load, Qint. And by averaging the annual sum of the building envelope loads due to wall conduction, window conduction and window solar radiation over the speciﬁc operating hours of the building will yield the average envelope load, Qenv. However, this may not be the case for residential buildings as it is reasonable to expect that a residential building operates, with certain diversity, throughout the whole year, in contrast to commercial building operation. Furthermore, unlike commercial buildings where cooling takes place during the day, residential building air-conditioning patterns are controlled solely by its occupants, and air-conditioning takes place mostly at night. Therefore, the cooling load seen by the air-conditioning equipment at night in residential buildings is signiﬁcantly lower than the average day time load. The ETTV correlation is particularly suited to buildings experiencing tropical climates where outdoor-indoor temperature difference and diurnal variations of temperature are relatively small. The ETTV formula is thus presented as

ETTV ¼ TDeq ð1 WWRÞUw þ DTðWWRÞUf þ SFðWWRÞðCFÞðSCÞ

(1)

where TDeq is equivalent temperature difference ( C), DT is the temperature difference ( C), SF is the solar factor (W/m2), WWR is Window-to-wall ratio, Uw is the thermal transmittance of opaque wall (W/m2 K), Uf is the thermal transmittance of fenestration (W/ m2 K), CF is the solar correction factor for fenestration and SC is the shading coefﬁcients of fenestration. The coefﬁcients TDeq, DT and SF vary according to the weather of the locality of interest. These coefﬁcients are determined using computer simulations using the particular local weather ﬁle. Coefﬁcients for each particular heat gain component can be obtained using the following three equations as proposed by Chou and Chang [13].

P TDeq ð1 WWRÞðUw Þ ¼

DTðWWRÞ Uf

1 year

annual operating hours A P

¼

1 year

1 year

(2)

Qwin;cond

annual operating hours A P

SFðWWRÞðSCÞ ¼

Qwall;cond

(3)

Qwin;rad

annual operating hours A

(4)

Equations (2), (3) and (4) account for the heat conduction through the walls, the heat conduction through the windows and the solar radiation through the windows, respectively. Using Singapore’s weather data consolidated for a particular year, the three coefﬁcients can be derived from performing several multi-parametric simulations on two residential building types.

2.2. Annual cooling energy consumption (Ec) The annual cooling energy consumption, Ec, is deﬁned as the annual electrical energy consumption of the air-conditioning system. This includes chillers, cooling towers, AHUs (Air Handling Units) and other miscellaneous pumping equipment. The annual cooling energy can be estimated using the modiﬁed degree-day method for cooling formulated earlier by Chou et al. [14]. The present method of estimating energy, invoking the ETTV, is by way of estimating the cooling degree days for the speciﬁc location. If heating is necessary, the heating degree-day parameter may be

669

incorporated in place of the cooling degree-day parameter to estimate the energy consumption. The resulting equation for Ec can be expressed as

Ec ¼

ðcÞðQd Þð24ÞðDÞðaÞðbÞ DtðCOPÞn

(5)

Further work by Chou and Chang [15] has led to a simpliﬁcation of the modiﬁed cooling degree-day equation (6); resulting in the development of a new expression incorporating the ETTV. The simpliﬁed cooling energy estimating equation is thus written as

Ec ¼

gðETTVÞAð24ÞðDÞðaÞðbÞ DtðCOPÞn

(6)

where a and b are diversity factors of the operating hours in a day and the operating days in a week, respectively. Thus,

a ¼

operating hours of a building in a day 24

(7)

operating days of a building in a week 7

(8)

and

b¼

The cooling degree-day is deﬁned as the difference between the daily mean outdoor temperature and reference temperature and is expressed as:

D ¼

n X

Tm Tref

o

C:day

(9)

i¼1

Equation (6) can also be re-cast in the following form:

i h a $b ln D24$D$ t$Ec =ðcQd Þ n ¼ ln½COP

(10)

Equation (10) can be further re-arranged to obtain the values of n (the part-load factor of the cooling plant) and Dt (design indooroutdoor temperature difference) from the multi-parametric simulation data by making use of the approximate linear relationship between the two values. The resultant expression is given as:

