LOWERING INSIDE TEMPERATURE OF BUILDINGS BY AUTOMATIC OPTIMUM GENERATION

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Lowering Inside Temperature of Buildings by Automatic Optimum Generation

LOWERING INSIDE TEMPERATURE OF BUILDINGS BY

AUTOMATIC OPTIMUM GENERATION

Dirk Rilling, Ammar Al-Shalabi and Sree Ram A/L Apana Nayaranan

Multimedia University, Melaka Campus, Faculty of Engineering And Technology, Melaka, Malaysia

[email protected]

[email protected]

ABSTRACT:Inside temperature of buildings in Malaysia is greatly determined by local weather

conditions, i.e. the sun. For this paper the influence of the environment on inside temperature was

studied and also how an autonomous optimisation can help to find to meet the objective of lower

inside temperature, hence cooling load. Design parameters used are building orientation and

different insulation setups. Research was conducted on a standard Malaysian terrace house

model arranged in array form. EnergyPlus is set to work alongside with GenOpt in order to carry

out simulations and optimisations. The terrace house is designed with either the standard materials,

which are normally used in residential construction, and a highly energy efficient building material,

i.e aerated concrete block. The key is to find the ideal orientation that has lower inside temperatures.

Subsequently, the optimisation shows the relationship between building materials, orientation,

and inside temperature. The optimization started from viewing the lowest temperatures to optimized

orientations and finally selecting the orientation, which is feasible throughout the year. Every

month requires different orientation setting to suit the climate change, thus it is not possible to

have a house built for multiple orientations. It is found that aligning the row of terrace houses at

an orientation of 137.5 followed by 222.5 off North for the given model is best and will improve

thermal comfort in sense of lower inside temperature throughout the year for both insulation

setups. The average inside temperature is found to be 27.3 C and 25.9 C for standard Malaysianand highly efficient building materials, respectively. This renders to 0.153 kWh lower additional

need of auxiliary electrical power based on a comfort neutral temperature of 26 C per design day.

Keywords: Building Orientation, Thermal Optimisation, Tropical Climate, Design, Simulation

1. INTRODUCTION

The global demand for improvement in building designs has significantly encouraged many researchers

and students to pick up the challenge to contribute new design technologies to the developers. Some

of the many improvements needed come from performance consideration such as energy, comfort,

cost, environment impacts, etc. Energy efficiency has mostly been limited to South Korea, Japan,Singapore and Australia (GOLDSEA, 2006). Countries such as Malaysia are very new to design

concepts where proper simulation and optimization on a building is done before hand. Developers

generally concentrate on other aspects including cost.

Currently, Malaysia is growing at a high-speed pace and every developer is competing for a

share. The thought of constructing an optimized building has never been taken into consideration as

a result of the high costs involved. However, studies have shown to build high efficient building

would not cost more than 10% of conventional buildings construction and this highly depends on the

assertiveness of the design (DOE, 2006).

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SENVAR + ISESEE 2008:Humanity + Technology

The successful application of simulation and optimization tools for building design problems is

shown through works of Wright (Wright, 2002) and Gustafsson (Gustafsson, 2000). It is a multi-

dimensional solution space with few objectives or constraints and they do not have the same physicalmeaning.

The energy analysis done for a typical Malaysian terrace house in order to get the thermal

behavior by help of EnergyPlus software and local weather data of Kuala Lumpur (DOE, 2007).

Based on the results and experiences of earlier research findings which have been published (Rilling

2007, Rilling, 2008), the aim of this research is to figure out how autonomous optimization approach

can be utilized in oder to find points of thermal comfort with given design by computer based tools.

Hence, the main focus was on building fabric. Although the model is zoned according to different

usage, user pattern was not further investigated. As objective function, the orientation of the the

terrace house array is chosen. In addition, the aim is to show how to proceed with the objective

using one-objective optimization algorithm. The thermal comfort is represented by inside temperature

of center block of the terrace house array. Lower inside temperature leads to lower utilization of

auxiliary energy for air-conditioning and hence to lower emission of pollutants on the energy production

side.

2. METHODS

For the project purpose to optimize the terrace house arrangement, the software package EnergyPlus

is connected to generic optimization program GenOpt.

EnergyPlus (or E+) is a building energy simulation program for modeling building heating, cooling,

lighting, ventilating, and other energy flows (DOE, 2002). It is based on the most popular features

and capabilities of BLAST (Building Loads Analysis and System Thermodynamics) (Building Systems

Laboratory, 1999) and DOE-2 (Crawley, 2005). E+ provides the simulation engine but only

rudimentary graphical user interface features. DesignBuilder software fills this gap with an easy-to-

use solid modeler, which allows house and building models to be easily assembled in 3-D space by

providing a graphical user interface (GUI) for easy interaction (DBS 2008). The model then is

thermally zoned and transferred to the E+ program to do the simulation part; the results are displayed

and evaluated again in DesignBuilder. Data communication between DesignBuilder and E+ is based

on the IDF/IFC-files-standard (Bazjanac, 1999).

