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Lots of research es have shown that the optimization of building envelope reduces building energy consumption during its lifecycle. Due to the uncertainty of the relationship between individual design parameters and building performance, the extent of impact cannot be well-understood. Therefore, it is essential to evaluate the impact extent for different design parameters and identify the one (s) that impact (s) more to the building performance and hence focus so as to improve building performance efficiently. In the present research, main design parameters that affect the building performance are selected to analyse the extent of the impact. Material quantities are extracted directly from the Building Information Modelling (BIM) model so as to calculate the embodied energy in material. Moreover, simulation of energy consumption is run for different scenarios during operation stage. Energy embodied in typical construction materials are calculated for each scenario accordingly. Finally, sensitivity analysis is applied to find the extent of impact on life cycle energy of building for the selected design parameters in terms of both embodied energy (EE) and operational energy (OE). A case study of a manufactory plant is carried out to investigate the impact of the selected design parameters.

The optimization of building envelope often involved the analysis of multi-de- sign parameters (elements of building envelope) together to investigate the optimal design scheme that can achieve both cost-saving and reduce energy consumption. The traditional optimization process is time-consuming and complicated since it is generated by the interactions between different design parameters. Besides, previous studies [

In addition, due to the complexity of building nature and occupants behaviour, the relationship between design parameters and building performance is often uncertain and if the uncertainty of impact factors can be well-understood, the building performance can easily be improved by carefully choosing important design parameter and hence focus [

The objectives of this paper are: 1) to investigate how the thermal performance of each envelop element impacts building energy consumptions; 2) to measure the correlation degree between embodied energy and operational energy in building for each envelop element; and 3) to identify which envelop element has the most influential impact on life cycle energy of building by performing sensitivity analysis.

Over the last years, there are great numbers of research focused on the building envelope optimization. Many measures have been proposed and assessed from the view of economic, energy efficiency and resource conservation. Pikas et al. [^{2}. The proposed building envelope design optimization strategy by Liu et al. has reduced the lifecycle cost and lifecycle carbon emission of building by 29.83% and 30.44%, respectively [

From literature review, lowering U-value of building envelope can reduce the heat losses during winter and heat gains during summer efficiently, as a result reduce energy consumption during building operational stage. Therefore, a summary can be drawn: the thermal properties of building envelope are correlated with building energy used in the later operational stage: the lower the U-value, the less amount of operational energy required. However, the production of building envelope elements with better thermal performance often involved energy-intensive construction processes. Hence, the additional energy required needs to consider along with the lifecycle of building to determine the true benefit from the optimization of building envelope.

Embodied energy (EE) in construction material and operational energy (OE) are the two major lifecycle energy (LCE) of building and the total of these two energy can constitute up to 90% of LCE demand with other LCEs being insignificant (maintenance and demolition energy), therefore, great deal of effort have been carried out to focus on the reduction of EE and OE during the lifecycle of building. EE is the total energy required for the processes of construction stage such as building material production, transportation and assembly onsite. While OE represents the energy used during operational stage of building to meet various requirements such as heating, cooling, ventilation, water heating and electrical appliances (such as lighting, escalator, etc. [

As mentioned, building envelope plays an essential role to lower energy consumption of building and it is mainly associated with both construction and operational stages in lifecycle of building. During construction stage, the production of building envelope material (brick, cement, steel and etc.) represents the main contributor to EE in building. The EE of building envelope can constitute up to 45% of LCE demand in building, therefore careful selection of building envelope materials with consideration of their EE is crucial for minimizing EE in building and hence, reduce LCE of building as well [

Previous studies often revealed that EE of a building was small as compared to OE across its lifecycle [

In the optimization of building envelope, EE of element are considerably increased while increasing the material consumption to satisfy the target thermal properties and reduce OE in buildings. Therefore, in order to reduce the total LCE in building, EE needs to be considered along with OE. Considering various relative thermal properties of different materials and the proportion of envelope elements within building yield different energy demand during operational stage, sensitivity analysis was proposed as a tool in this research to measure the correlation degree between EE of building envelope and OE of building and also used to understand the uncertainty between thermal properties of envelope elements and EE of building envelope and its impacts on building performance.

