A BIM-integrated Thailand building energy code framework for building envelope thermal requirements in a conceptual design stage

Abstract

This work presents the development of a building energy code (BEC) of Thailand compliance framework during conceptual design stages (CDS) using building information modeling (BIM) through visual programming language (VPL). The proposed method included five steps: (i) establish a commercial building reference; (ii) develop a BIM base model; (iii) create a BIM overall thermal transfer value (OTTV) and roof thermal transfer value (RTTV) calculation model with VPL scripts; (iv) test the BIM framework with various scenarios; and (v) verify the results with Thailand BEC software. The findings revealed an insignificant difference between the results from the BIM calculation model and those estimated by the Thailand BEC software. This study demonstrated that using BIM with VPL for OTTV and RTTV calculations during the CDS may be implemented automatically, which could help designers make timely decisions to comply with Thailand’s BEC, considering building design, materials, and fenestration ratios.

Keywords

Building information modeling building energy code conceptual design stage thermal transfer value visual programming

Introduction

In tropical climate zones, the average temperature has been increasing every year. The temperature difference between indoor and outdoor spaces affects the demand for air conditioning. Therefore, building envelope performance plays an important role throughout the day as a heat barrier, maintaining indoor comfort.¹ Analyzing a building’s thermal performance is crucial, particularly in tropical climates where high temperatures persist throughout the year.

In Asia, a building’s thermal performance is often measured using an overall thermal transfer value (OTTV) with a roof thermal transfer value (RTTV). They are used as prime values to calculate the average rate of heat transfer through a building envelope, which refers to wall and roof.^2–6 The OTTV and RTTV are widely used in tropical climate zones, including such countries as Singapore, Malaysia, and Thailand.⁷ The OTTV and RTTV standards apply only to air-conditioned buildings, with the objective of achieving a building envelope design that reduces external heat gain, thereby reducing the cooling load on the air conditioning system.⁶ These indicators are based on simplified models used to examine the annual average heat gain through the building under typical local climate conditions.⁵ The OTTV and RTTV models are similar to the Envelope Thermal Transfer Value (ETTV) model, except the model coefficients are different.⁸

Building Information Modeling (BIM) software is commonly used in the Architecture, Engineering, and Construction (AEC) industry.⁹ BIM can also be applied to calculate OTTV and RTTV by combining it with Visual Programming Language (VPL) to write an algorithm for modeling calculations. It can be used to automatically extract the physical model and materials from BIM database. The advantages of BIM over the existing methods used in OTTV and RTTV calculation, include reduced time and human error in input data processing.^7,10 BIM allows for digital modeling with high accuracy, ensuring that the data used in calculations is precise. Also, BIM can be used for various performance analyses, assisting in design decision-making and serving as a decision-support system.¹¹

In Thailand, the OTTV and RTTV calculation reports are required for a new building construction permit. They must fulfill the government’s standard criteria based on Thailand’s Building Energy Code (BEC). The Thailand Ministry of Energy recommends calculating OTTV and RTTV using BEC software, which aids in investigating building design performance in terms of energy consumption.¹² Despite using the BEC software for calculations, it is important to acknowledge its limitations. The BEC software is a standalone program that relies on manual input calculations by humans. In addition to that, it is time consuming and prone to human errors during the data input process because it requires several coefficient values for the process. Therefore, there is a clear necessity for tools that can accurately and efficiently calculate OTTV and RTTV in the context of Thailand.

Previous studies employed BIM to assist in these calculations, however, such studies focused on calculating OTTV and RTTV during the after-design stages.^7,10 Calculating OTTV and RTTV during this stage has some limitations, as designers have minimal flexibility to modify the design, being limited to adjustments in material type and thickness. Therefore, performing OTTV and RTTV calculations during the Conceptual Design Stage (CDS) could offer significant advantages, allowing designers to select the optimal design scenario to minimize heat gain into the building. To our knowledge, no studies have utilized BIM for calculating OTTV and RTTV during the CDS stage, which represents a research gap. Therefore, our current study aimed to develop a framework for calculating OTTV and RTTV by integrating BIM and VPL during the CDS.

Literature reviews

Building energy performance in Asia

The growing energy consumption in buildings is intensifying issues such as climate change and environmental degradation.¹³ Buildings consume a high proportion of energy, which has led to the emergence of numerous environmental problems that negatively impact human existence. The prediction of building energy use is essentially proclaimed to be a method for energy conservation and energy usage reduction.^14,15 Environmental parameters such as temperature, relative humidity, ventilation, and solar radiation influence building energy consumption. The heat gain through the opaque building envelope surfaces alone contributes to about 64% of the total electricity consumption.¹⁶ Therefore, the building envelope performance significantly influences the building energy consumption, and its environment interaction can be explained by the OTTV and RTTV.¹⁷ Understanding the thermal performance of building envelope design helps the designer make sure that the indoor environmental conditions are maintained. Meanwhile, designing envelope configurations of buildings, such as a proper roof, is important to reduce energy consumption in a building.^18,19

Similar to other regions, Southeast Asia uses OTTV and RTTV to evaluate the thermal performance of buildings. For instance, Malaysia and Indonesia set energy-efficiency standards for buildings and mandate their design and construction to meet these specific standards. These regulations include OTTV and RTTV restrictions for both residential and commercial buildings in order to decrease energy consumption and environmental effects. The Building and Construction Authority (BCA) in Singapore establishes energy-efficiency standards for buildings, mandating their design and construction to adhere to specific energy-efficiency standards, which include restricting OTTV and RTTV.^1,7,20 In Thailand, the Ministry of Energy set a Thailand BEC regulation for buildings and required that buildings be designed and constructed to meet certain energy-efficiency standards, including limits on OTTV and RTTV.¹²

