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BIM and BEM Methodologies Integration in Energy-Efficient Buildings Using Experimental Design

Buildings 2021, 11(10), 491; https://doi.org/10.3390/buildings11100491

by Jorge González¹

, Carlos Alberto Pereira Soares¹

, Mohammad Najjar²

and Assed N. Haddad^3,*

Reviewer 1: Anonymous

Reviewer 2: Anonymous

Buildings 2021, 11(10), 491; https://doi.org/10.3390/buildings11100491

Submission received: 2 September 2021 / Revised: 1 October 2021 / Accepted: 5 October 2021 / Published: 19 October 2021

(This article belongs to the Section Building Energy, Physics, Environment, and Systems)

Round 1

Reviewer 1 Report

Article deals with energy performance modeling using BEM tool integrated in Autodesk Revit. The impression is that novelty of research topic is not presented in sufficient manner. There is significant lack of argumentation in results (inputs that was assumed for HVAC). Article cannot be published in presented form.

Main comments are:

article is to long, basics (for example L426-433) should be deleted;
home appliances are not included in HVAC;
are there some action of authors to improve/define new techniques for BEM integration ?; this will be original contribution (to science);
L 388 if thermal properties cannot be seen, how authors could defend their results ?
there is mistake in Tab. 2;
orientation is not shown on Fig 3, 4;
properties of windows are not shown in Tab 2;
authors claim that climate was carefully chosen (L 290), this is not justified, is this related to Fig. 9, 10 ?
selected Taguchi matrix is not presented (DOF) (Tab 7);
units in Tab 7. missing, definition of variables is not presented (e.g. what exactly is light efficiency)
purpose of Table 7 according to Table 8 ?
which parameter is Eh (in equation L 534) ? how type of HVAC is defined ?
how efficiency option range is defined ? (L508)
input data of the systems missing as well as precise description (e.g. what exactly is high-efficient package system......
in case 10 high eff heat pump is Eh; how indoor comfort is established taking into account Table 5.
what exactly is power load efficiency, no eq. is shown
what was the internal gains taken into account?
what was SFP of ventilation system, was VAV controlled according the schedules ?; are results compared to VAV single duct (L402);
definition of statistics and method is not presented (how significance is determined ?);
why statistics and regression coefficients are not included in main text ? t and p-test are not commented.

Author Response

Reviewer 1:

Thank you for your comments! I've followed all of your recommendations.

Article is to long, basics (for example L426-433) should be deleted;

R: That paragraph was deleted, as well as many others, to diminish the text.

home appliances are not included in HVAC;

R: No, they are not included in HVAC. As defined by Autodesk Insight, the plug load efficiency is the one that considers "the power used by equipment like computers and small appliances"; due to this, "lighting or heating and cooling equipment are excluded".

are there some action of authors to improve/define new techniques for BEM integration ?; this will be original contribution (to science);

R: the contribution to science is the consolidation of the energy performance analysis (by applying the BEM methodology) by using one single software, in this case, Autodesk REVIT. This idea comes up due to the complex exchange of data (related to the energy model), which had to be accomplished between two different software. Also, the utilization of experimental design and statistical analysis as tools to obtain the minor electric demand considering the chosen parameters are also considered a contribution to science. In the next paragraph is highlighted the contributions of this research, mentioned in the Introduction Section:

In this article, we integrate BIM and BEM methodologies using REVIT, experimental design, and statistical analysis to simulate facilities' energy loads, a building typology representative of houses located in tropical and subtropical areas, as an option to obtain the minor electric demand in the dwelling.

By allowing the simulation of energy performance from the integration of systems, methods, and procedures that consider the variation in the characteristics of the building and facilities, the methodology applied helps designers, builders, and users assess the benefits of design alternatives and upgrades and adjustments existing buildings.

In a scenario of growing scarcity of energy sources and increased demand, this work also contributes to reducing energy consumption without compromising the needs and expectations of users regarding the building's performance, particularly concerning comfort and well-being. Another important contribution is that the solution adopted for integrating systems, methods, and procedures can inspire professional researchers to extrapolate their benefits and potential to this field of knowledge. Finally, this work also contributes to the literature on energy efficiency in dwellings.

L 388 if thermal properties cannot be seen, how authors could defend their results ?

R: The phrase was changed to explain better what we wanted to say.

It should be noted that the thermal variables of the imported elements (furniture, doors) come directly from the online library and are unmodifiable;

there is mistake in Tab. 2;

R: I think the mistake is the lack of information about the windows. It is clarified below.

orientation is not shown on Fig 3, 4;

R: Figures were modified and added its orientation.

properties of windows are not shown in Tab 2;

R: Thermal properties of windows are now included. It must be highlighted that the thermal mass in glass is omitted, and the solar heat gain coefficient is added as a property to consider.

Element	Thickness, (cm)	Heat Transfer Coefficient, U ( )	Thermal Resistance, R ( )	Thermal Mass ( )	Solar Heat Gain Coefficient
Wall type 1	15	6,85	0,15	22,11
Wall type 2	10	9,93	0,10	13,97
Floor	15	8,40	0,12	28,26
Roof	12	9,03	0,11	21,40
Windows	0.4	3,69	0,27	-	0.78

authors claim that climate was carefully chosen (L 290), this is not justified, is this related to Fig. 9, 10 ?