Ec ð24ÞDab ¼ n lnðCOPÞ þ ln ln Dt cQd

(11)

Equation (11) produces a linear plot and the values of n and Dt can then be directly extracted from the gradient and y-intercept, respectively. In determining the cooling load of the building, the correlation factor, g, is deﬁned as the ratio of the annual cooling load to envelope heat gains, thus,

g¼

ðcÞðQd Þ total cooling load ðcÞðQd Þ ¼ ¼ Qenv ðETTVÞðAÞ ðETTVÞðAÞ

(12)

where the total cooling load ¼ Qenv þ Qint þ Qmisc and Qenv is the average cooling load due to envelope heat gains (W); Qint is the average internal load due to occupants, lights and equipment (W); and Qmisc is the average miscellaneous load due to inﬁltration, roof and ground heat gains (W). Both Qenv and Qmisc are affected by changes in weather as well as building operation schedules. Therefore, Qint, which can be easily calculated, is chosen as an independent variable. Thus, equation (12) is re-arranged as

g ¼ Bþ

ð1 þ MÞQint ðETTVÞðAÞ

(13)

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where B is the cooling load due to envelope gains/(ETTV$A) and M is given by Qmisc/Qint. Chou and Chang [15] also found that the ratio M shares a linear relation with

M ¼

18

in equation (13). Therefore, M, in terms of

16

is expressed as

14

Qmisc ðETTVÞðAÞ ¼ a$ þb Qint Qint

(14)

Finally, using equations (16) and (17), Chou and Chang [13] Qint ðETTVÞðAÞ,

expressed g as a linear function of

g ¼ ðB þ aÞ þ ð1 þ bÞ

Q int ðETTVÞðAÞ

given by

Households

ðETTVÞðAÞ , Qint

ðETTVÞðAÞ Qint

Week day air-con operation 20

12 10 8 6

(15)

where B is the cooling load due to envelope heat gains/(ETTV$A) and, a and b are constants. The value of average internal load, Qint, is required to enable the calculation of g using equation (15). In order to estimate Qint, we used the Cooling Load Factor (CLF) Method speciﬁed in the ASHRAE Handbook of Fundamentals [16]. Qint is the sum of three components, namely, Qocc (average cooling load due to heat gain from occupants), Qlight (average cooling load due to heat gain from lights), and Qequip (average cooling load due to heat gain from equipment). 2.3. Simulation program eQuest is a building energy analysis tool that allows users to perform detailed comparative analysis of building designs and technologies. It is equipped with a simulation engine that is derived from the latest version of DOE-2 which is a fully validated [17] and ASHRAE qualiﬁed [18] computer program that predicts the hourly energy use and energy cost of a building when information such as the hourly weather information, building model descriptions and its HVAC equipment and utility rate structure are provided. DOE-2 has undergone rigorous validations and results from a latest validation work has shown that it is able to predict energy usage in ten buildings to within 5% of mean biased error between measured and computed monthly energy use in buildings [19]. eQuest is further equipped with the capacity to create virtual built-environment, in which the operations of the HVAC system and the lighting of the facility can be studied [20]. In addition, it is tooled with an easy-to-use graphic interface CAD platform for users to construct their reference buildings. In short, eQuest is a building energy analysis tool which produces high quality results by integrating a building creation wizard, an energy efﬁciency measure wizard and a graphical results display module with an enhanced DOE-2 simulation program [21]. Despite its versatility, one speciﬁc limitation of the present eQuest version is the difﬁculty in obtaining dimensions of imported CAD building drawings. Therefore, information related to building dimensions needs to be provided by the CAD originator. In this paper, eQuest was adopted to produce ETTV and Ec correlations and to study energy performance of residential buildings in Singapore. 2.4. Residential air-conditioning proﬁle To obtain a base-case air-conditioning usage pattern in a typical residential household, a pilot household survey has been conducted with the help of BCA. Information obtained from some 100 households include: apartment type and size, number of people living in that household, and more importantly, air-conditioning usage schedules for weekdays and weekends, and equipment type. As can be seen from Fig. 1, most households turn on their air-

