BIM in Green Building Design and Construction

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Faham Tahmasebinia	--	2637	2023-11-17 10:27:31	\|
2	layout & references	Sirius Huang	Meta information modification	2637	2023-11-21 01:44:12	\|

This entry is adapted from the peer-reviewed paper 10.3390/buildings13041089

Addressing clients’ demands, designers have become increasingly concerned about the operation phases of buildings and, more specifically, energy consumption. This issue has become more prominent as people realize that the Earth’s resources are limited and depleted, and buildings are major energy consumers. Building Information Modelling (BIM) has gained popularity and is widely used by architects, engineers, and construction teams to collaborate and provide a comprehensive design that follows a sustainable strategy.

green building building information modelling BIM regression analysis EUI energy optimization

1. Introduction

Building Information Modelling (BIM) provides a comprehensive platform for designers to gain a deeper understanding of the various building parameters to design buildings that are low in energy consumption ^[1]^[2].There are many tools on the market that use and are compatible with BIM technology, which facilitates designers from different fields. In addition, as an important part of statistics, regression models can provide a more significant indication of the dispersion and correlation of data. Combining the two allows designers to understand the parameters most critical to energy consumption. BIM is widely used by many companies, and a technical review of BIM applications suggests that two main considerations in BIM utilization are its compatibility with needs and its interoperability with other tools ^[3]. These two drivers can make BIM more useful to the community or industry. BIM integration with various tools and sources of data, such as building shape, geometry, materials, and equipment types, allows the creation of accurate energy modelling. Energy modelling practices are important since advanced models can be used to predict the energy consumption of various buildings. This outcome of simulations and estimations of energy consumption need to be compared with actual data acquired from energy meters or submeters recording the electrical, heating, ventilation, or air conditioning usage or the data collected and managed through building management systems in real time at smart buildings ^[4]. The validation of models using read data will eventually help to apply artificial intelligence (AI) algorithms to predict energy consumption and optimize energy efficiency based on changes in design variants, construction, or usage during the operation phase of the building ^[5]. Thus, the current practices of energy modelling are important to test or cross-verify the estimation methods for various design alternatives and shapes. This type of practice will eventually support advanced AI-based optimizations and predictions. Based on lessons from the current practices, AI will be further developed to automate the modelling process using big data created by numerous smart homes and buildings ^[4]. These modelling exercises, optimizations, and predictions are significant because they help facility managers and the building sectors to improve the energy performance, reduce greenhouse gas emissions, and contribute to sustainable development goals (SDGs), in particular, SDG 7, 11, and 13. These SDGs are respectively connected to the provision of modern and sustainable energy, the development of resilient and sustainable cities, and the implementation of urgent actions to combat climate change. The present discussion on building energy performance modelling through regression analysis is very significant and relevant to achieving the SDGs.

2. Implementing BIM in Green Building Design and Construction

2.1. Background of BIM Development in Green Construction

BIM was developed in the 1970s, but the promising concepts and values of BIM were agreed upon in 1999. Since this time, continuous efforts have been made to implement BIM in green building design. Succar revealed that BIM is comprised of interacting processes and technology, which can generate and manage building data throughout the entire project life cycle ^[6]. Abanda et al. then provided a list of 122 BIM software systems and indicated that the various functions of BIM can be competent in various aspects of green construction, such as building energy performance assessment, lighting analysis, and construction and demolition waste analysis ^[7]. Wong and Zhou presented a summary of literature reviews on BIM green construction but lacked the detail of specific frameworks to link BIM with green construction ^[8]. However, Lu et al. presented an essential assessment of the relationship between BIM and green buildings and proposed a Green BIM Triangle organization to conceptualize the interactions between BIM and green buildings.

2.2. Energy Simulation by BIM

BIM stores massive energy-related building information and has a strong capability for information exchange. It can extract all building geometry information from the architectural building model and forecast total energy consumption and make recommendations for energy optimization ^[9]. With these results, designers can build more sustainable buildings.