The research methodology, as shown in

According to literatures, thermal properties of external building envelope, i.e. wall, windows and roof, are the most influential factors affecting energy consumption in buildings during operational stage since the heating and cooling loads in buildings are transferred externally through building envelope from exterior environment [^{2}∙K) which is the amount of heat energy transfer through a building envelope element. In addition, window to wall ratio (WWR) has significant impact on building energy consumption for HVAC system as windows and walls contribute to heat gain and loss in building, either from sun or through conduction, and the amount of heat gain or loss is dependent on the area and thermal properties of windows and walls. Therefore, WWR is also included as design parameter in this research.

The base model of a building was created by using BIM tools. The building energy consumption baseline (OE and EE) was calibrated by the design parameters and properties of base model building. The EE of base building envelope and OE of base model from energy analysis are used as energy baseline values for comparison in the SA. The embodied energy (EE) associated with building envelope elements is estimated based on energy coefficient, density and volume of the elements. ^{3}) by the density of element (kg/m^{3}) and the energy coefficient of element (MJ/kg), the EE of building envelope element (MJ) is obtained.

The OE of building is dependent on the type of building, the building properties, such as density of people, occupancy schedule during weekday and weekend, infiltration flow and equipment load (computer, lighting, etc.) and these

Construction Materials | Energy coefficients (MJ/kg) |
---|---|

Bricks | 3.00 |

Cement | 4.60 |

Concrete | 0.95 |

Glass | 15.00 |

Timber | 8.50 |

properties are defined in the model. These building properties are used to calculate the internal heat gain in building and subsequently compute the energy consumption for equipment and HVAC system to maintain thermal comfort within the building [

To perform SA, the design parameters were varied in the model from baseline value within certain range and interval to create different scenarios, while other design parameters were fixed to its base model value.

To simplify SA process and to understand the individual impact of thermal mass on U-value exclusively, the insulation for walls and roof are ignored in this research and merely focused on the thermal mass of element, therefore the considered factor impacts U-value of elements is the thermal properties of material. By changing the thickness of the wall and the roof to meet corresponding U-values, different scenarios are created. Depending on glazing type and the number of glazing layer, the U-value of windows was modified by varying the thickness of glass plane, which is the traditional variable of windows [

R 1 + R 2 + ⋯ + R n = n t g K g + ( n − 1 ) ( t a K a ) = 1 U (1)

where t_{g} is the thickness of glass (mm); K_{g} is the thermal conductivity of glass (W/m K); t_{a} is the air space of inert gas (mm); K_{a} is the thermal conductivity of inert gas (W/m K); n is the number of glazing layer. In this research, the glazing type of windows was kept to be the same when varying the thickness of glass plane to ignore the impact of glazing type on U-value of windows. According to literatures, for glazing with two or more layer, the ratio of air space between

Design parameters | Typical range | GB50189-2015 recommended maximum value | |
---|---|---|---|

U-value (W/m^{2}∙K) | Wall | 0.37 - 1.95 | 0.5 |

Window | 1.70 - 6.00 | 3.5 | |

Roof | 0.18 - 3.00 | 0.8 | |

WWR | 0.10 - 1.00 | - |

glass plane (t_{a}) and thickness of glass (t_{g}) was generally to be 2:1 [

t a = 2 t g (2)

Based on this assumption, the required thickness for glass (t_{g}) can then be solved by Equation 3 with the known properties of glazing and inert gas between glass plane to achieve target U-value:

t g = 1 ( U ) ( n K g + ( n − 1 ) ( 2 K a ) ) (3)

In this research, different window to wall ratios (WWR) are to be examined to study their impact on building energy consumption. The typical WWR is range from 0.10 - 1.00, as shown in _{wd}) to wall area (A_{w}). The target WWR can be achieved by changing the size and number of windows. In this research, the latter was used as the approach to vary WWR.

