Energy efficient of the residential buildings based climatic condition using experimental design : a case study in malaysia

In recent years, energy consumption has become a critical issue in the developed and developing countries. Residential buildings are one of the most users of energy in the construction sector that use the highest share of energy. This paper aims at evaluating the effect of four factors that are temperature, humidity, airflow and pressure on the cooling load in the residential buildings. To achieve this goal, statistical experimental design is used to determine the optimum setting of factors that result in optimum energy usage. Simulation software and energy analysis is used to simulate a two-storey building in Malaysia as the case of study. Final results showed that the temperature, humidity and interaction between them have the most significant effect on the energy cooling load. Moreover, to obtain the minimum value of cooling load the temperature and humidity should be equal to A=20 Celsius degree and B=60% respectively. In addition, the other two insignificant factors, airflow and pressure should be placed at the high level which are equal to C=3 cubic meters per hour, and D=6 Pascal (P) respectively.


Introduction
Energy optimization plays an important role in the new world.There is a global concern due to a probable lack of energy in the near future as well as some environmental effects like global warming [1].Energy is becoming more and more costly and greenhouse gas emission is the disastrous effect of global warming that threat the whole human life [1].One of the most cost-effective measures to minimize of carbon dioxide emission is to improve the energy efficiency of buildings [2].Hence, energy efficiency is a key factor that should has been considered as an effective solution [1].Among the widest range of energy consumers, residential buildings consume the largest amount of energy most of which is consumed by air conditioning systems in tropical countries [3].In tropical regions with naturally hot and humid weather, a high amount of energy has to be consumed to provide a tolerable environment.In Malaysia it is claimed that air conditioning and refrigerators consumed nearly 70% of the electricity in buildings [3].In this paper one residential building in Malaysia was simulated using Revit Architecture as a case study.The statistical method, Design of Experiments (DOE) was applied to assess the effect of four climatic factors that are temperature, humidity, airflow and pressure on the cooling load and energy saving.

Literature review
There are some investigations about the energy efficiency assessment in the residential buildings that are discussed here briefly.One case study in in the humid and hot climate in Saudi Arabia used Visual DOE4 software to assess the energy consumption of a five storey office building [4].The results indicated that increasing the insulation thickness does not have any considerable impact on energy efficiency.Another study in Singapore applied thermal analysis software (TAS) to investigate the effect of some microclimatic criteria on minimization of the heat in terms of passive climate control in residential buildings that are ventilated naturally [5].In another study in Saudi Arabia, the impact of three factor on energy saving was investigated.The results claimed that these factors can enhance the thermal comfort and energy usage [6].Recently, some investigations were done by using combined computer simulation and statistical analysis for enhancing the performance of construction process such as concrete pouring process and energy consumption in buildings [7,8].In one study, Experimental design (DOE) approach was implemented to find the best combination of some selected factors in a two-storey building which located in Malaysia.Final result indicated that changing ceilings and ceiling materials play a significant role to decrease energy consumption [3].Additionally, in another study, statistical Taguchi method was proposed to find the optimum value of the main parameters of buildings that are window, ceiling, and wall along with the effect of uncontrollable factors such as humidity, temperature, and air flow in residential buildings [9].In another research, building simulation and DOE were combined to evaluate the effect of main climate factors on energy saving and cooling load.Final result showed that the temperature and humidity have the most significant effect on the energy saving [10].Therefore, this paper aims at evaluating the effect of four factors that are temperature, humidity, airflow and pressure on the cooling load in the residential buildings.To achieve this goal, building simulation and Design of Experiments (DOE) is applied to find an optimum setting of factors by doing the sensitivity analysis.

Simulation model and experimental design
In this study, a two-story building separated into 2 uniform apartments (each level 1 unit) was selected Malaysia.Total building area is 676 m 2 .The house is separated into eleven zones with separate thermal properties for each level.Revit Architecture software is one of the most useful dynamic simulation tools that is used to simulate the building.In order to simulate, the CAD drawings are imported to Revit Architecture and simulated there by particular parametric design principles.The specifications were re-assigned to Ecotect software and a final sketch-up was imported to Energy Plus software.[11].In order to implement DOE, the following steps are followed.The steps are: 1. Selection of the factors and their levels, 2. choosing a response variable, 3. Determination of experimental design 4. Running the experiments 5. Analysis of results 6.Conclusion and recommendations.

Choosing factors and response variable
As mentioned earlier, four climatic factors were chosen to examine their effects on the cooling load in the selected case.Table 1 shows the variation range or level of factors is indicated that each factor has a high (+) and low (-) level.Cooling load was considered as a response variable.Due to small number of factors, full factorial design (2 n ) is used.In factorial design, all possible combinations of factors are considered in an experiment, which is replicated two times in order to decrease the error.In addition, three center points are considered to examine the curvature of experiment.responses.It should be noted that, whenever P-values are less than 0.05, they should be considered as a significant.In contrast, when P-values are more than 0.05, they should be assumed as an insignificant factor [11].Table 3 shows main effects of A (Temperature), B (Humidity) and two-way interaction (AB) are significant factors.The normal probability plot of effects shows that the significant factors are far from the line [11].Figure 1 shows main effects of A (Temperature), B (Humidity) and two-way interaction (AB) are significant factors.Pareto chart is also used to compare the sequence of the statistical significance of the both main and interaction effects from highest to lowest effects.The factors that exceed the reverence line are considered to be significant with the confident level of 95% (Figure 2).

Table 1 .
Factors and levels

4 Result and discussion 4.1 Performing simulation experiment Table 2 shows the result of 35 experiments that is run using the simulation software.Table 2 .
Result of simulation experiment

Table 3 .
ANOVA table for cooling load

Table 4 .
Estimated effects and coefficients for cooling load