Kamel and Memari summarized previous case studies about the use of various BIM tools in energy analysis. These case studies indicate that the building models are mostly developed based on Revit or ArchiCAD ^[10]. Revit and ArchiCAD have strong conceptual massing capabilities, which can use basic shapes to model building form and orientation in the early design stage. The building energy model can be automatically generated from the building model through the prototyping of an Application Programming Interface (API). The user can update the design concept anytime in Revit or ArchiCAD and the energy model is also updated automatically. The two most prevalent BIM file schemas are gbXML and IFC, which allow data transfer. Feasible energy simulation tools are various, including EnergyPlus, OpenStudio, and GBS. They take either IFC or gbXML as the input to generate the building energy performance results.

3. Research Gap and Challenges for BIM-Based Energy Simulation

3.1. Limitations of Using BIM Tools

Kamel and Memari also investigated the feasibility of data exchange from BIM to BEM and found that the lack of interoperability was a major issue in implementing BIM tools ^[10]. Table 1 summarizes their findings in data transfer between different tools based on three case studies. Direct transfer from Revit to GBS is the most feasible and convenient solution. Other BIM tools either have missing data or require manual input, which is not user-friendly. Secondly, today’s industry is lacking clear construction standards and codes for various aspects of the BIM application. Thirty-six existing standards of Green BIM application have been studied by Chong et al., but those standards do not pay much attention to the specific execution of BIM application in green building design, such as refurbishment and demolition of green buildings ^[11]. Lastly, low industrial acceptance of BIM technology is also an important issue. BIM is an innovative technology that requires collaboration among various software tools. The lack of knowledge and expertise in the industry makes workers rely on conventional construction methods ^[12].

Table 1. Data transfer issues between different BIM tools ^[10].

Data Type	Case Study 1	Case Study 2			Case Study 3
Data Type	Revit-GBS	Revit-gbXML	gbXML-OpenStudio	OpenStudio-IDF	GBS-IDE
Weather File Schedules	Automatically obtained through the location button in Revit Transfers	N/A	Requires manual input	N/A	Requires manual input
Schedule	Transfers	Transfers	Does not transfer and manually added using GUI	Transfers	N/A
Constructions	Complete transfer, GBS divides surfaces to sub-surfaces based on the energy analytical model	N/A	Complete and correct transfer	N/A	Transfers
Loads	Transfers	Does not Transfer	Does not transfer and manually added using GUI	Transfers	Transfers
Space Types	Transfers	N/A	Transfers	N/A	Transfers
Building Information	Transfers	Transfers	Partial Transfers For example, typical floor height is not transferred	Transfers	Transfers
Thermal Zones	Transfers thermostat set points based on default values	Does not transfer	Does not transfer and manually added using GUI	Transfers	N/A
HVAC	Transfers	Does not transfer	Does not transfer and manually added using GUI	Transfers	Transfers

N/A represents there is no information about this field.

3.2. Future Work Directions of BIM

According to the issues identified in Section 2, the process of data extraction and exchange needs to be optimized by enhancing the functionality of gbXML and IFC Schema. An efficient algorithm also needs to be developed to enable the automatic extraction of specific types of energy data from the BIM model. More compatible middleware is required to develop if direct data transfer is prohibited. Moreover, future research should pay more attention to the technical details of implementing BIM in energy simulation and all relevant green building designs rather than just making recommendations to use BIM. A complete industry standard of BIM application and a more effective framework for the energy simulation process is expected to be developed.

4. Multiple Linear Regression (MLR) Models of Energy Simulation

4.1. Background

As the building sector is the largest energy and CO₂ emitter, energy prediction models have been researched and innovated over the past decades. In the early research, physical models with static energy analysis were first used to predict energy consumption, which is quite time-consuming and labor-intensive. Some mathematical models and energy forecasting software were then developed, which improve the prediction speed but cannot perfectly match the simulation data. Moreover, the involved mathematical formulas or programming language requires expertise ^[13]. Compared with various energy prediction tools, the multiple linear regression (MLR) models are the most appropriate prediction tool to be implemented in this thesis study due to its simplicity and high accuracy ^[14]. MLR can express the simulated energy data in a linear equation to predict the energy consumption of given material properties.

