2.2. Future Directions of BIM
Although building construction based on BIM has increased in past decades, green-building researchers and operators still need to improve their awareness regarding the application of BIM to a certain extent. The building construction industry realizes that there should be an urgent connection built between green-building and BIM. Namely, project phases, green attributes, and BIM attributes are three basic dimensions of the “Green BIM Triangle”; they are reflected in green building lifecycle contribution and application, distinct functions of environmental sustainability analysis, and the integration of green building assessments (GBA), respectively [
6].
Regarding the past research on BIM building simulations, the results of critical reviews and future perspectives on using BIM in green buildings are as follows: (1) BIM is a necessary modelling tool for the whole building lifecycle, especially for the cost, construction, facility, and operation management. Therefore, it increases various industries and stakeholders’ connection, communication, and cooperation when managing a green building; (2) The energy systems can be easier to analyse; (3) BIM applications supported the GBA in potentially in managing the documentation and assessments procedures.
2.4. Application in Building Energy Simulation
Regression-prediction and DOE-simulation were used in HVAC systems to predict annual building energy usage with a difference within 10% [
7]. In this research, to assess the dynamic energy interactions between external climate, building envelope, and HVAC systems, the computerized energy simulation was used as a technique. At the same time, this computerized simulator provided mathematical and statistical validation for the performance-based design and analysis of buildings, ensuring that the analysis and research work were on the right track.
From a probabilistic energy model that used an inverse analysis of parameter estimation, MLR is also used to deduce the distribution of parameters if the linear regression equation was established [
8].
In 2014, the researchers tended to use the software of DOE-2 simulation (eQUEST) with Monte Carlo simulation and regression equations to estimate building energy performance [
1]. It demonstrated a simple model to quantitatively analyze the energy consumption in a commercial building. This study investigated how the shapes of the building affect its energy consumption. MLR models were able to predict annual energy consumption through a parametric analysis. In its regression simulation, the impacts of 17 design variables were fit into seven different building shapes to predict the annual energy consumption. The first step of establishing the regression equation is to generate a large database through the study of several parameters, and then process the obtained data and establish the most appropriate regression equation with the mathematical model. In MLR simulation, the combined effects of interaction representation factors on dependent variables need to be considered [
1]. This is called correlation coefficient analysis.
2.5. Design Variables
In 2003, Pessenlehner and Mahdavi [
9] found that the shape and orientation of the building have a critical impact on thermal performance and energy consumption. Moreover, geometric factors are also closely related to the heating-cooling energy demand of a building. Building shape coefficient and passive volume ratio were proposed in 2005 by [
10] to be used as an index for studying energy optimization. In 2009, [
11] identified relative compactness, WWR, and glazing type as three key factors affecting building energy consumption. As regards building orientation, its impact on energy consumption depends on the geographical location of the construction and the solar trajectory [
12]. During the simulation, the orientation parameters allow the building to be rotated to cover all orientations, and the extent of energy optimization can be analysed based on a random sample of orientation parameters [
3].
The building envelope, as a barrier separating the interior of the building from the exterior, is a key factor in determining and controlling the quality of indoor conditions. The most significant components of the building envelope, such as walls, fenestration, roof, and thermal insulation, play a critical role in building energy optimization. Since the 1990s, many studies based on building energy simulations have been conducted to analyze the impact of the building envelope on energy consumption [
13]. In determining the design variables related to the building envelope, the detailed model of the building envelope developed by [
13] can be used as a reference. The variables most relevant to heat absorption and solar absorption should be selected according to preference. Furthermore, there have been significant advances in recent years in research on advanced and sustainable materials for the building envelope, so the range of design variables will be even wider [
14].
2.6. Recent Research on the Relationship between Building Parameters and Building Energy Performance
Table 1 summarizes the research on BIM and energy simulation in recent years. These articles provide inspiration for the methodology and simulation process of this study.
With the rapid development of urbanization, the issue of building energy and pollution has become a topic of research interest. There are a number of strategies that could be implemented to address this issue; and the green building is one of the potentially available methods. Generally, a green building is an environmentally friendly building that can not only decrease its passive influence but also provide positive effects on the environment during its life cycle [
25]. It is a critical step to choose the suitable building parameters, such as materials, envelope or inside structures, and the ratio of window to wall etc., for achieving a green building because those building parameters have a significant impact on the building's energy consumption. However, it is not easy to evaluate the energy impact of every single parameter of a building, since a building contains a large amount of information that needs to be considered.
BIM technology is considered a novel tool to promote the building analysis process. According to [
26], BIM is a visual, reusable, interactive building information technology based on business processes associated with construction projects.
