Applied Mechanics and Materials Vol. 606 (2014) pp 265-269 © (2014) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMM.606.265
Application of Statistical Taguchi Method to Optimize Main Elements in the Residential Buildings in Malaysia Based Energy Consumption Seyed Mojib Zahraee1, a*, Milad Hatami1, b, Ali Asghar Bavafa 2, c, Kambiz Ghafourian3, d, Jafri Mohd Rohani 1, e 1
Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Skudai, Malaysia 2
Faculty of Civil Engineering, Universiti Teknologi Malaysia, Skudai, Malaysia
3
Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia, Kuala Lumpur, Malaysia *a
[email protected],
[email protected],
[email protected] , d e
[email protected],
[email protected]
Keywords: Energy Consumption, Residential Building, Taguchi Method, BIM Application, Energy Efficiency
Abstract. Energy consumption is one of the controversial issues in the world. Rapid growing world energy consumption has already increased concern about the supply problems, heavy environmental effects such as climate change. One of the most users of energy is residential buildings that consume the biggest share of energy. Growth in population, rising demand for buildings together cause the upward trend in energy consumption. Therefore, energy efficiency in buildings plays a significant role to decrease the environmental effect. The goal of this paper is optimizing the main elements which are window, ceiling, and wall by considering the effect of uncontrollable factors such as humidity, temperature, and air flow in residential buildings using statistical method, namely Taguchi method. A two-storey house in Malaysia was selected to simulate by means of Building Information Modeling (BIM) application. Based on the result, the optimum energy saving will be achieved when the type of material which are used for wall, ceiling, and window are Brick Plaster, Acoustic Tile Suspended, and Single Glazed Alum Frame, respectively. Introduction Today energy consumption is one of the controversial issues in the world. The rapid growing world energy consumption has already increased concern about the supply problems, heavy environmental effects such as global warming, climate change. One of the most users of energy is residential buildings that consume the biggest share of energy. Growth in population, rising demand for buildings together causes to increase the upward trend in energy consumption. Therefore, energy efficiency in buildings plays a significant role to decrease the environmental effect. Some investigation has been done to improving the energy efficiency in buildings [1]. Haberl et al. [1] worked on energy saving in 132 commercial building in US. They applied the simulation optimization model to enhance the saving energy in residential buildings. It was shown up to 77% saving energy at those building could be achieved from control system. Another study used the Visual DOE4 software to evaluate the energy usage of a five storey office building in the humid and hot climate in Saudi Arabia. The results showed that increasing the insulation thickness does not have any significant effect on energy efficiency [2]. One investigation was done in Singapore on passive climate control in residential buildings that are ventilated naturally. The thermal analysis software (TAS) was applied to study the influence of some microclimatic criteria on minimization of the heat [3]. Wong and Li [4] attempted to examine the influence of various conditions of roof, wall and floor materials on the cooling energy. They claimed that light-weight wall caused to increase 16% saving in cooling energy as well as using concrete roof tiles with a white painted steel resulted in 5.8 % energy saving.Yuan. Moreover , E´rika Mata et al. [5] Built the bottom-up model All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of TTP, www.ttp.net. (ID: 91.133.200.63-22/07/14,10:06:19)
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based on 1400 building in Sweden with twelve energy saving measures (ESMs) based on data that released from 2005. Base on the result applying ESMs would enhance saving energy with decrease Carbon Dioxide emission as well as reduce energy demand. In this paper the statistical Taguchi method is applied to optimize energy saving by evaluating the main element factors that are window, wall and ceiling as well as considering the effect of uncontrollable factors. Methodology Simulation Model Computer simulation has been widely used in different areas such as construction process manufacturing systems and Port container terminal [6-8]. In this paper , in order to analyze the energy consumption of the building used as a case study, the Revit Architecture software was applied to simulate the building .This software is one of the most useful simulation tools for BIM software. The specifications were re-assigned to Ecotect and a