GenOpt as generic optimization program must be connected to other simulation program such

as SPARK, E+ or TRNSYS, in order to conduct an optimization of parameters from simulation

programs (Wetter, 2001). In GenOpt this is done by help of one-parametric and multi-parametricoptimization (Wetter, 2003).

The workflow in order to obtain optimized results regarding thermal comfort is done usually in a

manual way. Parameters are changed, the model simulated and the result evaluated in order to cut

down the dimension of solution space. The aim is to eventually arrive at a small, lower-dimensional

solution space, where the predicted results are within set limits. By coupling E+ and GenOpt, this is

transferred into a simulation-optimization approach where the result is found autonomously. Other

authors have came up with approaches of evolutionary programming (Fong, 2006), or using neuro-



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fuzzy algorithms (Lu, 2005) in order to solve energy optimization problems. GenOpt is chosen for

this project because it is developed for optimization of objective function from external simulation

programs, such as SPARK, E+ or TRNSYS.

2.1 Description of Case Study Building

The reference type of house design for this research is chosen according to data of the Valuation &

Property Services Department of Malaysia (Finance Ministry of Malaysia, 2005). It states that the

terrace house holds the biggest fraction of both existing supply and newly planned supply or residential

units in Malaysia.

The floor plan of a single storey terrace house used in this project was obtained online (iProperty,

2003). The living space is 120 m2 and the same for all calculations, with a height between floors of

2.5 m. The window ratio is 22% at the front side and 12% at the rear side of the unit, with an air

exchange rate of 0.5 ACH. For the simulation one unit is modeled, thermally zoned and then arrangedto an array in a way often seen in new land developing projects (see Figures 1 and 2).

For this type of housing design, which is kept the same for all simulation, two material setups

have been chosen. One which is typical and can be found in almost every terrace house, hence

called standard Malaysian. The other which is based on application of a highly energy-efficient

residential building, the so-called Cooltek house, located in the Tiara Melaka Golf and Country Club

(see Figure 3).

Its insulated walls are made of aerated concrete blocks and extra insulated floors, and double

glazing. The differences in setup can be seen in Table 1.

Figure 1: Floor Plan for Terrace House (left) and thermal zones



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Figure 2: Visualisation of the Terrace House Array with North Point in DesignBuilder.

Table 1: Material data for standard Malaysian and Cooltek setup (continues on next page)

Type Thickness/m U-Value/W/m2K

Malaysian Wall

Outermost layer: Concrete 0.01

Brickwork 0.12

Innermost layer: Concrete 0.01 2.962

Cooltek Wall

Outermost layer: External rendering 0.01

Aerated concrete slab 0.25

Innermost layer: Plaster (lightweight) 0.01 0.549

Malaysian/Un-insulated RoofOutermost layer: Clay tiles 0.025

Air gap 0.02

Innermost layer: Roofing felt 0.025 2.79

Cooltek Roof

Outermost layer: Zinc-Aluminum 0.0005

Innermost layer Steel 0.0048 6.364

Malaysian Window

Single-glazed clear 6.121

Cooltek Window

Double-Glazed clear 6 mm + Ar 1.499

Figure 3: The Cooltek House



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Objective is to find the orientation that contributes to lowest inside temperature in the terrace

house array for two different material setups. Hence, analysis for this research was done on the

house in the center of the array, block 6 (see Figure 2), because the result of the simulation can betransferred easily to other center units, whereas both ends of the array are considered similar to

semi-detached houses and hence not practical. It is noticed that highest heat transfer occurs at the

end units (units 1 and 11) due to larger side surface area relative to the front or rear side of the

block. For both end blocks orientation around 0 off North would be best to avoid direct sun light.

Figure 4 shows this effect: the black line arrow shows the sun moving from East to West and the the

thick lines at front and rear encircle the area of contact with the sun. Compromising and placing the

array at orientation of 0 would generally cause higher temperature throughout the row of houses

because the area which faces the sunlight is the entire row. For this reason, its not possible to

compromise the orientation for the end units. It can be concluded for this arrangement that

improvement of end units design regarding their cooling load should rather be done by additional

thermal insulation or changes in design. However, this is not the scope of this research.