R: The criteria for choosing the five locations is justified in Section 4: Case Study, Subsection 4.2: Climatic Conditions. However, to complement the information in L 290, the paragraph was re-adjusted, as can be seen:

In this work, five different locations for the same case study are proposed to represent the energy demand, the required comfort standards, and its relation with the climatic conditions from those locations. The five locations carefully chosen were Armação dos Búzios, in southwest Brazil; Capri, in the Mediterranean Italian coast; Punta Cana, in the Caribbean Sea; Dubai, in the Persian Gulf; and Sydney, in the East Coast of Australia; they were defined to evaluate the energy performance of the house in five different climates.

selected Taguchi matrix is not presented (DOF) (Tab 7);

R: Sorry for that mistake. Here is presented the correct form of the table:

Lighting Efficiency ( ) (W/m²)	Plug Load Efficiency ( ) (W/m²)	HVAC System ( )
11.95	10.76	Central VAV
7.53	6.46	ASHRAE package system
3.23		High-Efficiency Heat Pump
		High-Efficiency Package System
		High-Efficiency Package Terminal AC
		High-Efficiency VAV

units in Tab 7. missing, definition of variables is not presented (e.g. what exactly is light efficiency)

R: The definition of the variables is exposed a few lines above. However, some changes were made to understand the paragraph better:

Lighting Efficiency ( ) shows the average internal heat gain and power consumption of electric lighting per unit floor area. It consists of three levels offered by Autodesk Insight analysis, which are 11.95 W/m², 7.53 W/m², and 3.23 W/m², depending on the dwelling's electric potency lighting devices. Plug Load Efficiency ( ) displays the power used by equipment in the building, such as computers, printers, washing machines, refrigerator, hairdryer, internet modem, etc. It consists of two levels, also available in the Autodesk Insight analysis, which are 10.76 W/m² and 6.46 W/m², following the mentioned equipment's load. HVAC System ( ) consists of seven levels, which are the different possible options of HVAC system available in Autodesk REVIT, and each one of them has its own implicit efficiency: Central VAV, ASHRAE Package System, High-Efficiency Heat Pump, High-Efficiency Package System, High-Efficiency Package Terminal AC, High-Efficiency Package VAV, and ASHRAE Package Terminal Heat Pump.

Here is the correct form of the table:

Lighting Efficiency ( ) (W/m²)	Plug Load Efficiency ( ) (W/m²)	HVAC System ( )
11.95	10.76	Central VAV
7.53	6.46	ASHRAE package system
3.23		High-Efficiency Heat Pump
		High-Efficiency Package System
		High-Efficiency Package Terminal AC
		High-Efficiency VAV

purpose of Table 7 according to Table 8 ?

R: While table 7 describes the design factors and levels, table 8 shows the different combinations of these factors and levels to develop the study, perfectly organized in the form they were statistically analyzed.

which parameter is Eh (in equation L 534) ? how type of HVAC is defined ?

R: As mentioned in table 7, Eh represents the HVAC system available in the options of Autodesk REVIT to perform the energy analysis. However, the phrase was modified for a better understanding:

This parameter, following the equation aforementioned, is based on the all relationships possible between the factors considered showed in Table 7 aforementioned ( , , , , and ).

how efficiency option range is defined ? (L508)

R: The efficiency options for Lighting and Plug Load are offered automatically by Autodesk Insight, the suite where the evaluation of the energy performance is accomplished. It was now clarified for a better understanding:

input data of the systems missing as well as precise description (e.g. what exactly is high-efficient package system......

R: A paragraph related to the description of the HVAC systems was incorporated:

The Central VAV presents a static pressure duct system with variable speed drive, a Coefficient of Performance of 5.96, and a water heater. The ASHRAE Package System has an Energy-Efficiency Ratio of 11, presenting the minimum efficiency of the HVAC systems in REVIT. The High-Efficiency Heat Pump has a Seasonal Energy-Efficiency Ratio of 17.4 and works with a constant volume cycling fan. The High-Efficiency Package System presents a small unit in a single zone system, and has a Seasonal Energy-Efficiency Ratio of 20. The High Efficiency Package Terminal AC is a type of self-contained heating and air-conditioning system, with an Energy-Efficiency Ratio of 12.7. The High-Efficiency VAV is an air terminal with a high-efficiency turndown on an air system, underflow air distribution with a Coefficient of Performance of 7.5. The Package Terminal Heat Pump consists of a separate, un-cased refrigeration system installed in a cabinet, which uses reverse cycle refrigeration as its prime heat source and has an Energy-Efficiency Ratio of 11.9.

in case 10 high eff heat pump is Eh; how indoor comfort is established taking into account Table 5.

R: indoor comfort is ensured by introducing the recommended values in the correspondent HVAC zone settings. We decided not to include the captures of these values to show the mentioned summary table. However, they are shown above.

what exactly is power load efficiency, no eq. is shown

R: Sorry for the mistake. All the terms "power loads" were modified to the right one (plug load efficiency).

what was the internal gains taken into account?

R: Internal Gains were considered generated by the persons depending on the room they are in. It was not mentioned, but now, they are specified in the Case Study section, table 4.

Space Type	Area per person (	Lighting Load Density (W/	Power Load Density (W/	Sensible Heat Gain per Person (W)	Latent Heath Gain per Person (W)	Occupancy
Dormitory Bedroom	10	11,95	5,81	73,27	45,43	Home Occupancy (24h)
Restroom	10	9,69	3,23	73,27	58,61	Home Occupancy (24h)
Library - Audiovisual	4	13,99	16,15	73,27	58,61	Retail Facility Occupancy (7am to 8pm)
Dining Area	1,5	9,69	5,81	80,56	80,56	Home Occupancy (24h)
Laundry – Ironing and Sorting	5	6,3	32,29	80,59	139,21	Retail Facility Occupancy (7am to 8pm)
Stairway	10	6,46	3,23	73,27	58,61	Home Occupancy (24h)
Parking Area	20	2,05	3,23	73,27	58,61	Home Occupancy (24h)

what was SFP of ventilation system, was VAV controlled according the schedules ?; are results compared to VAV single duct (L402);

R: VAV Single Duct is the HVAC system defined. It represents the same as Central VAV (the first HVAC system in the levels of Eh in Table 7) and has a minor energy efficiency. According to Autodesk REVIT, this Central VAV responds to the specifications defined in Table 5: it simulates these conditions by using the VAV Single Duct system. Regarding to SFP, there is no way to know the SFP value because REVIT does not emit this particular information, and also, the efficiency of the Central VAV system is considered as a whole.

definition of statistics and method is not presented (how significance is determined ?);

R: The statistic method followed is shown in section 4.6, describing the ANOVA and Linear Regression Models. The significance is now established in the same section. The paragraph was modified and can be seen next:

To establish the accuracy of the analysis, the P-value test was carried on to determine the representativeness of the results; it was established that the P-values could not exceed 0.05 to confirm the correctness of the linear regression analysis.

why statistics and regression coefficients are not included in main text ? t and p-test are not commented.