final sketch-up was imported to Energy Plus software. To establish a baseline for the energy consumed by a typical house, the characteristics of materials used in initial model were defined in the software. In order to achieve the most accurate analysis, variables such as the type of building, the orientation of the building and climatic data for the location of the building were essential. The final simulation model was applied to run the experiments in different conditions of the Taguchi design to obtain the responses for analyzing. Taguchi Method There are various methods used for improving the quality in variety of industries. Taguchi method is one of the best optimization techniques to achieve high quality without increasing cost. It is a simple, systematic and powerful method to increase the quality [9, 10]. Taguchi method was introduced by Dr Genichi Taguchi in Japan. The main objective of the Taguchi method is decreasing the effects of noise factors as well as determining the optimum level of the main controllable factors by considering the Taguchi’s robust design [11]. Orthogonal array (OA) designs for allocating the chosen factors for experiment were applied by Taguchi. The most useful (OA) designs are L8, L16 and L18. The orthogonal array is applied for calculating the main and interaction effects by running the minimum number of experiments [12]. Taguchi method applies the signal-to-noise rate (SNR) to minimize the effect of noise and optimize the process performance. In other words, the SNR is the response (output) of the experiment. In order to conduct the Taguchi method, different steps expressed as below should be followed [12]. Choosing Control and Noise Factors The selected controllable factors in this study are walls (A), ceiling (B), window (C). The selected noise factors are humidity (E), air flow (F) and temperature (G). The range or levels of main and noise factors are shown in Table 1. As can be seen, each controllable and noise factor has a low level (1) and high (2) level. Table 2 defines each level for the main factors. Selection of the Orthogonal Array Design In this paper three main factors were chosen with two levels so the L8 (OA) was used which indicated assignment of seven factor levels in two levels. Only 8 number experiments are required. So it is more cost effective in comparison to full factorial design of experiment [12]. Table 1. Main and noise factors and levels Main Factors Level
Noise Factors
Wall (A)
Ceiling (B)
Window (C)
Humidity (E)
Air flow (F)
Temperature (G)
Low level(1)
1
1
1
60
1
20
High level (2)
2
2
2
80
3
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Table 2. Types of Material for Each Level Element
Material of Lowest Cooling Load (1)
Reverse Brick Veneer R20 Plaster Insulation Suspended Double Glazed Timber Frame
Wall Ceiling Windows
Material of Highest Cooling Load (2)
Brick plaster Acoustic Tile suspended Single Glazed Alum Frame
Results and Discussion Performing Simulation Experiments and Data analysis After designing the experimental, the models and energy analysis were completed for different combinations. Table 3 shows the results of simulation experiments. Since the output response is a nonnegative value and is aimed at minimizing the cooling load, the calculation of SNR is based on the situation “Smaller is better” [12]. In this case, the formula below (Eq.1) is applied for calculating the SNR. In this formula n=number of values in each experimental conditions and Yi =each observed value. (1) Table 3. Analysis of the data produced by JMP software L4 OA (Noise Factor) L8 OA Controllable Factor Run
A
B
C
1
1
1
1
2
1
1
2
3
1
2
1
4
1
2
2
5
2
1
1
6
2
1
2
7
2
2
1
8
2
2
2
E F G
1 2 1
2 1 1
2 2 2
Mean
SN Ratio
220350
225206
216404
226332
222073
-106.93
232116
228140
223432
229120
228202
-107.16
227305
229335
233115
227300
229263.7
-107.20
226227
224125
225005
223305
224665.5
-107.03
241126
246125
255405
249451
248026.7
-107.89
240680
245260
243140
249400
244620
-107.77
240900
244255
245020
257325
246875
-107.85
223778
223020
229002
221120
224230
-107.01
235126.1
-107.35
1 1 2
Average
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Determining the Optimum Condition Table 4 showed the most significant factors affecting the output response based on the analyzing of mean and SN ratio are window, ceiling and wall respectively. In order to make decision about which factor setting or level is selected to maximize the energy saving, the SNR values at the both levels (High or Low) of each factor were compared. According to the main effect plots for mean and SNR as shown in Fig.1, the optimum conditions for the controllable factors will be achieved when all the factors placed on second level. It means that the type of material used for wall, ceiling and window should be Brick Plaster, Acoustic Tile Suspended and Single Glazed Alum Frame respectively.