4.2. Application in Building Energy Simulation

Multiple regression models have been applied widely in different areas of energy analysis, such as residential building retrofit, heat load prediction and building energy usage estimation, etc. Walter and Sohn used a multivariate linear regression model to quantify the contribution of each building variable to the total energy usage. However, the model is not validated by the post-retrofit energy data ^[15]. Martinez et al. applied the same technique to assess the energy performance results of a residential house after retrofit and found that upgrading building envelopes increase energy savings ^[16]. Jaffa et al. also developed an accurate polynomial model summarizing the influence of heat consumption ^[17]. Lam et al. studied the energy consumption of office buildings in different climates in China. They imported 12 design variables, including building loads, HVAC system, etc., and surprisingly found that the difference between regression prediction and DOE simulation is within 10%. The result implies that the regression models developed can be used to estimate the likely energy savings or consumption during the initial design stage when different building schemes and design concepts are being considered ^[18].

Mottahedi et al. obtained a more accurate prediction with a maximum error of 5% on total energy consumption. The researchers made a multiple linear regression model to predict the energy consumption of an office building with seven different shapes ^[19]. The model includes the regression coefficient for each design parameter, and the format is also adopted in this study to generate an MLR model.

4.3. Limitations of Multiple Linear Regression Models (MLR)

The main issue of using a multiple linear regression model is the multicollinearity, which is caused by the high correlation among internal factors. Multicollinearity makes it hard to interpret the coefficients and thus reduces the capability of the model to present the effect of independent variables. Another issue is that MLR cannot perform accurate predictions as non-linear models produced by machine learning models, such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs) ^[20]. This is because linearity cannot entirely represent reality, and the building model is so complicated that not every detail of the building can be modelled.

4.4. Future Work Direction of MLR

The MLR needs to be improved to detect the multicollinearity issue and illustrate the residuals from its best-fit regression equation. Moreover, the sensitivity analysis should be applied to aid in the regression approach as it can measure the relative standing of the effect limitations, making it possible to replace the design parameters that may influence an insignificant effect on the dependent variable. Moreover, the stepwise function can be used to determine whether to eliminate or add influence variables based on the level of correlation. However, the future trend should focus more on non-linear modelling to obtain more accurate forecast results. More machine-learning models should be introduced to the industries ^[20].

5. Energy Regression Analysis Combine with BIM Tools

5.1. Necessity for MLR Combine with BIM

Many case studies have indicated the applicability of BIM in energy simulation in Section 2, but few papers have verified the simulation results and tested their validity. The accuracy of the simulation is confronted by the challenge of interoperability issues among BIM tools. Interoperability refers to the ability of two or more systems to exchange information that is needed and available to use the information ^[11]. Several studies have summarized the potential issues of the two most proposed Data exchange Schemes (IFC, and gbXML). Choi et al. found that data loss frequently occurs when transferring IFC model data into the energy model, leading to incomplete or incorrect IFC files. Moreover, the definitions of IFC can vary with different BIM authoring tools, and thus unexpected errors were often created, reducing the reliability of results ^[21]. Pinherio et al. pointed out that the gbXML-enabled data exchange is inappropriate for large complex building shapes since the calculated surface areas and space volume differences might exceed the standard engineering tolerance and cause an overestimation of building energy consumption ^[22]. Moreover, the lack of the geometric representation of heating, ventilation, and air conditioning systems for gbXML might affect the simulation results ^[23].

These findings above indicate that the energy simulation process produces errors and thus, the model validation is necessary to decide whether the errors are within the acceptable range. Model validation can be achieved by constructing MLR with a series of statistical analyses, such as T-test, F-test, and ANOVA. MLR can predict the energy simulation results produced from the BIM energy analysis software and represent them in linear equations. The comparison between regression prediction and BIM simulation results can be visualized by generating bar plots on MATLAB.