4 2 0 <7pm

7pm

8pm

9pm

10pm

11pm

12am

>12am

Time Fig. 1. Air-conditioning pattern for residential buildings during weekdays.

conditioning at night; most notably from 9pm to 11pm weekdays. For both weekends and weekdays, air-conditioning is usually operated at night, with most households turning on between 9pm to 11pm. In addition, most people turn off their air-conditioning during weekdays and weekends between 6am and 8am. 2.5. Simulation parameters To obtain the residential ETTV coefﬁcients and energy estimation correlations, energy performance simulations with local climatic data were conducted. The weather ﬁle is a compilation of typical climatic data ranging from dry-bulb and wet-bulb temperatures, wind velocities, cloudiness and hourly values of direct and diffuse radiation for all building operating hours of the year. The data in the weather ﬁle indicated that in Singapore, the typical drybulb temperature lies between 25 and 32 C on an average day, with relative humidity at about 95% in the morning and reducing to 75% in the late afternoon. The weather ﬁle also showed that cloud cover is signiﬁcant, resulting in an average diffuse component of the solar radiation of about 40% of the total radiation on a typical day while on clear days, this ratio is about 20%. The Window-to-Wall ratio (WWR) of buildings represents the ratio of the fenestration area to the total wall façade. Fifty multiparametric simulations have been carried out on each of the reference base-case buildings with lighting, equipment and occupancy schedules typical of that in a generic residential building. The envelope parameters that were varied in each simulation are shown in Table 2. The range of the envelope components of the building covered some of the common combinations of building construction materials and glass types used in the construction of residential buildings in Singapore. The shading coefﬁcient (SC) can be deﬁned as the ratio of the solar heat gain through fenestration under standard conditions to the solar gain through a single pane of

Table 2 Ranges of envelope parameters used. Parameters

Ranges

Uw (W/m2 K) Uf (W/m2 K) SC WWR LIT (W/m2) LTS

1.2–4.5 2.2–6.17 0.2–0.9 0.2–0.51 2.69–5.11 0.5–0.8

K.J. Chua, S.K. Chou / Energy 35 (2010) 667–678

2.6. Point and slab block descriptions

Table 3 Ranges of system parameters used. Parameters

Description

System type Cooling set point Operation Timing

Split unit 24 C Weekdays: 2200 h–0700 h Weekends: 2200 h–0800 h A family of four

Number of occupants in an apartment Windows COP

671

Opened when A/C is off, closed when A/C is on 2.0–3.0

a reference double strength sheet glass under the same conditions [13]. The windows were designed to be ﬂushed with the walls, meaning there is no window setback. In addition to envelope load and internal load parameters, air-conditioning system settings and internal makeup of the generic residential buildings used in the simulations are laid out in Table 3.

The reference generic buildings used for computer simulations are 12-storey point block and slab block, modelled after actual residential buildings in Singapore. The ﬂoor plan of a generic point block building is presented in Fig. 2. The reference case building of a point block is of symmetrical cross section and its four facades face the North, South, East and West orientations. It has a ﬂoor-to-ﬂoor height of 3.2 m. One storey houses four apartments, each with 144 m2 or 1552 ft2 of space. In each apartment, up to two bedrooms can be air-conditioned. This provides a measure of diversity with regard to air-conditioned space within each household. The bigger bedroom occupies a space of 36 m2 while the smaller bedroom occupies 16 m2. To make it more realistic, a common non-air-conditioned corridor adjacent to the apartments was also modelled. The ﬂoor plan is identical for each of the building’s 12 storeys. Due to the shape of the point block, some surfaces are shaded by the building itself.

Fig. 2. (a) Two-dimensional geometry of a generic point block building; and (b) three-dimensional geometry of a generic point block building.

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The ﬂoor plan of a generic slab block building is presented in Fig. 3. The reference building of a slab block has a rectangular ﬂoor plan, generally with individual apartments along the length of the block. The reference building is oriented in the East-West direction. Like the point block, each storey comprises four apartments, each with 144 m2 or 1552 ft2 of space, and the ﬂoorto-ﬂoor height is 3.2 m. The generic makeup within each apartment is the same as the point block. There are two airconditioned bedrooms, with the bigger bedroom occupying a space of 36 m2 while the smaller bedroom occupies 16 m2. A common non-air-conditioned corridor runs the length of the block. The corridor is 1.8 m wide and acts as an overhang to the façade of the ﬂoor below it. Residential slab blocks usually have such common corridors that provide some form of shading to one façade of the building. The ﬂoor plan is identical for each of the building’s 12 storeys.