R: Statistics and regression coefficients are included in the Complementary File. However, they will be highlighted in the results section.

The statistical analysis accomplished in Minitab can be revised in the Complementary File. In there it is possible to observe the results of the General Factorial Regression, and how its methodology was followed, being possible to obtain the factor information, which describes the division of factors and levels; the analysis of variance, where are showed the information of the P-Values; the summary of the model, with the S and R values; the coefficients summary, showing the T-values and P-values and coefficients for each one of the interactions between levels; and finally, the Regression Equation, which expresses the significance of the interactions between factors and levels. By motives related to the size of the regression equation and the coefficients' tables, this information can be seen more accurately in the Complementary File.

Also, the last three paragraphs of the Results section show the interpretation of the statistical graphics emitted when completing the Minitab analysis.

Author Response File: Author Response.pdf

Reviewer 2 Report

The topic presented in the paper is of interest, both for its methodological development and for the resulting conclusions.
However, there are some issues of order of discourse that need to be improved. In general, Sections 1 and 2 present the research topic in a messy way. A general methodology section is missing where the type of building studied is explained, the reason for that choice and how the models are defined and developed. This explanation could be at the beginning of the "Materials and Methods" section or in a separate previous section.
An example of texts that are methodological, that are scattered and should not be, are lines 65-73, lines 179-188.
In line 199 there is talk of "Comfort, as perceived by the sense organs, could be divided into thermal, visual, auditory, 199 olfactory, and hygienic comfort [32], [33]." specifies which variables are taken into account. Issues like this should be clarified in the methodology section.
This makes it necessary to reorder and rewrite sections 1, 2 and 3.

Author Response

Reviewer 2:

Thank you for your comments! I've followed all of your recommendations.

In general, Sections 1 and 2 present the research topic in a messy way.

R: Sections 1 and 2 were modified as recommended.

A general methodology section is missing where the type of building studied is explained, the reason for that choice and how the models are defined and developed. This explanation could be at the beginning of the "Materials and Methods" section or in a separate previous section.

R: Methodology was improved. The explanation of the building selected was included in this section.

An example of texts that are methodological, that are scattered and should not be, are lines 65-73, lines 179-188.

R: New paragraphs were included in the Methodology section, as follows:

A house whose characteristics and consumption pattern are representative of tropical and subtropical areas, with a high standard, was selected to develop this research. The designed house has traces inspired by Spanish and Italian architecture, showingtucco or plaster exterior and red clay roof tiles, ornate archways, ceramic tiles on the floor, and exposed columns and beams [28]. The reason to choose this house was to analyze the possible energy performance advantages present in houses with spacious rooms, which present some attributes such as large windows, open spaces, balconies, high ceilings, shading devices, clay roofs tiles, and clear colors. These characteristics could significantly improve indoor comfort conditions by keeping a refreshed and pleasant environment inside the house [29], [30].

Bulky houses similar to the one used in this study are often used in warm climates, mainly in middle/upper-class neighborhoods. This house typology has also become attractive to this whole, for having high energy loads due to its inhabitants' weather conditions and behavior and for having the potential to reduce energy loads and increase energy efficiency.

An equation that could calculate the energy consumed was created by putting the appliances under interaction to determine their influence and possible performance solutions. The equation variables are called "design factors", and their different options are called "levels." Following the experimental design methodology, they have considered three factors (low-consumption lighting, low-consumption power loads, and HVAC systems with low energy demand). Within them, the levels were specified as three different lighting efficiency values, two different load efficiency values, and seven different options for the HVAC system for developing the experimental planning study.

In line 199 there is talk of "Comfort, as perceived by the sense organs, could be divided into thermal, visual, auditory, 199 olfactory, and hygienic comfort [32], [33]." specifies which variables are taken into account.

R: The variables taken into account are specified in Section 4: Case Study. Subsection 4.1: Comfort Conditions; as follows:

Only thermal and visual variables will be considered, such as temperature, humidity, air velocity, airflow, and illuminance, to guarantee comfort inside the house of the case of study. It is because those are the ones that can be analyzed in the physical and energy models recreated in the simulation.

Issues like this should be clarified in the methodology section.

R: The information above was relocated in the methodology, as recommended.

This makes it necessary to reorder and rewrite sections 1, 2 and 3.

R: Sections 1, 2, and 3 were reorganized.

Round 2

Reviewer 1 Report

Authors have responsed to comments.

Author Response

Thank for you time and knowledge. We tried to further improve the paper in this second opportnity.

Reviewer 2 Report

The changes that were requested in terms of the methodological definition of the study have been made. However, the reviewer considers that there is insufficient justification for the type of house chosen for the investigation (lines 254-268). This choice seems arbitrary and is not justified by references to the representativeness of this type of building. Consequently, what can be stated in sections 6 and 7 should be clear that it is only for this type of building. In this way, morphological factors such as the form factor, the number, and size of the openings, and the original construction techniques of the building will influence its performance, this must be clarified.

Author Response

Response to Reviewer Comments:

The changes that were requested in terms of the methodological definition of the study have been made.