2.2 Forming the Objective Function

In order to determine the optimal values of parameters, it is necessary to define the objective

function. In this case to get as close as possible to the comfort level expressed as inside air

temperature, which can be easily obtained from EnergyPlus?

GenOpt allows that only the first function defined in the initial file can be the objective function

to be minimized or maximized. The range of the values for optimized parameter is defined in the

Figure 4: Sunrise and sunset pattern over the row of terrace house aligned at 0 & 90



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command file (Wetter 2003). In a next step, the initial file is set to seek optimal orientation points.

This is the file GenOpt is using for the optimization process. Once GenOpt is loaded, this file is used

in the building simulation program EnergyPlus. GenOpt automatically creates new input files, callsEnergyPlus comparing the template file and the building file, replacing the numerical values at the

set parameters and runs the simulation. This process is repeated until a minimized objective value is

found, like shown in Figure 5. Based on the found inside air temperature in the center block, the

orientation of the whole array is adjusted to the value range set in the command file.

3. RESULTS AND DISCUSSION

Figure 6 shows the lowest inside temperatures for annual pattern for the above-described setups,

standard Malaysian and Cooltek. Optimization was done on first of every month. Temperaturefluctuations are mainly due to effects of sun movement. For weather data of Kuala Lumpur it was

evident that temperature differs to a maximum of 2 K.

Due to equinox occurring in late March and September, temperature at this month is recorded

highest for both standard Malaysian and Cooltek, at 28.3 C and 26.5 C respectively. Summer and

winter solstices occurs during June 21st and December 21st. This is during the time the sun moves

23.5 towards North or South from the equatorial belt. Therefore lowest temperatures are seen

during that time. Due to the location of Malaysia slightly away from the equatorial belt the effects

however are felt not directly during this time but slightly later in the following months. Hence, the

lowest temperatures are felt during January and July.

Figure 5: Interface between GenOpt and the simulation program



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The optimizations for the lowest temperature profiles in Figure 6 were not done on a weekly or

monthly basis because it is an optimization process to find lowest inside temperatures on annual

basis. A single day simulation and optimization would lead to find lowest temperature in that particular

day of that month. Whereas doing a weekly and monthly optimization would give almost no variation

or fluctuation in temperature. The temperatures found with each simulation cycle are with air

conditioning set to OFF to easily point out the influences of orientation to inside temperature.

It is noticed over the year that the Cooltek setup leads to higher comfort level, i.e. lower inside

temperature, with a difference up to inside

= 1.2 K compared to standard Malaysian, based on the

higher insulation standard. The lowest inside temperature for Cooltek was found to be 24.9 C,

compared to 26.1 C for standard Malaysian in month November. Compared the annual average

inside temperature: 25.8 C and 27.4 C for Cooltek and standard Malaysian, respectively, and its

deviation shows that Cooltek setup has not only lower inside temperature but also provides least

fluctuation of it by 1.8%.

The next step was to find out at what orientation lowest inside temperature for the terrace

house array may be found. Figure 7 shows optimized orientation points (angle in degree off North)

for standard Malaysian setup over a year. Optimized orientation is the way the row of terrace house

should be aligned. Placing this array of house in one of these optimized orientations will lead to

increased thermal comfort.

Figure 6: The lowest annual temperature points regardless of the orientation of each month

and with standard Malaysian and Cooltek setup; A/C off.



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It is seen that orientation points overlap from one month to another. In January for example four

optimized orientations were found. During the first and the last quarter, most optimized orientations

are seen in range of 220 to 310, for the rest of the year orientations are within 50 to 140. The

optimization shows that center block avoids sunrays as much as possible in order to keep inside

temperature low, as the sun moves from equinox to solstices. Only two optimized orientation are

found to have the same orientation throughout the year, which are 137.5 and 222.5. Both these

orientations do not render to lowest inside temperatures but are better choice whereas other points

would only give lower inside temperatures for certain months. For example, orientation points for

April at 84.9, 92.64 and 105.04 may have lower temperatures compared to 137.5 and 222.5 but

these points are not an ideal choice as it does not have a common orientation throughout the year.

Figure 8 show that Cooltek setup was found to have the same annual orientation pattern 137.5and 222.5. This indicates that the design, the architecture, is the determining factor for this type of

residential building. By comparing the Cooltek and the standard Malaysian setup it is clear that most

of the orientation points are about the same.