3. Results and discussion 3.1. Residential building load distribution Fig. 4 portrays the different load distribution for a private residential point block. It shows that the envelope loads account for approximately 64% of the total cooling load, with solar heat gain through windows accounting for 35%. For a residential building, the predominant factor governing the magnitude of cooling load is heat gain through the envelope. Loads contributed by people, lights and equipment are internal to the building and are directly governed by occupants. Whereas, envelope related loads are design related and can vary from one building to another having the same occupancy rate. This study on the residential ETTV seeks to characterise the building envelope thermal performance so as to derive a quantitative test of the envelope design that limits heat transmission into the space.

Fig. 3. (a) Two-dimensional geometry of a generic slab block building; and (b) three-dimensional geometry of a generic slab block building.

K.J. Chua, S.K. Chou / Energy 35 (2010) 667–678

673

Residential building load distribution Load due to infiltration

Uwall value of 4.5 W/m2K

19%

17%

Uglass value of 4.5 W/m2K 10%

Qwall Qglass Qsolar Qothers Qinfil

19%

Includes loads due to: > Lighting > People > Miscellaneous equipment > Roof

35% WWR = 51%

Average SC value of 0.4

Fig. 4. Distribution of cooling loads in a residential building.

3.2. Developing the ETTV for residential buildings (ETTVres)

3.3. Cooling load correlations

As mentioned earlier, unlike commercial buildings where cooling takes place during the day, residential building air-conditioning patterns are controlled solely by its occupants, and airconditioning takes place mostly at night. Furthermore, the cooling load seen by the air-conditioning equipment at night in residential buildings is signiﬁcantly lower than the average day time load. These factors inﬂuence the choice of the normalizing factor in computing of the coefﬁcients of the ETTV equation. In determining the coefﬁcients for the residential ETTV equation or ETTVres, the envelope loads were generated in order to determine the coefﬁcients at a cooling set point of 24 C. The values of TDeq, Dt and SF for each of the two types of residential building were evaluated by using Equations (2), (3) and (4) and the coefﬁcients calculated for each building type and tabulated as shown in Table 4. Comparing the building types, it can be seen that when orientated along its length in the East–West direction, a slab block building yielded higher coefﬁcient values on a per metre square basis than a symmetrical point block building, despite having window shading due to common corridors along one side of its length. This seems to imply that the effects of orientation and building aspect ratio play quite a signiﬁcant role in heat gain through the envelope. To facilitate the submission of residential ETTV calculations to BCA and to standardise the ETTVres equation, we propose an averaging of the two sets of coefﬁcients. The proposed residential ETTV equation is thus:

The cooling load correlating equations have been generated for both types of residential buildings. The load correlations were derived from the eQuest database output. These load correlations were then used in the energy estimating equation to predict annual cooling energy consumption of residential buildings. In determining the load correlations, the annual sum of internal loads, envelope loads and all hourly cooling loads were generated in order to determine the cooling load correlation factor, g. The different internal load components were extracted from the loads report generated by eQuest. Extracting the relevant results from each simulation and averaging them over annual operating hours, the graphs of g (or cQd/(ETTV$A)) against Qint/(ETTV$A) for residential point block and slab block were plotted and are shown in Fig. 5a and b, respectively. Inspecting the data points in these ﬁgures, we observe the strong linear correlations between these two parameters for both residential types.