Sensitivity coefficient (SC) and standardized regression coefficient (SRC) were selected as local sensitivity indicator and global sensitivity indicator respectively to measure the sensitivity of different design parameters on OE of building and its energy-efficiency. SC has been used frequently in the field of statistics and was used only for local sensitivity analysis (LSA) in this research, which varying one design parameter at one time while other parameters kept being constant. It is used to measure the impact degree of each design parameter value on the building energy used and it is a dimensionless value that defined as ratio of output value changes (building energy used) to input value variation (design parameter). If SA involves only one step change, the change is calculated with respect to base model value and SC can be calculated by Equation 4. If the input parameter has more than one data set, SC can be determined from the gradient slope of the data set which also can demonstrate its correlation [

SC = ( E f − E i ) ( D P f − D P i ) (4)

where SC is the sensitivity coefficient; DP_{f} is the design parameter value; DP_{i} is the base value of design parameter; E_{f} is the output value when the design parameter is DP_{f}; E_{i} is the output value of base model.

Since the design parameter was varied within certain range and interval, the input parameter would then has more than one data, therefore the graphs of building energy used respect to each input design parameter were plotted and obtained the corresponding gradient slope. The obtained values were compared to study the relative importance and correlation of design parameter to the output results (OE of building). The latter was measured and presented as coefficient of determination (R^{2}). Similarly, the correlation degree of OE and EE can be measured by plotting the graph of OE versus EE and determined from the gradient slope of the graph. Higher SC value indicates that the design parameter is more important and sensitive [

For Global Sensitivity Analysis (GSA), the correlation degree between input and output was evaluated by varying more than one input parameters simultaneously. SRC is used as the quantitative measure of GSA in this research through linear regression approach [

y ( x ) = b 0 + ∑ i = 1 n b i x i (5)

where y is the output value, b_{i} is the linear regression coefficient and x is the input value. Then, the SRC can then be calculated by standardizing the linear regression coefficient by the standard deviation ( σ ) of input and output values:

SRC = b i σ ( x ) σ ( y ) (6)

Positive SRC value means that the input parameter has positive impact on the output result, which increasing the parameter would lead to an increase of the dependent output result; negative SRC means that with the increase in the parameter, the output result is decreased [

The case study building is a 3-storey industry currently constructing in SuZhou. The 3D model of a manufacturing building was created by using Autodesk Revit. While the building energy analysis was performed by Energy Analysis (EA) add-in tool in Revit, which links the design feature of the model to analysis feature of Green Building Studio (GBS). The energy simulation performed in GBS uses DOE2 simulation program to run building model simulation with the purpose of producing sustainable and energy efficiency design in the earlier stage [

The energy settings were defined in Revit, which includes building type, ground plane, location and other parameters. These parameters were fixed constant except the design parameters of interest to ensure the reliability of output results. By changing the design parameters within certain range and interval as shown in

Material | Thermal conductivity (W/m K) | Energy coefficient (MJ/kg) | Density (kg/m^{3}) |
---|---|---|---|

Light weight concrete | 0.209 | 0.95 | 950 |

Autoclaved aerated concrete | 0.210 | 3.50 | 580 |

Glass | 1.100 | 15.00 | 2480 |

Air | 0.025 | - | - |

of building envelope elements considered to calculate the embodied energy and required thickness of element to achieve target U-value.

The thermal conductivity and density of material were extracted from Autodesk Material Library which is a CIBSE standard system library. From these data, the corresponding total embodied energy of walls, roof and windows in the base model were then calculated to be 1757.32 GJ. The calculated value is validated through the study of [

As shown in

These findings are in agreed with the study of [

building envelope can be priority consideration over other building envelope elements. This is due to the roof has larger area than the walls and windows in this case study; therefore it is reasonable that the influence of roof U-value is greater than walls and windows.

Performing SA on building envelope is to provide information to facilitate designer in making better decision on prioritizing the optimization target in selecting efficient building envelope design [

envelope in this research as compared to other elements; therefore, the impact of window’s U-value variation on EE of building envelope is insignificant. This also explained the graph trend of EE versus WWR in

Based on these facts, this research proposed to perform SA based on EE instead of design parameters to determine the impact of EE variation due to thermal properties change on the OE of building and relative importance of envelope elements in term of LCE. EE based LSA is performed by calculating EE of building envelope corresponding to the design parameters and comparing the output OE of building on the basis of EE.

tionship of design parameters, which is the sensitivity of OE with respect to change in EE of building envelope resulted from the variation of design parameter. It can be seen that the OE-EE relationship of windows has steepest gradient slope, followed by roof and walls. This means that OE of building is most sensitive to the variation of EE due to U-value of windows changes. Steeper slope indicates minor increase in EE can lead to significant decrease in OE used in building as compared to those of gentler slope. Although the material of windows have highest energy coefficient among other parameters (