The following figure compares the optimal values obtained over the period of optimization. The

only two ideal values; 137.5 and 222.49 for both the materials used are plotted. Figure 9 shows the

comparison for both setups and both orientations. It clearly shows the both setups follow the same

pattern throughout the year with an inside temperature difference ofinside

2 K.

Figure 7: Optimized orientation points at lowest inside temperatures using standard Malaysian setup



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Figure 8: Optimized orientation points at lowest temperatures for Cooltek setup

Figure 9: Comparison between two best orientations, 137.5 and 222.5,

and inside temperatures for standard Malaysian and Cooltek setup



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Both setups are observed to rise and fall similarly throughout the year. During the 1st quarter

and the last month, 222.5 has noted lower inside temperatures for both the materials. Following up

the other months, April till November, orientation 137.5 proves to have lower temperatures. Bycomparing the number of months for a lower inside temperature sets 137.5 as the best choice. The

standard deviation and the coefficient of variation for both setups at 137.5 are lower compared to

222.5. However, the difference for both orientations is only marginal. To conclude, the best-optimized

orientation is the Cooltek at 137.5, with an average inside temperature of 25.9 C and subsequently

the standard Malaysian setup at 137.5 with average inside temperature of 27.3 C.

4. CONCLUSION

This project focuses on the utilization of automated optimization approach in order to lower the

inside temperature of a terrace house. During research one terrace house design with two differentinsulation setups was used. By help of simulation-optimization process, orientations were found

which lead to lowest inside temperature. This was done for the geographical location of Malaysia.

A normal workflow for getting knowledge about thermal building performance engineers have

to adjust parameters of building fabric or site characteristic in order to get optimized results. For the

indicated building orientation, normally 45 steps would be chosen and to see whether to find optimized

results within one turn, i.e. 360. This is time consuming and results may not be revealed by just

manual adjustments, like shown in (Rilling, 2007). With the used model simulation-optimization time

was half compared with a manual approach and, as the process implies, optimized orientations were

found. Hence the approach saved compute and operator (engineer) time which is an asset in order

to stay competitive.

The influence of orientation on cooling load, i.e. inside temperature was shown. Based on the

used model and the insulation setups a reduction of inside temperature of approximately 2 K was

found. To show the effect on lowering cooling load the found inside temperature in the model for

both setups is referred to the neutral temperature of comfort level, n. Hamdan (Hamdan et al.,

2007) has made and extensive study in comparing the comfort level in Malaysia and concludes

that neutral temperature is at n

= 26 C. With the width of the comfort zone taken to be 5 C

(Szokolay, 2004), thermal comfort temperatures extend approximately about 2.5 C above and

below the neutral temperature. Both setups at both orientations a well within this comfort zone

and the Cooltek setup slightly below n. If we assume this to be a wanted state of comfort,

additional energy has to be utilized to bring the standard Malaysian setup to this level. For the unit

investigated this would render toE= 545 kJ = 0.153 kWh per day. Extrapolated to a whole

developing site or the whole nation it certainly makes an impact. The positive outcome from

cooling load reduction is decreased energy usage in order to reach comfort level in the used

building model. This leads to less emission of CO2, amongst others,

on the production side. Burning

fossil fuels like crude oil and natural gas liquids predominantly does the supply of electrical energy

in Malaysia (EarthTrends, 2003). With an assumed power plant efficient of = 0.4 (AES, 2005;

Herrmann, 2005), the energy content of crude oil eOil

= 11.63 kWh/kgOil

, and the emission factor

for CO2 of crude oilE* = 0.27 kgCO2/kWh

Oil(Thames Valley Energy, 2006), the savings sum up

to 1066 kgOil

and 3350 kgCO2

, respectively, for a site of 100 units (whereas only the eight middle

units are acknowledged) and for one year.



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Where this has positive impact on the environment, stakeholders are more interested in the cost

side. In the end it is always left to the negotiations whether lower initial cost but a steeper slope of

cost function during building operation or higher initial cost but much lower slope bears advantage onthe long run. Very likely the cost for energy is rising over the time, so the latter is more reasonable

to choose. It has to kept also in mind that it may take up to two years of fine tuning until a building

reaches it maximum optimization potential during operation.

Designing a building should be done in a holistic approach. Building simulation as a tool can

show how different sub systems interact with each other and hence how the ecologic and economic

footprint will be. It should be stated that the result a closely bound to the given design hence the

results show that in the architecture of the house itself lays a higher potential of energy and thus cost

saving is incorporated. This is especially important since not always an optimized orientation can be

applied based in given site characteristics.

Further research will be done on multi-dimensional optimization in order to find further insight

about building characteristics.

ACKNOWLEDGEMENT

The authors would like to thank Mrs. Stephanie Bacon and Mr. Harry Boswell for sharing their

experience with design and operation of their home, the Cooltek house. Additional thanks goes to

netWORKED IDEE Building Consultants Sdn. Bhd. for their technological support and feedback.

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