We thank you for your comments. Your suggestions were followed and some modifications were accomplished. They are showed next:

However, the reviewer considers that there is insufficient justification for the type of house chosen for the investigation (lines 254-268). This choice seems arbitrary and is not justified by references to the representativeness of this type of building.

R: The selection of the house in the case study was made because bulky houses, similar to the presented one are widespread over the Brazilian territory as a common single-family residential type in high-income neighborhoods, and tend to consume more energy than in mid/low-income dwellings. It all occurs even though a fraction of the energy loads could be diminished by taking advantage of its architectural features and its relationship with the climate of the location (window size, open spaces, wall and roof material).

In the literature review section, we added new paragraphs, and others were modified to better sustain the reasons to choose these types of households. They are presented as follows:

Special attention is given to climatic conditions to achieve high comfort and optimum energy performance. The climate plays an important role in energy consumption and energy-efficiency systems in buildings [27]. The buildings are subjected to climate conditions which can affect the energy consumption of the building [33]. Architectural features distinguish buildings in the different climatic zones worldwide. For example, buildings in warm zones globally, such as tropical areas, are frequently designed to heighten the interactions between indoor and outdoor climates. The opposed situation occurs to constructions in cold zones, where the design tries to insulate the building from temperature exchange [27].

Through bioclimatic design and the use of design measures, a higher level of building energy efficiency and indoor thermal comfort can be achieved; the measures related to windows performance to increase ventilation levels and lighting, installation of shading devices, consideration of thermal insulation techniques, and airtightness [34], [35]. The house's orientation affects the quantity of energy to be demanded due to the linkage between environmental factors and indoor comfort. The more benefit from the orientation of the house (daylighting, shading, direction of winds), the more comfort to be felt by the occupants. The successful application of architectural features based on the climatic conditions will define and valorize the energy efficiency, exploiting the benefits that the environment offers [36], resulting in a diminution in the energy load.

Houses in tropical and sub-tropical high-income neighborhoods present elevated levels of energy consumption [37]. Most of the recent studies have highlighted the energy consumption in low-income households, and not that much in the ones in high-income dwellings; however, a few researches were found, like the ones of Malama et. Al., Allen et. Al, Xu, and Williams et. Al. [37]–[40]. Apart from the design and location of the building [41], [42], it was found that some of the reasons causing elevated energy consumption in bulky households are due to income and behavior of the users, age, activities developed, the technology level of the appliances connected. The consciousness of the dwellers is another factor; high-income consumers tend to be more environmentally responsible, but it may not be applied in the personal energy-use ambit [39].

So, it could be said that high-income households, despite presenting some advantageous architectural features, appropriated for warm climates, most of them are not properly utilized, as a consequence of inadequate occupancy schedules and exacerbated utilization of electrical devices to achieve thermal comfort.

Consequently, what can be stated in sections 6 and 7 should be clear that it is only for this type of building.

R: The suggestion was considered and new paragraphs were added for better explanation.

In the discussion

A hypothetical house compiling the features presented in single-family, high-income households in Brazil (wide windows, uninsulated walls, clay-roof tiles, open spaces, among others) was analyzed to consider its advantages in the energy consumption of the building when related to loads of the mentioned facilities.

The model’s design is representative for Brazil, where similar houses are widespread all over the country, the same as for other warm-climate zones in America, like the Caribbean. Nevertheless, it may not be similar to households present in Australia or some other sub-tropical zones due to the differences in the construction techniques or material properties. The model analyzed was extrapolated to other locations to apply the statistical-experimental design approach as a form to acquire the minimum results of energy consumption.

In the conclusion:

The selected case study has its particular features, and, despite it is similar to Brazilian bulky houses, its application as a real physical model in other world locations to obtain similar results needs to be studied and improved, adapting to the requirements needed in the location. However, the applied methodology is valid for usance in any other model, where the diminution of the energy loads needs to be achieved.

In this way, morphological factors such as the form factor, the number, and size of the openings, and the original construction techniques of the building will influence its performance, this must be clarified

R: We agree with this comment, this needs clarification and adjustment. Despite the characteristics and morphological factors of the physical models has significant effects on the energy consumption of the building, it is not on the scope of this research study to analyze these factor’s effects on the energy consumption. On the other hand, this type of construction has its characteristics and this influences the form and number of windows are determined by what is present on this specific typology of houses. We tried to replicate these features in this study and that is the reason why we have it this way. But the effects of appliances load on the buildings, and the application of a statistical approach to obtain the minimum values possible of EUI. However, due to the importance of highlight the information about the morphological factors, some information was added in the description of the case study (section 4).

As particular features of this household, windows and doors have a significant effect on natural lighting and ventilation rates, which influences the energy demand for the appliances. The description of its characteristics can be seen in Table 3:

Table 3. Doors and Windows.