Traditionally, the OE-EE relationship is in decreasing trend as in

This phenomenon is attributed to the variation of thermal load in building with WWR when U-value of walls and windows changed. In the scenarios of lowering U-value of windows (Line 4 in

has compensate the increasing solar heat gain with WWR, therefore the cooling load required is also reduced. As a result, the overall OE required with increasing WWR is in a decreasing trend. While for the scenarios with higher U-value of windows (Line 2 in

GSA was also performed to investigate the sensitivity of design parameters based on EE. Therefore, the corresponding EE value respect to WWR and U-value variation was calculated and as plotted in

From the calculated EE values, GSA was interpreted as the sensitivity of OE with respect to change in EE of building envelope by transforming the relationship between OE and WWR in

The findings from

Scenario | OE-EE relationship | |
---|---|---|

U-value of Windows | U-value of Walls | |

Higher | Base value | Decreasing trend |

Lower | Base value | Increasing trend |

Base value | Base value | No trend |

Base value | Higher | No trend |

Base value | Higher | No trend |

ing trend (Line 4 in

GSA was performed on total 62 scenarios with the variation of WWR, U-value of windows and U-value of walls together. Standard Regression Coefficient (SRC) was chosen as the sensitivity indicator for GSA in this research due to its ability to provide the information of model behaviour and it is useful to understand the strength of the correlation degree between input and output of a linear model [

The sensitivity results are as shown in ^{2}∙K) have positive SRC value, i.e. OE is reduced when EE decreased (WWR increased), and the degree of influence is varied slightly with increasing U-value of walls. This means that during WWR variation, thermal properties of walls have insignificant impact on OE-EE relationship of WWR and lowering U-value of windows is the most influential energy- efficient practice that brings positive impact to the overall LCE of building. In contrast, the OE-EE relationships of WWR in the scenarios with higher U-value of windows have negative SRC value and the highest value was observed when U-value of walls is lower. However, the practices in scenario with negative SRC value are not of interest in this research since these practices are less energy-

efficient and should not be the priority during optimization process. The findings from EE based LSA and GSA of OE with respect to the variation of WWR, U-value of windows and U-value of walls are summarized as: 1) OE of building is most sensitive to the variation of EE due to U-value of windows changes and least sensitive to the variation of EE due to U-value of walls changes; 2) Thermal properties of walls have insignificant impact on OE-EE relationship of WWR whereas changing thermal properties of windows affects the OE-EE relationship behavior or WWR; 3) Lowering U-value of windows brings positive impact to the OE-EE relationship of WWR; and 4) Increase U-value of windows brings negative impact to the OE-EE relationship of WWR. Based on these findings, it was concluded that targeting on thermal properties of windows when varying WWR brings noticeable impact to both EE of building envelope and OE of building. Therefore, the suggestions for designer in selecting efficient building envelope designs are summarized as: 1) Minimize U-value of windows and maximize U-value of walls during design stage; 2) Minimize U-value of windows when consider increasing the WWR of building; and 3) Maximize U-value of windows when consider decreasing the WWR of building.

In this research, the impact of building envelope elements, including walls, windows and roof, on building energy consumption was investigated with the aid of BIM tools as platform and the envelope element that has the most influential impact on building energy consumption was investigated by performing SA on a case study of manufactory plant in Suzhou. The design parameter in this research focused on the thermal properties of envelope element and WWR. Due to the envelope elements have their distinct thermal properties and comprised of different proportions in building envelope, the SA was proposed to carry out based on EE instead of design parameters, which measure and compare the correlation degree between EE and OE in building for each envelope element. Throughout EE based SA, the uncertainties between thermal properties and EE of building envelope were revealed, subsequently the resulted OE-EE relationships of envelope element were then compared to understand the relative important of envelope element on building performance in term of EE. The outcomes of this research provide information to facilitate designer to make better decision on prioritizing the optimization target in selecting efficient building envelope design.

Zhang, C. and Ong, L.J. (2017) Sensitivity Analysis of Building Envelop Elements Impact on Energy Consumptions Using BIM. Open Journal of Civil Engineering, 7, 488-508. https://doi.org/10.4236/ojce.2017.73033