5.2. Summary of Relevant Case Study

There is a lot of separate literature on regression analysis to predict energy use and BIM to simulate energy models, but few studies have mentioned how the two technologies work together. Table 2 below is a summary of case studies about the combination of these two techniques.

Table 2. Summary of case studies about regression analysis combined with BIM tools.

Author	Case Study	Modelling Technique	Findings
Mottahedia, M Mohammadpour, A Amiri, S, S Riley, D Asadi, S	Annual energy consumption of a typical office building with seven different building shapes in two different climates ^[19]	e-Quest and DOE-2: used to generate building energy model. MLR: used to predict total energy consumption in a linear equation. Coefficient determination (R2), F-test, and root mean squared error (RMSE): used to discuss the accuracy of regression models.	Space heating is the main source of energy consumption in the polar climate zone. T-shape buildings consume the most energy in both cold–dry and warm–marine climates.
Mottahedia, M. Asadi, S. Amiri, S.	Development of a Multiple Linear Regression Model to assess energy consumption in the early stage of commercial buildings design ^[24]	Monte Carlo Analysis: used to estimate the probability of getting acceptable results. eQuest and DOE-2: used to create and simulate modelling. MLR: used to identify the relationship between independent variables and the dependent variable. F-test, and RMSE: used to tell whether the design variables are significant	1. A basic framework is developed to link the regression model with the BIM simulation tool. 2. The errors between DOE simulation and regression prediction are within 5%.
Aghdaei, N Kokogiannakis, G Mccarthy, T	Prediction of annual heating and cooling demand in three types of Australian dwellings ^[25]	MLR with ANOVA approach: used to predict annual heating and cooling demand. Building Energy simulation method (not specified): used to verify the prediction results. Sensitive analysis: used to identify the most influential parameters.	The error between energy simulation and prediction results is less than 15%. R² was over 0.90, indicating a good agreement between the simulation and regression model.
González, J. Alberto, P. Soares, C, A, P. Najjar, M. Haddad, A, N.	BIM and BEM methodologies integration in energy-efficient buildings using experimental design ^[26]	AutoCAD Revit: used to define a physical model and perform model integration. GBS: used to run the generated model 42 times. Regression Models developed by Minitab software with p-value: used to determine the representativeness of the buildings	The higher the efficiency of lighting and applications, the lower the electric demand. The lack of information about thermal material properties affects the accuracy of the simulation.