Many researchers and scholars have used BIM as an energy analysis tool to identify the crucial building parameters which may affect building energy performance. Alothman and Ashour [
15] found that BIM could make an effective contribution to helping designers choose proper materials and components with better energy performance in the early design stages. In the research of [
15], Autodesk Revit 2018 and GBS were utilized as building modeling and energy consumption measuring tools for analyzing the energy performance effect of various building components. They found that wall and HVAC systems contribute the highest impact on energy performance (0.21% and 0.19%, respectively), while a low contribution (0.07% and 0.11%) comes from the orientation and the roof. Amani and Reza Soroush [
20] examined the utilization of BIM for evaluating the impact of the building components on building energy consumption in a mild climate zone. They then found that applying BIM to adjust the design parameters, which can affect building energy consumption, is an effective way to decrease energy cost, and the HVAC system usually contributes the most impact on building energy consumption. Khahro, Kumar [
16] also conducted an investigation to evaluate the positive effect of BIM on sustainable decision-making of the green building project. The questionnaire was designed to collect data on the benefits of BIM integration from construction sector practitioners; then SPSS version 24 was applied to analyze the data. In the last stage, they made a case study for energy analysis based on BIM and GBS to prove the data collected. They found that BIM and GBS can effectively provide optimized results by alternative trials in the early design stage for a green building. Unlike the studies above, Yakut and Esen [
17] evaluated how building design parameters affect the energy consumption in hot-humid climate zones, and Autodesk Revit and GBS are used for building modeling and energy performance analysis. After the analysis, they pointed out that the HVAC system is the main source of effect for energy consumption and the process of energy performance analysis can be significantly accelerated by the application of BIM in the design process because design alternatives can be easily established. Changsaar and Abidin [
23] investigated the probability of utilizing BIM to optimize building energy performance and identify the main types of equipment that could affect the energy consumption of an eco-home. They also applied Autodesk Revit 2018 and GBS for 3D modeling and optimized energy analysis, respectively. In the summary of their research, they pointed out that the overall energy performance measurements can be effectively improved by energy performance analysis by the integration of BIM and GBS. Furthermore, the highest percentage of energy consumption was from the air conditioner (about 40%) in the case building, while the equipment with the lowest energy needs was the smart ceiling fans.
Compared to other researchers, Singh and Sadhu [
19] considered more factors, including weather and climate condition, the building network, and internal and external parameters of buildings, etc., which may influence building energy efficiency. The result of their shows that the building lifecycle budget can be affected by the external or internal configuration changes of the structure; the location of the building relative to the sun’s path determines the building’s ability to get heat from solar energy; Changing the window to wall ratio (WWR) plays an important role in optimizing building energy performance. The research of [
21] developed a framework to improve the building energy consumption management in different climatic conditions based on BIM integration. They then noted that building energy consumption can be significantly influenced by the type of building design and the climate data, and the WWR has more influence on building energy consumption than using super-high insulation building components. In order to improve the decision-making of energy optimization for existing buildings, [
22] developed an innovative framework based on the integration of mathematical optimization, BIM, and LCA. After energy simulation by GBS, they found that using the developed framework can reduce service life costs and energy consumption for the studied case building by 24–58%. Wang and Guo [
24] evaluated the availability of using BIM big data in Building Digital Twins for building energy efficiency. They developed a simulating experiment and used some approaches including DL, DFA and BPNN, etc. to achieve the comprehensive utilization of BIM and Digital Twins. The final result of the research showed that 92.38% of residents are satisfied with the environmental impact of the case building.
Suitable energy analysis software can not only provide accurate analysis results, but it can also improve the efficiency of the analysis. In order to find suitable software for the energy performance of a building, Al Ka’bi [
18] evaluated the performance of the most common simulating and modeling applications for green building energy consumption. They compared and ranked the building energy analysis performance of 10 of the most common energy in the unifying criteria, and the final ranked result in descending order was as follows: (1) TRNSYS, (2) Ecotect, (3) Autodesk-GBS, (4) EnergyPlus, (5) IES-VE, (6) IDAICE, (7) VIP-Energy, (8) DesignBuilder, (9) eQUEST, and (10) RIUSKA.
From the previous research presented above, it seems that building parameters can significantly affect building energy performance, and wall and HVAC systems contribute the highest impact on energy performance [
15,
17,
20,
23]. Moreover, WWR is another crucial factor in building energy consumption [
19,
21]. In addition, some researchers also stated that BIM could effectively assist the designers and stakeholders in identifying the key parameters which could affect building energy consumption and make sustainable design in the early phase of construction design for green buildings [
15,
16,
20]. Motalebi and Rashidi [
22] and Wang and Guo [
24] even made some improvements to BIM with other technologies for building energy performance. They integrated LCA theory and Digital twins technology into BIM, respectively, to improve the ability to assess the environmental impact and the interaction capacity of BIM, and the results showed that BIM performed well in both pieces of research. In light of this, it is obvious that identifying key building parameters which may influence the building energy performance are necessary for green building design, and BIM plays a positive role in the effective modeling and energy analysis of buildings.
2.7. Contributions Compared to Relation Research
The researchers first needed to establish a basic understanding of the simulated data. Although this study has similarities to past research methods, the software, variables, and methods used are different and the results of this study are different from those of past studies. The researchers have improved on past experimental methods in the course of researching this topic in order to make the simulations more realistic. This makes the results of this study unique.
The researchers conducted tests between the results of the BIM-based energy modeling simulations and the actual energy consumption of the case study. During the energy simulations, the researcher made various attempts to get the right data for the study. This was done until the energy simulation data met expectations. A total of 100,000 simulations were carried out in this study. The study provided complete simulation results for each shape studied. Finally, the results of the simulations were applied to the actual building, and recommendations were made for the energy optimization of the building. The investigation revealed many interoperability issues regarding BIM data input and data interpretation of the energy simulation results.