ETTVres ¼ 3:4ð1 WWRÞUw þ 1:3ðWWRÞUf

Heat Conductionwall

Heat Conductionglass

þ 58:6ðWWRÞCðCFÞðSCÞ

Solar Radiation=Heat Retentionglass

(16)

Table 4 TDeq, Dt and SF for various residential building types. Building type

TDeq ( C)

Dt ( C)

SF

Point block Slab block

3.31 3.56

1.21 1.32

56.12 61.05

3.4. Relationship between ETTVres and cooling energy In earlier simulations, we prescribed the cooling capacity for each split unit instead of allowing eQuest to auto-size its capacity according to calculated loads. However, based on the feedback from BCA, ﬁxing the cooling capacity may lead to the air-con capacity being oversized. Therefore, a separate study was conducted to look into the differences between prescribed capacities for HVAC and allowing the simulations to auto-size the air-conditioning equipment based on cooling load requirements. To investigate the differences in annual cooling energy Ec and ETTVres brought about by auto-sizing, a set comprising 100 parametric simulation runs for each building type was performed. The parameters employed are shown in Table 5. Using these values, results were generated and illustrated in plots of Ec against ETTVres for both ﬁxed capacity and auto-sizing as shown in Fig. 6. This ﬁgure suggests that there was an appreciable difference in Ec between the ﬁxed capacity method and auto-sizing. Interestingly, Ec demonstrated a strong linear correlation with ETTVres. Therefore, ETTVres, by itself could be a stand-alone parameter for appreciating the energy consumption pattern in buildings linked to envelope heat gains.

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a

c.Qd/Qenv Vs. Qint/Qenv 2.500

c.Qd/(ETTV.A)

2.000 y = 2.0675x + 1.0174 R2 = 0.9931

1.500

1.000

0.500

0.000 0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

Qint/(ETTV.A)

b

c.Qd/Qenv Vs. Qint/Qenv 3.000

2.500

c.Qd/(ETTV.A)

y = 2.0682x + 1.0175 R2 = 0.9934

2.000

1.500

1.000

0.500

0.000 0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

Qint/(ETTV.A) Fig. 5. (a) Graph of g (or cQd/(ETTV$A)) against Qint/(ETTV$A) for point block; and (b) graph of g (or cQd/(ETTV$A)) against Qint/(ETTV$A) for slab block.

It is now appropriate to discuss the potential energy savings based on the equations displayed in Fig. 6a and b. At about the midpoint ETTVres range of 20 W/m2, it is readily observed that for slab block, a unit decrease in ETTVres resulted in 3.5% decrease in annual cooling energy. Correspondingly, for point block, a unit decrease in ETTVres gave rise to 4% decrease in annual cooling energy. Based on the results obtained, an ETTV cut-off at 25 W/m2 was recommended in the immediate and near term for residential buildings in Singapore. From the simulation, this value stood in the upper range of ETTVres of residential buildings. This is well within the capability of most public residential buildings as a large number does not have high WWRs. A cut-off at 20 W/m2 may be envisaged in the future phase/longer term. This tightening can be achieved with continued application of modest WWR and improved building envelope material properties. For longer term gains, the ETTVres cut-off could be reduced further in a later phase to take advantage of improved envelope systems and designs. In addition, the following approaches may be adopted to reduce, in varying degree, the ETTV of a residential building: (i) reducing glass shading coefﬁcient; (ii) reducing wall absorptance; (iii) increasing wall resistance; (iv) reducing U-value of fenestration; (v) applying appropriate wall cladding; (vi) implement external shading devices; (vii) applying solar ﬁlm; and (viii) employing self shading in building design.

In some of the above approaches such as in wall and glazing resistance and cladding, materials are major players contributing to signiﬁcant beneﬁcial effects. Architectural aspects that exploit the natural elements of the location and designing to take advantage of self shading and orientation of the building are of equal importance in helping to reduce heat transmission through the building envelope. The ETTVres is now embodied in the GMIS. Presently, all buildings approved for development are required to be certiﬁed to a minimum standard stipulated as basic. The basic certiﬁcation requires that residential building development, whether public housing or private condominium, to comply with the minimum Table 5 Ranges of parameters used in auto-sizing simulations. Parameters

Ranges

Uw (W/m2 K) Uf (W/m2 K) SC WWR LIT (W/m2) LTS Cooling capacity

1.2–4.5 2.2–6.17 0.2–0.9 0.2–0.51 4.31 0.7 22,000 Btu/hr for Bedroom 1 & 15,000 Btu/hr for Bedroom 2

