Review Reports

Reviewer’s comments

Authors’ Reply

1 the layout in the introduction needs to be adjusted, such as 1.1 and 1.2.

Re: Dear reviewer, thanks for your valuable advice. I double-checked and found it is better to put section 1.2 before 1.1. As 1.2 is more like background, and the objective of this paper is just drawn from the introduction to the background. Then 1.1 is an expanded description of the two technologies (BIM and statistical analysis) mentioned in 1,2. Therefore, it is better to first put 1.2 and then 1.1.

2 the authors mentioned some simulation method for building energy performance. In fact, IDEAM model is also an important simulation method, it would be better to add the review on this method.

https://doi.org/10.1016/j.energy.2022.124395

https://doi.org/10.1016/j.apenergy.2022.119828

Re: Dear reviewer, thanks for your suggestions. We have added a separate section to introduce the benefit of IDEAM. We have read these two articles and have an initial idea about the IDEAM. This change can be found in Section 2.5 and 2.6. It is also mentioned in Section 7. The changes are attached below:

·           Section 2.5

The traditional methods to explore building energy consumption mainly focus on fundamental data and lack interaction of influencing elements from both macro and micro level [28]. However, an innovative energy simulation model was introduced by Huo et al to investigate the carbon emission of Chinese commercial buildings toward 2060, which is the integrated dynamic emission assessment mode (IDEAM) [29]. The IDEAM consists of the system dynamics (SD) model and a bottom-up end-use decomposition model, which can not only reflect the influential parameters but also provide an interactive feedback mechanism between different levels of parameters [29]. The IDEAM has been successfully utilised in exploring the interaction among different influencing elements and predicting the urban residential building carbon emissions [30].

·           Section 2.6

Additionally, the innovation research method (IDEAM) that considering both macro and micro variables are compared with traditional method. However, this article aims to explore the interaction between energy consumption and building internal variables, which are factors belong to micro level. The ambient influencing elements are ignored to simplify the whole process. Hence, the traditional fundamental study is more compatible with this research.

·           Section 7

Fifthly, an innovative method to explore a more complex and realistic relationships between energy consumption and influencing factors of macro and micro level has been introduced in previous part, which is IDEA model. A more interactive research can be performed by considering building elements, occupant behaviours, ambient environment and even socioeconomic factors.

3 in table 3.3.2, how did the authors select those properties?

Re: Dear reviewer, the values shown in table 3.3.2 are default in Autodesk Revit software. The reason we choose these default properties is that the primary aim of this paper is to investigate the influence of changing a certain building property on the total energy consumption. A control group with default values can allow us to easily compare and pick up any difference efficiently.

However, after carefully reviewed your question and our manuscript, we realised that a more common and realistic combination should be chosen as the control group in future research related to this topic, to reflect the real construction in the life.

4 why was the Linear Regression Model adopted, rather than nonlinear model?

Re: Dear reviewer, thanks you for picking up this point. The reason we choose linear regression model is that the LR method is widely used in research and it has the benefits of displaying results intuitively. We have added more references to support this point in Section 4.4.

We realised that only using LR model is limited in data analysis, and we think this could be a point to improve in our future study. Thank you for your valuable comments.

·           Section 4.4

The regression method is a central part of many research projects, and has been utilised in almost every field, including economics, biological science and engineering [39]. Regression method provides users a simply, efficient way to study the dependence, which can be understanded that if a result depends on other characteristics and how strong it is. Linear regression is the essential and most commly used method in regression [40]. Previous research by others had validated the effectiveness of using linear regression method to analysis building energy consumption [41-43]. Other researchers, such as Aydinalp-Kiksal et al. also agreed that regression method is easier to use without specific expertise is required. [44, 45]. This study aims to find the preliminary relationships between building variables and building energy consumption. These relationships can be explained as the dependence of energy consumption on multiple building variables. Therefore, the linear regression can summarise obtained data as simply as possible [40], which is highly compatible with this research.

5 in table 5.3.4.1, why is there no random error term?

The error was reported in the text.

Reviewer’s comments

Authors’ Reply

1. Title: A bit difficult to perceive the key message from current title

Re: Dear Reviewer, thank you for your valuable comments, we have modified our title as below:

“Using Regression Model to Develop Building Energy Simulation by BIM Tools: A Case of Tyree Energy Technologies Building at UNSW Sydney”

2. Abstract: Not very clear, suggested revising it

Dear reviewer, thank you for your comment.