Element	Quantity	Space	Material	Height (m)	Width (m)
Window 1	1	Master bathroom	Sash and Glass	0,5	2,0
Window 2	3		Wood and Glass	0,6	0,465
Sliding Door	1		Wood	2,1	1,0
Window	2	Master walk-in-closet	Wood and Glass	2,0	0,5
Sliding Door	1	Master walk-in-closet	Wood	2,1	1,0
Sliding Door	3	Master bedroom	Sash and Glass	2,5	1,1
Door	1	Master bedroom	Wood	2,1	0,9
Window	1	Library	Wood and Glass	2,0	1,8
Door	1	Library	Wood and Glass	2,5	1,6
Door	1	Entry hall	Wood and Glass	4,0	1,6
Sliding Door 1	1	Living room	Sash and Glass	2,5	2,9
Sliding Door 2	1	Living room	Sash and Glass	2,5	1,9
Sliding Door	1	Dining room	Sash and Glass	2,5	2,9
Sliding Door	1	Kitchen	Sash and Glass	2,5	1,9
Window	1	Visitors' bathroom	Wood and Glass	0,6	0,465
Door	1	Visitors' bathroom	Wood	2,1	0,7
Window	1	Laundry	Wood and Glass	1,5	1,0
Door	1	Laundry	Wood	2,1	0,8
Window 1	1	Garage	Wood and Glass	1,5	1,0
Window 2	1		Wood and Glass	2,0	1,8
Door 1	1		Wood	2,1	0,8
Door 2	2		Wood	2,5	2,5
Window	3	Stairs	Wood and Glass	1,2	0,6
Door 1	1	Bedroom 1	Wood and Glass	2,5	1,6
Door 2	1	Bedroom 1	Wood	2,1	0,8
Door	1	Walk-in-Closet 1	Wood	2,1	0,7
Sliding Door	1	Bedroom 2	Sash and Glass	2,5	1,83
Door			Wood	2,1	0,8
Door	1	Walk-in-Closet 2	Wood	2,1	0,7
Sliding Door	1	Bedroom 3	Sash and Glass	2,5	2,9
Door			Wood	2,1	0,8
Door	1	Walk-in-Closet 3	Wood	2,1	0,7
Window	1	Bathroom 1	Sash and Glass	0,5	1,0
Door	1		Wood	2,1	0,7
Window	1	Bathroom 2	Sash and Glass	0,5	1,0
Door	1		Wood	2,1	0,7

Author Response File: Author Response.pdf

Round 3

Reviewer 2 Report

The changes suggested by the reviewer have been made and are correctly justified. It is also suggested to accept the article for its publication.

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.

Round 1

Reviewer 1 Report

This paper focuses on the interesting topic of the integration between BIM and BEM methodologies for the analysis of the energy performance of an existing building. However this is not a new topic and I suggest a more in-depth examination of the relevant literature in this regard.

It's not really clear the novelty of the experimentation. I suggest to better explain it after the integration and the analysis of the new literature references.

I suggest to better specify (also with a simple schema) the workflow for the integration between Revit/Insight/GBS.

It is not clear how the BIM model was developed. It was created a master model start by the specific models (architectural, structural and MEP,.....) Which loin? Which level of graphic and information details to achieve the goals of the experimetation?

Author Response

Reviewer 1

Thank you for your valuable comments. We tried to address all of them and incorporate the suggestions in the document.

The relevant literature was incorporated in the text, as can be seen at Introduction - line 48:

Data exchange between BIM and BEM applications is not a seamless task. Usually, it requires manual intervention and data transformation [6] due to the need for available software programs that support a robust BIM-BEM translation process. Some interoperability issues in the manual process appear because the model's focus remains on the construction documentation rather than the energy performance simulation [7]. This situation trades in incomplete or incorrect HVAC system modelling, missing information about controls and internal loads, and difficulties reading geometry and attributes data for the physical model [8]. This situation derives from a time-consuming process and non-optimized, less energy-conscious models.

Also, incorporating experimental design to assess the results of the interoperability BIM-BEM results in a kind of new topic. The pertinent literature examination can be also found in Background – lines 302 to 313 / 319 to 330:

The two main applications of experimental design are screening, in which the factors that influence the experiments are identified, and optimization, in which the optimal settings or conditions for an experiment are found. The domains that experimental design has impacted significantly comprise real experiments, simulation-based experimental design, and parameter learning or hyper-parameter tuning [41], [42]. It can be achieved by balancing several features, including power, generalizability, forms of validity, practicality, and cost [43].

The manipulation of variables brings an interesting approach to its results. The effects and statistical significance of a larger group of experimental variables can be determined through factorial or screening designs, which enable choosing the relevant variables or conditions for the next set of experiments, considering different levels for each factor [14], [37].

There are many examples of experimental designs in the construction industry. Kioupis [38] handled a parametric experimental design to identify the settings of the process factors that optimize the quality characteristics of geopolymeric products. Yong et al. [44] adopted the experimental design to find optimal building envelope parameter values to minimize the heating load in a single-family house. Bustami et al. [45] investigated the potential of native plants growing in vertical walls for green buildings in Australia in accordance with different plant species, soil substrates, and irrigation regimes. Serbouti et al. [46] employed an experimental design to optimize a building's energy performance in Morocco using Python and TRNSYS. Najjar et al. [14] developed research where various performance parameters related to the building design (construction materials, window-wall ratios) were analyzed to obtain the best-suited arrangement of factors that present a more efficient energy consumption for the case of study building.

It's not really clear the novelty of the experimentation. I suggest to better explain it after the integration and the analysis of the new literature references.

We apologize for not having included in the original document the full experiment outcomes and analysis. The experimental design is now incorporated, the discussion is improved, and the title changed to acknowledge this.

The novelty is highlighted in the Introduction, lines 85 to 89.

Despite some researches implementing a statistical approach for analyzing building construction's results, it was not found anyone that applied an experimental design for studying the results of energy performance of appliances, when integrating BIM and BEM methodologies towards the diminution of the energy load in a building.

The experimental design was integrated with a case of study: validating the flowchart of the methodology, lines 686 to 697; in the Results, figures 8a, 8b, 8c, 8d, and 8e; and in the Complementary file.

The experimental design applied can be summarized by the following steps: First, the goal's definition, which implies the possible energy reduction according to the suggestion items emitted by Autodesk Insight's results. Second, the determination of which results to take into account to manipulate and carry on the simulations. Third, were defined the factors and levels that affected the EUI and created the equation that represents the energy consumption. Fourth, were developed the forty-two simulations that combine the different factors and levels defined above. Fifth, the different results were analyzed and determined the behavior of the EUI in the different cities in accordance with the combination of factors and levels. And lastly, it was conducted statistical analysis (analysis of variance and linear regression) to verify the study's accuracy and determine the significance of the results obtained. The software Minitab was adopted for this calculation to conduct the main effects plot by integrating the statistical process).