Fig 1. Maximum desirability of energy saving Table 4.Effect and ranking of mean and SN ratio for each controllable factor Factors Levels Low level (1) High Level (2) Effect Ranking
Wall (A)
226051 240938 14887 3
-107.08 -107.63 -0.55 3
Ceiling (B)
235730 231259 -4471 2
-107.44 -107.28 0.16 2
Window (C)
236560 230429 -6134 1
-107.47 -107.25 0.22 1
Discussion Generally, building elements behave differently regarding energy performance depending on the location, climatic conditions and physical properties of the building. In other researches have been done before such as Wong Li [4] They proposed that using light-weight wall and concrete roof tiles with a white painted steel caused to increase 16% and 5.8 % saving in cooling energy . In this paper, building simulation and Taguchi method was applied for evaluating the performance of the walls, windows, ceilings, and air quality (temperature, humidity and air flow) in terms of their ability to reduce cooling loads in residential tropical buildings. The results of the simulation and energy analysis of our case study building revealed that the using Brick Plaster for wall , Acoustic Tile Suspended for ceiling and Single Glazed Alum Frame for window resulted in highest energy saving. Conclusion In the residential buildings there are many factors that have effect on the energy efficiency and saving such as location, climate condition and physical properties and etc. The goal of this paper is optimizing effect of the main elements which are window, ceiling and wall on energy saving by considering the effect of uncontrollable factors such as humidity, temperature and air flow using
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statistical Taguchi method. Based on the result, the optimum energy saving (0.49%) will be achieved when the type of material which are used for wall , ceiling and window to be Brick Plaster, Acoustic Tile Suspended and Single Glazed Alum Frame respectively. More study can be done by applying other statistical analysis such as response surface methodology to optimize the air quality factors in residential buildings. References [1] J.S.Haberl, M. Lui, J. Houcek and A. Athar David E. Claridge, Can You Achieve 150% of Predicted Retrofit Savings: Is It Time for Recommissioning? Washington, D.C,1994,Vol. 5, pp. 7387. [2] M. F. Saber Sabouri, M. Zain , M. Jamil , Exploring role of different floor, wall and roof details in energy efficiency of a bungalow house in Malaysia, Scientific Research and Essays, (2011) ,6(30) 6331-6345. [3] I. Iqbal, M.S. Al-Homoud Parametric analysis of alternative energy conservation measures in an office building in hot and humid climate. Building and Environment, (2007), 42(5) 2166-2177. [4] N.H. Wong, S. Li, A study of the effectiveness of passive climate control in naturally ventilated residential buildings in Singapore. Building and Environment, (2007), 42(3) 1395-1405. [5] E. Mata, A. Sasic Kalagasidis, F. Johnsson, Energy usage and technical potential for energy saving measures in the Swedish residential building stock, Energy Policy, (2013),55,404 414. [6] S.M. Zahraee , M. Hatami, N.M. Yusof, .M. Rohani, F. Ziaei,Combined Use of Design of Experiment and Computer Simulation for Resources Level Determination in Concrete Pouring Process, Jurnal Teknologi, (2013). Vol 64,No.1,pp.43-49. [7] S.M. Zahraee, M. Hatami, J. M. Rohani, H. Mihanzadeh, M.R. Haghighi, Comparison of Different Scenarios using Computer Simulation to Improve Manufacturing System Productivity: a Case Study, Advance Materials Research, (2014),Vol. 845, pp. 770-774. [8] A. Shahpanah, S. Poursafary , S. Shariatmadari, A. Gholamkhasi1, S. M. Zahraee, Optimization Waiting Time at Berthing Area of Port Container Terminal with Hybrid Genetic Algorithm (GA) and Artificial Neural Network (ANN),Advanced Materials Research،(2014)، Vol. 902، pp 431-436. [9] C. Chan, W. Hsu, C. Chang, C. Hsu, Preparation and characterization of gasochromic Pt/WO3 hydrogen sensor by using the Taguchi design method, Sensors and Actuators B: Chemical, 2010,145:691–7. [10] T. Tsai, Improving the fine-pitch stencil printing capability using the Taguchi method and Taguchi fuzzy-based model, Robot Comput Integr Manuf, 2011;27:808–17 [11] J. Antony, Some Key Things Industrial Engineers Should Know about Experimental Design, Logistic Information Management,1998, Vol.11, No.6, pp. 386-92. [12] J. Antony, F. Antony, “Teaching the Taguchi Method to Industrial Engineers” Work Study,( 2001) Vol. 50, No. 4, pp. 141 – 149.