K.J. Chua, S.K. Chou / Energy 35 (2010) 667–678

a

675

Graph of Cooling Energy Vs ETTVres 400

Cooling Energy / MWh

350 300

y = 9.3914x + 91.386 R2 = 0.8454

250

y = 9.4755x + 62.296 R2 = 0.8823

200 150 100 50 0 0

5

10

15

20

25

30

2

ETTVres (W/m ) Fixed Capacity

b

Auto Sizing

Linear (Fixed Capacity)

Linear (Auto Sizing)

Graph of Cooling Energy Vs ETTVres

350

Cooling Energy /MWh

300 250

y = 7.5146x + 83.963 R2 = 0.9482

200

y = 6.4907x + 62.12 R2 = 0.9833

150 100 50 0 0

5

10

15

20

25

30

35

2

ETTVres (W/m ) Fixed Capacity

Auto Sizing

Linear (auto Sizing)

Linear (Fixed Capacity)

Fig. 6. (a) Graph of Ec versus ETTVres for point block; and (b) graph of Ec versus ETTVres for slab block.

ETTVres stipulated as 25 W/m2. Incentives are offered to designers and developers whose buildings exceed the minimum standard and can demonstrate additional savings against prescriptive standards governed by existing applicable codes or practice. Estimation of Ec and comparing it against the energy budget calculated from compliance with prescriptive standards provide the basis of compliance for energy efﬁciency. Higher up the ranking of energy efﬁcient buildings in the GMIS are Green Mark certiﬁed Gold and Platinum awards with cash grants provided at design and postcommissioning stages. In these higher awards, both the ETTVres and the energy consumption reduction against prescriptive standards need to be demonstrated. Energy modeling is required to show that the building being designed will be able to achieve the minimum stipulated energy reductions in order to qualify for the cash grants. The ETTVres criterion can be revised as buildings become more energy efﬁcient, measured by their energy index (W/m2/year) at the design and post occupancy stage. Revision will need to take into consideration the availability of energy efﬁcient alternatives such as envelope systems and components, and the progression of

design innovations and alternatives brought about and incentivized by the GMIS. 3.5. Part-load performance In the methodology to correlate energy consumption to ETTVres, we deﬁne a part-load factor, n, in Equation (10). With the values of cQd (total cooling load) found from loads correlations, Ec/cQd values can be evaluated and averaged so that an average value of Ec/cQd is determined for each COP value. The values of n and Dt for that particular building type can be found by plotting the natural logarithm of the average values of Ec/cQd against the natural logarithm of the corresponding COP values. A strong linear relationship was observed to exist between the two terms whereby the n and Dt values can be deduced from the gradient and the y-intercept, respectively, as shown earlier in equation (11). From equation (11), the values of n and Dt were obtained from the plots shown in Fig. 7a and b. The values of n and Dt are presented in Table 6.

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K.J. Chua, S.K. Chou / Energy 35 (2010) 667–678

a

ln[Ec/(cQd)] Vs ln(COP) 7.45 7.40 7.35

ln[Ec/(cQd)]

7.30 7.25

y = -0.9134x + 8.0579 R2 = 0.9964

7.20 7.15 7.10 7.05 7.00 0.500

0.600

0.700

0.800

0.900

1.000

1.100

1.200

ln(COP) ln[Ec/(cQd)] Vs ln(COP)

b

7.35 7.30 7.25

ln[Ec/(cQd)]

7.20 7.15 7.10

y = -0.9086x + 7.922 R2 = 0.9993

7.05 7.00 6.95 6.90 0.500

0.600

0.700

0.800

0.900

1.000

1.100

1.200

ln(COP) Fig. 7. (a) Graph of ln[Ec/(cQd)] versus ln(COP) for point block; and (b) graph of ln[Ec/(cQd)] versus ln(COP) for slab block.

residential buildings, given that every household in the building operates with the same schedule and equipment level.