We have revised our abstract as below:

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 realise that the earth’s resources are limited and depleted, and buildings are the major energy consumers. Building Information Modelling (BIM) has gained popularity in recent years and is now widely used by architects, engineers, and construction teams to collaborate and provide a comprehensive design that follow a sustainable strategy. To that end, this study created building information models of education facility office rooms and used Autodesk Insight 360 and Green Building Studio (GBS) to perform energy simulations. Thirteen variables related to building internal design were examined, and five were found to endure a substantial affect on building energy consumption. The study also looked at the window-to-wall ratio (WWR), which was analysed by multi-linear regression. However, the results showed that the model did not fit well, and the error obtained during the validation process ranged from 1.0% to 26.0%, which is unacceptable for this research. These findings highlight some limitations in using BIM tools and linear regression methods, and discuss some potential improvements that can be achieved in future studies.

3. Introduction: Need to revise the Intro. • Citation is required here for BIM…”BIM provides a comprehensive …”…need to pay attention for other cases as well and for each sections/subsections, not only the Intro part. • Especial revision for the outline: i. Background, Motivation, and Problem Formulation ii. Challenges and Contributions iii. Outline of the Paper • I see two different section numbers for “1.2. Problem Definition” and “1.3. Problem Definition”

Re: Dear reviewer, thank you for your valuable comments.

We have added more references to support our discussion.

We feel sorry for our carelessness. The typo has been revised now. Sector 1.3 should be objective.

Literature Review: • Is there any other similar technology or framework like Building Information Modelling. If so, required to have a comparative analysis and why BIM is chosen? • Most of the referred research works are not very recent. Suggested to add/review more relevant research works from 2020, 2021, 2022, and 2023.

Re: Dear reviewer, many thanks for your advice. I have added a new section Section 2.5 to compare IDEA model and existing methods. This innovative method provides an opportunity to research the influence of multiple variables, from both macro and micro levels on building energy consumption.

The newly added contents are attached as below:

2.5. Comparative Study between existing building energy studies and IDEA Model

The traditional methods to explore building energy consumption mainly focus on fundamental data and lack interaction of influencing elements from both macro and micro level [28]. However, an innovative energy simulation model was introduced by Huo et al to investigate the carbon emission of Chinese commercial buildings toward 2060, which is the integrated dynamic emission assessment mode (IDEAM) [29]. The IDEAM consists of the system dynamics (SD) model and a bottom-up end-use decomposition model, which can not only reflect the influential parameters but also provide an interactive feedback mechanism between different levels of parameters [29]. The IDEAM has been successfully utilised in exploring the interaction among different influencing elements and predicting the urban residential building carbon emissions [30].

More references are added, including those from recent years.

Materials and Methods: • I see that Figure 3.5.1 presents images with thermal states. What are the significances of doing this (depicting images with thermal state), not presented respectfully?

Thanks for your comments, please see the revised comments in the text.

6. Obtaining, filtering Data, and Performing Statistical Analysis: • 4.4. Linear Regression Model: The regression method is a central part of many research projects, and linear regres-471 sion analysis is the most commonly used method in regression. This study aims to find 472 the relationships between building variables and building energy consumption. The lin-473 ear regression can ssummarise obtained data as simply as possible, which is highly com-474 patible with this research. i. Why it aims so? ii. How/Why the Linear Regression can effectively model the subject of study? iii. Need to present it more thoroughly to the context. • How does the statistical analysis effectively be used in real life? Some real-life scenarios, examples, and applications. Any suggestions and ideas… to make its effective usage.

Re: Dear reviewer, thank you for your comments. We have added more references to support our opinion in Section 4.4.

We realised that the linear regression analysis could be an effective method for our research because it simplifies the statistics models and can present more intuitive results as highlighted by many other researchers.

The linear regression method is simple and widely used to research the dependence of the result on changing variables. This is highly suitable for our research as the fact of our study is to research the dependence of energy consumption on building variables.

We also realised that the non-linear regression analysis can also be used in our research. However, this method may require more specific expertise. Therefore, non-linear research can be considered further in our future research.