Figure 8. Correlation significance between the factors considered. a) Armação dos Búzios; b) Capri; c) Punta Cana; d) Dubai; e) Sydney.

I suggest to better specify (also with a simple schema) the workflow for the integration between Revit/Insight/GBS.

It was added a flowchart explaining the steps to develop the integration REVIT/Insight/GBS in Materials and Methods, line 394.

Figure 2. BIM-BEM integration flowchart.

The specification of the physical model I explained in case of study: validating the flowchart of the methodology, lines 521 to 527.

The inner walls (wall type 1) are made of typical brick (10cm thick), two layers of concrete cast-in-place (2cm thick each one), and two layers of paint (0,5cm thick each one). Ornamental walls in façades (wall type 2) are also made of brick, two layers of cast-in-place concrete and paint, but their thicknesses are 7,5cm, 1cm, and 0,25cm, respectively. Floors are made of concrete (13cm thick) and travertine (2cm thick). The roof is made of concrete cast-in-place (10cm thick) and clay-roof tiles (2cm thick). Windows and doors were acquired by imported from online BIM libraries [47], [48].

Also, figure 4 was upgraded and its size was increased for a better understanding of the architecture, in line 543.

(a)

(b)

Figure 4. Floor plans. a) first floor; b) second floor.

Author Response File: Author Response.pdf

Reviewer 2 Report

The paper deals with the integration of BIM and BEM for energy efficiency buildings. The topic could be of interest for the journal. However the paper is not suitable for being published in an international journal for the following reasons:

The title is misleading. The paper is not on the definition of a new methodology for the assessment of the integration between BIM and BEM, but is just on the use of commercial software (that already provide integration between BIM and BEM) on a case of study. Moreover the case of study selected is far from being considered a "energy-efficient" building.
The paper largely describes issues related to Nearly-Zero Energy Buildings, but avoiding to provide solutions for the optimization of comfort and energy demand by means of actions concentrated on the building envelope.
It is not clear why the selected building can be classified as a typical Mediterranean building. What are the characteristics of this building which are common to other Mediterranean buildings? Vernacular mediterranean prototypes make use of thermal mass, of shading, of the correct sizing of windows, of the use of typical local materials, which are not present in this building.
More importantly, the thermal properties of the envelope are very far from reality and from any reasonable value for such as this building. Most of the values are not only not compliant with basic energy codes valid in Mediterranean areas since 1990s, but also not compliant with the NZEB standard.
Table 5, if applied as described to the energy model, produces several inaccuracies. As an example, a 24h occupancy of 0.66 p/m2 for the dining room is not only unrealistic but also largely affects the results.
Also the building services considered are not typical for the areas considered, or at least unrealistic for buildings with very poor building envelope characteristics.
As a result the paper is just a theoretical exercise without any practical use. Such as this exercise cannot be performed without a real calibration of the model.

Author Response

The title is misleading. The paper is not on the definition of a new methodology for the assessment of the integration between BIM and BEM, but is just on the use of commercial software (that already provide integration between BIM and BEM) on a case of study. Moreover the case of study selected is far from being considered a "energy-efficient" building.

The title was updated to reflect the scope of the research better:

BIM and BEM methodologies integration in energy-efficient buildings using experimental design.

The novelty and contribution to knowledge is the application of the Experimental Design methodology, highlighted in Introduction, lines 85 to 89.

All the information about the incorporation of the experimental design is found throughout the article, especially in the case of study: validating the flowchart of the methodology, line 686 to 697; in the Results, figures 8a, 8b, 8c, 8d, and 8e; and in the Complementary file.

Figure 8. Correlation significance between the factors considered. a) Armação dos Búzios; b) Capri; c) Punta Cana; d) Dubai; e) Sydney.

The information about the NZEB parameters was changed to explain that, despite the common goal in the energy performance of buildings, it couldn't be achieved; however, the research pursuits the energy efficiency of a building. This information can be found in Background, lines 205 to 209.

Although the major goal of energy efficiency is usually to achieve NZEB standards, in this research, the evaluation of the energy performance and the diminution of the energy loads considers only the minor possible by the Autodesk Insight results, managing some EUI values that do not reach the NZEB parameters but approaches an energy-efficiency framework.

The paper largely describes issues related to Nearly-Zero Energy Buildings, but avoiding to provide solutions for the optimization of comfort and energy demand by means of actions concentrated on the building envelope.

The solutions proposed are not related to suggesting changes in the building envelope, but are related to study the effects of the building's appliances, as seen in Introduction, lines 27 to 31, and lines 85 to 89; Background, lines 103 to 108, and lines 331 to 334.

Part of the energy consumption in buildings is a consequence of highly demanding appliances and utilities employed to offer comfort and accomplishing daily tasks (heating, cooling, lighting, computer devices, cooking, and others). However, some inconvenience could appear when energy demand exceeds the user's economic and environmental parameters including risk for traditional power grid systems [1], [2].

Power demand is increasing daily in part due to the appliances (which includes air conditioners, heaters, lamps, fans, hairdryers, irons), reaching up to 40% of total energy demand only for residential buildings [15], [16]. Energy wasting in buildings is associated with inefficient systems or appliances, old-fashioned envelopes and space distribution, lack of control systems, and misguided consumption usage [13].

However, an experimental design had not been applied to analyze the result of the interoperability between BIM and BEM methodologies statistically when studying house appliances. In this case, the approach exposed permits the evaluation of the factors (home appliances) that most affect the energy loads in the case of the study presented.

Moreover, all the information exposing that the research was developed to pursue an NZEB parameter was changed, suggesting the pursuit of an energy-efficient model; it could be found throughout the paper.