3.6. Predictability of Ec correlation Validation of the simpliﬁed cooling degree-day equation was conducted by calculating Ec values via equation (6) and data gathered in load correlations. The values of n and Dt employed in this equation are shown in Table 6. The estimated energy consumption obtained from equation (6) was compared with energy consumption obtained from computer simulation. The ﬁrst ten sets of results for both types of buildings are shown in Table 7 and Table 8. The difference between predicted results and full scale simulation was generally less than 10%. The ability of the simpliﬁed cooling degreeday equation in predicting the Ec value in comparison to detailed simulation runs facilitates the energy consumption prediction of Table 6 Values of n & Dt for different residential buildings. Building Type

n

Dt

Point Block Slab Block

0.9134 0.9086

8.074 9.249

3.7. Sensitivity analysis tools for ETTVres and Ec Computations of ETTVres as well as Ec involve a large number of parameters and are fairly complicated processes. When more than one parameter needs to be changed, the impact on energy consumption may not always be accurately found by a simple sum of the energy changes due to each parameter. Some parameters have stronger inﬂuence on energy or the ETTVres than others. The construction of the multi-parametric analysis tool was based on the methods described by Turiel et al. [22], which proposed the use of the Taylor series expansion methodology to enable making predictions due to small changes in the selected parameters. The formulation for both ETTVres and Ec requires a selection of parameters of inﬂuence. For ETTVres, the key parameters are SC, WWR, Uw, Uf and as. For Ec the key parameters are COP, LIT, OA, LTS, and Tc plus an additional parameter in the form of the ETTVres, since an

K.J. Chua, S.K. Chou / Energy 35 (2010) 667–678

residential buildings has resulted in conclusive answers that demonstrate possible energy savings pegged to the ETTV. Building designers can now appreciate the relative impacts of the different parameters on the ETTV and thus the cooling energy consumption of the building. Using computer simulation, two types of residential buildings were modelled and parametric runs performed. An ETTV equation for residential buildings was formulated. We have shown that the ETTVres correlates well with the energy consumption of a large scale residential building. A set of simple energy and load estimating equations were developed using computer simulation and local climatic data. These equations provide a means of estimating the annual cooling energy consumption from calculations on the building envelope loads as well as the internal loads of the building. The relationship between Ec and ETTVres displayed a strong linear correlation. Also, from the equation of the line, we demonstrated that with every unit decrease in ETTVres, the annual cooling energy for was reduced by approximately 4% for point block. For a slab block, the rate of decrease was approximately 3.5% per unit decrease in ETTVres. A recommendation for the ETTVres cut-off value was presented. A cut-off at 25 W/m2 was recommended in the immediate and near term. For longer term gains, the ETTVres cut-off may be further reduced to take advantage of improved envelope systems and designs. Finally, an energy estimating tool was developed to study the impacts on the two indicators, ETTVres and Ec, due to variations in the key building parameters. The tool will prove useful to engineers and building services professionals during design and energy auditing exercises.

Table 7 Comparison of Ec results for point block. Data set

Ec (MWh)

Ec by simpliﬁed equation

% Difference

1 2 3 4 5 6 7 8 9 10

205.5 254.5 310.7 241.1 301.7 173.2 167.8 205.8 332.1 318.1

225.4 265.7 314.0 250.1 296.1 187.3 164.9 212.8 320.6 316.9

9.7 4.4 1.0 3.7 1.8 8.2 1.7 3.4 3.4 0.4

Table 8 Comparison of Ec results for slab block. Data set

Ec (MWh)

Ec by simpliﬁed equation

% Difference

1 2 3 4 5 6 7 8 9 10

175.4 214.5 252.4 200.6 243.7 153.8 134.9 169.9 263.8 264.1

182.7 216.3 255.7 203.7 240.6 153.1 135.1 171.9 256.6 256.3

4.2 0.8 1.3 1.6 1.3 0.5 0.2 1.2 2.7 2.9

increase in ETTV will directly affect the building cooling loads and cooling energy. To accurately predict the respective changes in ETTVres and Ec, a third-order Taylor series expression was employed. The third-order expression (for ETTVres) is given as:

677

DETTVres ¼ ETTVres Px0 þ u; Py0 þ v; Pz0 þ w ETTVres Px0 ; Py0 ; Pz0 ¼ u

vETTVres vETTVres vETTVres þv þw vPx vPy vPz

! 2 2 1 v2 ETTVres v2 ETTVres v2 ETTVres v2 ETTVres 2 v ETTVres 2 v ETTVres þ uv þ u2 þ v þ w þ vw þ uw 2 2 2 2 vPx vPy vPy vPz vPx vPz vPx vPy vPz ! 1 v3 ETTVres v3 ETTVres v3 ETTVres uvw v3 ETTVres 1 2 v3 ETTVres v3 ETTVres þ þ u3 þ v3 þ w3 þ þ v2 w u v 3 3 3 2 vP vP vP 6 2 3 x y z vPx vPy vPz vPx vPy vP2y vPz ! v3 ETTVres v3 ETTVres v3 ETTVres v3 ETTVres þu2 w þ uv2 þ vw2 þ uw2 2 2 2 vPx vPz vPx vPy vPy vPz vPx vP2z

A total of 24 equations were required to evaluate the partial derivatives shown above. Several hundreds simulations were performed to extract the respective coefﬁcients in the Taylor series expansion equations. A similar approach was adopted for Ec. The predictions made using the tools were done by the comparison of ETTVres and Ec values that have been obtained by running simulations of various parameter changes against values obtained from altering the parameters using the tools. The largest error for the ETTV analysis tool was 2.2%, while the largest error for the Ec tool was 5.2%. The detailed methodology involved in developing the analysis tools can be found in Chou et al. [23].

4. Conclusions The present work covered several key aspects regarding the energy performance of buildings, which were characterized by the ETTV and energy consumption, Ec. The central objective of focusing our attention on the ETTV and the extension of that to

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Acknowledgements The authors would like to acknowledge Mr. Liew Hing Tho, Mr. Ko Jing Rong, Mr. Oh Guo Long and Mr. Lim Jin Liang for their assistance in conducting part of the simulations presented in this work. Nomenclature

Area of the space or a group of spaces (m2) As A Total building envelope area (m2) AF Area factor adjustment for the space (-) BCA Building and Construction Authority c Load factor (-) COP Coefﬁcient of performance of the chiller at design point (-) D Number of 18.3 C-based degree-days in a year ( C.days) Annual cooling energy consumption (MWh) Ec Total annual energy consumption(MWh) Etotal Elighting Annual lighting energy consumption (MWh)

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Eequip Annual equipment energy consumption (MWh) ETTV Envelope thermal transfer value(W/m2) ETTVres Envelope thermal transfer value for residential buildings (W/m2) Fraction of equipment used in the j-th space and i-th hour fequip,ij of the year (-) Fraction of the light being used in the j-th space and i-th flight,ij hour of the year (-) Average fraction of occupants in the building (-) Focc Fraction of the maximum number of people in the j-th focc space (-) Total heat gain per person (W) HGp ILP AInterior Lighting Power Allowance (W) IRPA Interior Receptacle Power Allowance (W) LIT Lighting power intensity (W/m2) LPB Lighting power density of the space (W) LTS Heat of light-to-space ratio (-) n Correction factor for part-load performance of chiller (-) NOP Maximum number of people in the space in at that hour (-) OA Outdoor air requirement (cmh/m2) OTTV Overall thermal transfer value (W/m2) Px,Py,Pz Parameters in Taylor series expansion (-) Q Average cooling load due to heat gain (W) Design space cooling load (W) Qd RPB Receptacle power of the space (W) SC Shading coefﬁcient of fenestration SF Solar factor for vertical window (W/m2) Temperature set point ( C) Tc Equivalent temperature different for opaque walls ( C) TDeq Dt Design indoor–outdoor temperature difference (K) DT Temperature difference of outdoor and indoor condition for window (K) u,v,w Coefﬁcients in Taylor series expansion (-) Overall thermal transmittance coefﬁcient for the Uf fenestration (W/m2 K) Overall thermal transmittance coefﬁcient of the wall Uw (W/m2 K) WWR Window-to-wall ratio (-) Greek Symbols a Day diversity factor of building’s operation b Week diversity factor of building’s operation g Correlation factor for average space cooling load gp Correlation factor for peak space cooling load l Fraction of equipment being used in that hour

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