The revised Section 4.4 is attached as below:

The regression method is a central part of many research projects, and has been utilised in almost every field, including economics, biological science and engineering [39]. Regression method provides users a simply, efficient way to study the dependence, which can be understanded that if a result depends on other characteristics and how strong it is. Linear regression is the essential and most commonly used method in regression [40]. Previous research by others had validated the effectiveness of using linear regression method to analysis building energy consumption [41-43]. Other researchers, such as Aydinalp-Kiksal et al. also agreed that regression method is easier to use without specific expertise is required. [44, 45]. This study aims to find the preliminary relationships between building variables and building energy consumption. These relationships can be explained as the dependence of energy consumption on multiple building variables. Therefore, the linear regression can summarise obtained data as simply as possible [40], which is highly compatible with this research.

Simulation Results: • A thorough demonstration is required for each numerical analysis.

Re: Dear reviewer, thank you for your comments. Some parts in Simulation Results have been revised.

8. Where is section 6? I see that after section 5 it is section 7: Conclusion, Recommendations, and Future Research

Re: Dear reviewer, thank you for your careful check. We have corrected this typo. The section 6 should be Conclusion, Recommendations, and Future Research, and there is no section 7.

9.Conclusion, Recommendations, and Future Research • Need revising it. • This study aimed to investigate the relationships between building variables and en-868 ergy consumption in different building shapes by creating prototypes. The accuracy of the 869 regression models was measured through a validation process. However, the errors in the 870 models were found to be outside of an acceptable range, and as a result, the models were 871 deemed unqualified. The study acknowledges several potential reasons and limitations 872 discussed earlier in the content. The topic of this research paper is considered complex and has already been studied 875 by many other researchers. This study utilised a simplified research method to achieve 876 preliminary results. However, more extensive and detailed research is necessary to ex-877 plore the relationship between building variables and energy performance fully. … … Furthermore, this paper only considers interior elements and idealised scenarios. 897 Many default settings are selected during the analysis. Hence, a real external environment 898 is required to consider in future studies to obtain accurate and detailed results. This re-899 quires the author to have more in-depth knowledge of the heating, cooling, ventilation, 900 energy, and weather data. Realistic constructions that comply with Australian Standards 901 and Building Codes can be applied during modelling. i. Authors need to demonstrate/highlight the selling points more precisely.

Re: Dear reviewer, the Section 6 has been revised. we also realised the limitation of our research as it only considers the internal building elements. Building energy consumption can be influenced by both internal and external factors, such as occupant behaviour, ambient environment, seasons, even local government regulations and national socioeconomic policies. The revised contents are attached as below:

Based on the research process and results of this paper, the author makes several recommendations and points out several directions for future studies. Firstly, a more reliable and accurate energy analysis software, such as EnergyPlus can be used to validate the results obtained from GBS. Similar to Garcia and Zhu’s study, different energy analysis tools can be used to verify the calculations, and a real energy bill can be used to validate the results [50]. Secondly, as discussed in the limitation part, the geometry of the shape, including the aspect ratio and floor area, should be considered in a future study. The aspect ratio and floor area should be controlled to create a consistent simulation environment. Thirdly, more variables should be researched. Alothman has pointed out that the HVAC system is an important energy consumption element [51]. Asadi also said that ceil insulation type has a significant effect on energy consumption [25]. Fourthly, the author founds that TETB is surrounded by education facilities during the site inspection, and most of them have the same roof level as TETB. Adequate sunlight can enter the building and reduce the use of artificial light sources. Hence, the solar analysis and lighting analysis can be performed in future studies based on the preliminary BIM model of a typical floor in TETB as shown previously. Fifthly, an innovative method to explore a more complex and realistic relationships between energy consumption and influencing factors of macro and micro level has been introduced in previous part, which is IDEA model. More interactive research can be performed by considering building elements, occupant behaviours, ambient environment and even socioeconomic factors.

10. References: • References are missing in many places where it is very much required. As it is a case study paper, the authors must be careful about it. Some of the scenarios… i. Table 2.1. Data Transfer Issues Between Different BIM Tools: Keywords and Key phrases are missing citations. ii. Table 2.2. Summary of Case Studies about Regression Analysis Combine with BIM Tools: Proper citations are required. iii. Table 3.1.1 List of Selected Software: Proper citations are missing. iv. Notably, there are many other places as well as. • More relevant references are required. Irrelevant references need to be deleted.

Re: Dear reviewer, thank you for picking up these points.

We have added more references related to the keywords in our article. References related to linear regression have also been added.