It is not clear why the selected building can be classified as a typical Mediterranean building. What are the characteristics of this building which are common to other Mediterranean buildings? Vernacular mediterranean prototypes make use of thermal mass, of shading, of the correct sizing of windows, of the use of typical local materials, which are not present in this building.

The justification of the selection was upgraded and can be found in Background, lines 210 to 224.

A house influenced by the Mediterranean architectural style was selected to develop this research. They are inspired by Spanish and Italian architecture. They usually show stucco or plaster exterior and red clay roof tiles, ornate archways, ceramic tiles on the floor, and exposed columns and beams [28]. The reason to choose this house was to analyze the possible energy performance advantages present in houses influenced by this architectural style, which present some attributes such as large windows, open spaces, balconies, high ceilings, shading devices, clay roofs tiles, and clear colors. These characteristics could significantly improve indoor comfort conditions by keeping a refreshed and pleasant environment inside the house [29], [30].

Houses inspired in this architectural style are globally widespread in warm climates, in latitudes ranging from the Mediterranean Sea (in the north) to the Australian East Coast (in the south). Also, they are common in mid/high-class neighborhoods; it gives an idea of performing increased energy loads due to its inhabitants' climatic conditions and behavior, but with a high potential to diminish the energy loads and increase the energy efficiency, due to the reasons mentioned above related to the architectural influence.

All the information suggesting that the house was following a Mediterranean house was adapted all over the paper to suggest the inspiration of this architectural style when idealizing the physical model.

More importantly, the thermal properties of the envelope are very far from reality and from any reasonable value for such as this building. Most of the values are not only not compliant with basic energy codes valid in Mediterranean areas since 1990s, but also not compliant with the NZEB standard.

The information about the thermal properties present on the materials of the envelope were taken from research made in Brazil by Castro Ferreira, where the researcher obtained all the values via laboratory experimentation. This information can be found in the Case study: validating the flowchart of the methodology, lines 529 to 531.

However, the thermal variables of the constitutive materials, which directly affect the house's energy performance, are exhibited in Table 1nd come directly from the study made by Castro Ferreira in Brazil [49].

Table 5, if applied as described to the energy model, produces several inaccuracies. As an example, a 24h occupancy of 0.66 p/m2 for the dining room is not only unrealistic but also largely affects the results.

It does affect the values of the energy loads, but that is how the study was idealized: first, the values are defined by default by REVIT; then, in accordance with Insight results and applying Experimental Design, these values were upgraded to fix the minimal of them suggested after the statistical analysis.

The information about the EUI values, when minimized the loads, can be found in Results, line 790, figure 7.

Also the building services considered are not typical for the areas considered, or at least unrealistic for buildings with very poor building envelope characteristics.

The building selected is a representation of a house influenced by the Mediterranean architectural style typical from Brazil but reproduced in different locations worldwide to interpret different factors that can affect their performance due to appliance's energy loads.

This information can be found in case of study: validating the flowchart of the methodology, lines 647 to 654.

Five different locations were selected for the analysis to represent five different climatic conditions and evaluate how the house's response follows the same physical and energetic adjustments. It was used the Köppen-Geiger climate classification system. The locations were Armação dos Búzios, Rio de Janeiro state - Brazil, with Tropical Savanna climate; Capri Island, Campania region – Italia, presenting a Mediterranean Hot Summer climate; Punta Cana, La Altagracia province – Dominican Republic, exhibiting a Tropical Monsoon climate; Dubai – Dubai – United Arab Emirates, with Hot Desert climate; and Sydney, New South Wales state, manifesting a Humid Sub-Tropical climate.

As a result the paper is just a theoretical exercise without any practical use. Such as this exercise cannot be performed without a real calibration of the model.

We appreciate your comments

Author Response File: Author Response.pdf

Reviewer 3 Report

In general, the study provides interesting insights into how to analyze energy consumption in buildings with BIM and BEM methods. Writing/information is unbalanced (too much specificity in some cases and too little in others). Validating the simulation results is crucial as well.

Point 1: The introduction (line 23) could be more straightforward in explaining why BIM and BEM methodologies are necessary for energy consumption modeling. The introduction is too complicated and confusing to understand. Finding it difficult to find the problem statement. It would be better to simplify the introduction.

Point 2: The materials and method section (line 138) is too confusing, similar to the Introduction section. A more structured paragraph with a different topic sentence would be easier to understand.

Point 3: The graphics on Figures 3 and 4 are not clear enough to be useful. It would be more useful if dimensions and other information were more complete.

Point 4: The conclusions (line 653) and discussion (line 587) sections are underdeveloped. It is important to present the results of the simulation and analysis in context and quantify the potential impact of the results.

Point 5: Validation data should be presented clearly by the authors.

Author Response

We appreciate your comments, and we certainly made some adjust to upgrade the paper towards a more understandable lecture. The validation procedure was added in the Results section, figures 8a, 8b, 8c, 8d, and 8e, with a previous explanation of the statistical method applied in the Case study: validating the flowchart of the methodology, lines 686 to 697, and lines 750 to 758.

The experimental design applied can be summarized by the following steps: First, the goal's definition, which implies the possible energy reduction according to the suggestion items emitted by Autodesk Insight's results. Second, the determination of which results to take into account to manipulate and carry on the simulations. Third, were defined the factors and levels that affected the EUI and created the equation that represents the energy consumption. Fourth, were developed the forty-two simulations that combine the different factors and levels defined above. Fifth, the different results were analyzed and determined the behavior of the EUI in the different cities in accordance with the combination of factors and levels. And lastly, it was conducted statistical analysis (analysis of variance and linear regression) to verify the accuracy of the study and determine the significance of the results obtained (the software Minitab was adopted for this calculation to conduct the main effects plot by integrating the statistical process.