References

Abanda, F.; Byers, L. An investigation of the impact of building orientation on energy consumption in a domestic building using emerging BIM (Building Information Modelling). Energy 2016, 97, 517–527.
Zhang, C.; Nizam, R.S.; Tian, L. BIM-based investigation of total energy consumption in delivering building products. Adv. Eng. Informatics 2018, 38, 370–380.
Shirowzhan, S.; Sepasgozar, S.M.; Edwards, D.J.; Li, H.; Wang, C. BIM compatibility and its differentiation with interoperability challenges as an innovation factor. Autom. Constr. 2020, 112, 103086.
Sepasgozar, S.; Karimi, R.; Farahzadi, L.; Moezzi, F.; Shirowzhan, S.; Ebrahimzadeh, S.M.; Hui, F.; Aye, L. A Systematic Content Review of Artificial Intelligence and the Internet of Things Applications in Smart Home. Appl. Sci. 2020, 10, 3074.
López-Pérez, L.A.; Flores-Prieto, J.J. Adaptive thermal comfort approach to save energy in tropical climate educational building by artificial intelligence. Energy 2023, 263, 125706.
Succar, B. Building information modelling framework: A research and delivery foundation for industry stakeholders. Autom. Constr. 2009, 18, 357–375.
Abanda, F.H.; Vidalakis, C.; Oti, A.H.; Tah, J.H. A critical analysis of Building Information Modelling systems used in construction projects. Adv. Eng. Softw. 2015, 90, 183–201.
Wong, J.K.W.; Zhou, J. Enhancing environmental sustainability over building life cycles through green BIM: A review. Autom. Constr. 2015, 57, 156–165.
Gao, H.; Zhang, L.; Koch, C.; Wu, Y. BIM-based real time building energy simulation and optimization in early design stage. IOP Conf. Ser. Mater. Sci. Eng. 2019, 556, 012064.
Kamel, E.; Memari, A.M. Review of BIM’s application in energy simulation: Tools, issues, and solutions. Autom. Constr. 2019, 97, 164–180.
Chong, H.-Y.; Lee, C.-Y.; Wang, X. A mixed review of the adoption of Building Information Modelling (BIM) for sustainability. J. Clean. Prod. 2017, 142, 4114–4126.
Lu, Y.; Wu, Z.; Chang, R.; Li, Y. Building Information Modeling (BIM) for green buildings: A critical review and future directions. Autom. Constr. 2017, 83, 134–148.
Catalina, T.; Iordache, V.; Caracaleanu, B. Multiple regression model for fast prediction of the heating energy demand. Energy Build. 2013, 57, 302–312.
Braun, M.; Altan, H.; Beck, S. Using regression analysis to predict the future energy consumption of a supermarket in the UK. Appl. Energy 2014, 130, 305–313.
Walter, T.; Sohn, M.D. A regression-based approach to estimating retrofit savings using the Building Performance Database. Appl. Energy 2016, 179, 996–1005.
Martinez, A.; Choi, J.-H. Analysis of energy impacts of facade-inclusive retrofit strategies, compared to system-only retrofits using regression models. Energy Build. 2018, 158, 261–267.
Jaffal, I.; Inard, C.; Bozonnet, E. Toward integrated building design: A parametric method for evaluating heating demand. Appl. Therm. Eng. 2012, 40, 267–274.
Lam, J.C.; Wan, K.K.; Liu, D.; Tsang, C. Multiple regression models for energy use in air-conditioned office buildings in different climates. Energy Convers. Manag. 2010, 51, 2692–2697.
Mottahedi, M.; Mohammadpour, A.; Amiri, S.S.; Riley, D.; Asadi, S. Multi-linear Regression Models to Predict the Annual Energy Consumption of an Office Building with Different Shapes. Procedia Eng. 2015, 118, 622–629.
Yildiz, B.; Bilbao, J.; Sproul, A. A review and analysis of regression and machine learning models on commercial building electricity load forecasting. Renew. Sustain. Energy Rev. 2017, 73, 1104–1122.
Choi, J.; Shin, J.; Kim, M.; Kim, I. Development of openBIM-based energy analysis software to improve the interoperability of energy performance assessment. Autom. Constr. 2016, 72, 52–64.
Pinheiro, S.; Wimmer, R.; O’Donnell, J.; Muhic, S.; Bazjanac, V.; Maile, T.; Frisch, J.; van Treeck, C. MVD based information exchange between BIM and building energy performance simulation. Autom. Constr. 2018, 90, 91–103.
Liu, X.; Akinci, B.; Bergés, M.; Garrett, J.H. Extending the information delivery manual approach to identify information requirements for performance analysis of HVAC systems. Adv. Eng. Inform. 2013, 27, 496–505.
Asadi, S.; Amiri, S.S.; Mottahedi, M. On the development of multi-linear regression analysis to assess energy consumption in the early stages of building design. Energy Build. 2014, 85, 246–255.
Aghdaei, N.; Kokogiannakis, G.; Daly, D.; McCarthy, T. Linear regression models for prediction of annual heating and cooling demand in representative Australian residential dwellings. Energy Procedia 2017, 121, 79–86.
González, J.; Soares, C.A.P.; Najjar, M.; Haddad, A.N. BIM and BEM Methodologies Integration in Energy-Efficient Buildings Using Experimental Design. Buildings 2021, 11, 491.

© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.

Upload a video for this entry

Information

Subjects: Engineering, Civil

Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :

Samad M. E. Sepasgozar

View Times: 292

Update Date: 21 Nov 2023

Table of Contents

Video Upload Options

Confirm