It is necessary to apply two statistical models to perform the result analysis: an analysis of variance and linear regression. The main goal of the analysis of variance (ANOVA), is to study the total dispersion observed in the resulting values of the selected characteristics, and to attribute it to the examined factors in order to derive their significance over the system analyzed [38]. The linear regression is the equation that assumes the linear relationship between the input variables (independent variables) and the single output variable (response variable) [40]. In this case, Minitab software was utilized to carry out the statistical analysis, giving Paretos' and residuals diagrams, variance and p-values, coefficient values, and the final regression equations.

Point 1: The Introduction (line 23) could be more straightforward in explaining why BIM and BEM methodologies are necessary for energy consumption modeling. The Introduction is too complicated and confusing to understand. Finding it difficult to find the problem statement. It would be better to simplify the Introduction.

The whole Introduction was modified to a more straightforward content. The problem statement was explained in the Introduction, lines 37 to 42; and 48 to 56.

The BIM-BEM interoperability becomes necessary to incorporate energy performance analysis in the early steps of the building project. It permits the calculation of Energy Use Intensity (EUI), allocation of annual energy budgets, predict the year energy consumption, comparing HVAC systems and appliances, utility schedules, and defining energy and comfort standards. It is all for helping in the decision-making process to select competent and sustainable models towards energy-efficient buildings [3], [4].

The whole Materials and Method Section (line 335) was also upgraded to simplify the explanation. Also, a new section named "Background" (line 99) was created to explain the state of the art and separate the theoretical information from the experimental information.

Point 3: The graphics on Figures 3 and 4 are not clear enough to be useful. It would be more useful if dimensions and other information were more complete.

Figures 3 and 4, now named figures 4 and 5, were size increased and added some tags and dimensions for a better understanding, as seen next:

(a)

(b)

Figure 4. Floor plans. a) first floor; b) second floor.

(a)

(b)

Figure 5. HVAC zones. a) Zone 1; b) Zone 2.

The conclusions and discussion sections were also modified to a straightforward explanation. Figures 9 and 10 explain the impacts of the results.

(a)

(b)

Figure 9: Annual electric end-use charts obtained by Green Building Studio. a) Sydney; b) Dubai.

(a)

(b)

(c)

Figure 8: Annual electric end use charts obtained by Green Building Studio. a) Punta Cana; b) Armação dos Búzios; c) Capri.

Point 5: Validation data should be presented clearly by the authors.

Validation data was incorporated in the results (figures 8a, 8b, 8c, 8d, and 8e), and in the Complementary file, where is presented the whole statistical result's simulation (, Analysis of Variance, Linear regression equation, Pareto's charts, and Residual plots).

The comments were added in Results, lines 897 to 934, and in the Discussion and Conclusion sections.

When interpreting the sequences of every seven simulations in figure 7, it can be perceived that the energy use intensity seems to decrease when increasing the efficiency of the HVAC system, which is the most representative of the factors. In addition, it can be evident that for tropical climates, the energy consumption in lesser than for desert or sub-tropical climates. However, when the results are introduced in the Minitab software for its linear regression analysis, and making a comparison of the five different Pareto's diagrams, in accordance with the five different cities selected, the results show that HVAC systems is not the only factor that affects the most the EUI results, but also power loads.

Figures 8a, 8b, 8c, and 8d expose that the most representative effects are for factor B, which is referred to as the Power Loads. It could mean that the power loads increase the EUI equation (which can be demonstrated in figures 8a, 8b, 8c, and 8d that exposes the equations for EUI) in a form even higher than does by the HVAC systems or the lighting loads. Nonetheless, when checking Figure 8e for Sydney, it can be perceived that factor B decreases its effect. It could mean that power loads are not as representative as for the other locations for this specific location. It does not mean that the significance of the other factors (HVAC system or Lighting) is superior to for power loads; it could mean that the significance of those factors increases for Sydney.

As seen in the Complementary File, all the p-values of the statistical linear regression analysis are lower than 0.05; it means that the results are truly representative. The normal probability plot of the residuals for Búzios and Capri shows a one-direction tail, it means a normal distribution of the results. For Punta Cana and Sydney, the distribution softly draws an inverted S-curve, which expresses that the distribution of the results is not so normal, due to the variance of the data. For Dubai, the residuals are grouped in vertical lines, which means a more significant variation of the values.

When comparing the residuals versus its fits (Complementary File), it can be seen that the points are randomly distributed around the 0 for Dubai and Sydney; more distributed closer to the lower values of EUI in Búzios and Punta Cana, and more distributed closer to the higher values of EUI in Capri. It means that more variance of the results is found for minor EUI results in Búzios and Punta Cana and major EUI results in Capri.

For Capri and Punta Cana, residuals versus order are randomly distributed around the graphic, which means a normal behavior of the EUI results. For Búzios and Sydney, the graphic shows a tendency to reduce residual values in the middle, having more accurate values in medium EUI results. For Dubai, the graphic displays a tendency of rising residuals in the middle values, which means that the extreme values of EUI present more accuracy. When analyzing these results with the other plots (appendix 1), it is possible to comprehend that, even with some variation, the distribution of the residuals is relatively symmetrical, displaying a normal behavior in

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Dear authors, I appreciate your attempt to improve the paper. However, in my viewpoint the manuscript does not have the required degrees of novelty and depth needed for being published in an international journal. I still think that the selected case of study is far from being considered a Mediterranean architectural archetype and that it is useless, under a practical viewpoint, to push on adopting high-performing building services with poor construction technologies. If you were to perform such as this theoretical exercise, you should have selected a building with high-performing envelopes.

Reviewer 3 Report

This paper’s methodology, structure, and clarity have greatly improved from the original version.

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BIM and BEM Methodologies Integration in Energy-Efficient Buildings Using Experimental Design

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