OTTV and RTTV in Thailand BEC

The concept and application of OTTV and RTTV were first proposed by the American Society of Heating, Refrigerating, and Air-conditioning Engineers.²¹ In the concept of OTTV and RTTV calculation, it is usual to have two sets for a building. For instance, OTTV is used for the external building wall, and RTTV is used for the building roof. The OTTV and RTTV of an entire building are calculated by the average of the individual external walls and roofs of the building.³

Generally, a thermal transfer value is a value that represents a quantity of heat that is caused by the influence of the external atmosphere and the amount of solar radiation transferring into the building.² The basics of OTTV and RTTV values for calculating heat transfer are based on the theory of heat transfer.³ The Thailand Ministry of Energy’s code on envelope thermal performance for buildings intends to help architects and building professionals meet the envelope thermal performance criteria established in the building regulations.¹²

BIM and visual programming

BIM enters the scene to enhance the process of designing architecture.⁹ Currently, the AEC sector is interested in using BIM to optimize building design. Additionally, BIM features collaboration and communication tools, enabling users to share and access the model with other stakeholders.²² VPL tools are mostly used for parameterized geometry modeling, but they may also be utilized for other technical tasks.^23,24 Visual programming may be used for energy and shading analysis, for predicting and visualizing energy consumption in buildings, and for assisting in the optimization of building energy performance and thermal investigations.^22,25 As opposed to traditional approaches that employ data interchange formats such as Industry Foundation Classes (IFC), the usage of visual programming tools provides an efficient alternative solution for data interoperability in the BIM environment.^26,27 However, several studies have demonstrated the effectiveness of visual programming tools in automating BIM data extraction and manipulation.¹¹

CDS in building design process

The CDS stage is crucial in the building design process because it establishes the basis for the project’s eventual output. The designer will do a detailed site investigation during the CDS to evaluate the current circumstances, such as terrain, climate, landscapes, surrounding buildings, and infrastructure. They will also take into account zoning and land-use rules, which will influence the floor plan, size, and shape of the building.²⁸ It is critical to highlight that the CDS is iterative and flexible, allowing for modifications and updates as needed throughout the design process.²⁹

The CDS should use BIM Level of Development (LOD) 100, according to the Royal Institute of British Architects (RIBA),³⁰ with the model primarily focusing on the overall building layout, form and massing, site and context, building systems, and basic building components.³¹ Using BIM in the CDS can help to ensure that the building is designed and constructed in a way that is consistent, efficient, and cost-effective. Moreover, energy modeling should start during the CDS of the building design process to obtain the maximum feasible energy savings.²⁴ Decisions concerning the building’s form and construction materials are often made during this stage and can have a significant influence on how the building works.

Previous studies

There have been previous studies integrating BIM into OTTV and RTTV calculations. A study by Seghier et al. investigated building façade performance by integrating BIM and VPL for ETTV calculation in the design stage. They used Revit, Excel, and Dynamo as tools to analyze the envelope thermal performance according to the Green Mark rating systems of Singapore.²⁰ Another study conducted by Lim et al. indicates that integrating BIM and VPL together with a multi-objective optimization algorithm can be used to evaluate the performance of the OTTV value, which is suitable for the construction cost.¹ A recent study¹¹ used BIM, VPL, and genetic algorithms in MATLAB to reduce time- consumption in the design decision-making process and reduce the complications of design variables for selecting appropriate design alternatives for OTTV performance and cost-effective design options. The limitation of using the above-mentioned methods is that they are complicated because the data is exported from the main program to another program for recording and analysis. Similar studies conducted in Asia demonstrated that integrating BIM and VPL using the Application Programming Interface (API) in the design stage can automatically extract thermal and physical properties from the BIM materials database, as well as information for OTTV and RTTV calculation.^7,10 The summary of the methods used in previous studies is presented in Table 1.

Table 1.

Summary of methods used in previous studies.

Author (Year)	Parameters	Objective	Methodology
Seghier, et al. (2017)²⁰	ETTV	Appropriate façade configuration	Integrating VPL, BIM and Excel
Natephra, et al. (2018)⁷	OTTV/RTTV	Appropriate materials	Integrating VPL, BIM and API
Lim, et al. (2019)¹	ETTV	Appropriate materials and opening sizes	Integrating VPL, BIM, Excel and multi–objective optimization
Liu, et al. (2020)³²	ETTV	Appropriate green mark score	Developing a cloud-based BIM platform
Seghier, et al. (2022)¹¹	ETTV	Appropriate retrofit cost optimization	Integrating VPL, BIM, Excel and non–dominance sorting genetic algorithm II

Most previous studies^1,7,11,20,32 focused on integrating BIM to calculate the OTTV and RTTV in the after-design stage, this stage provided rich building design information for creating calculation models, according to the literature. This research gap arose from the fact that optimizing OTTV and RTTV performance after the design stage only involved changing the thickness and type of materials, making it challenging for designers to modify their designs when RTTV or OTTV fail to meet standard criteria. The current body of literature did not investigate RTTV and OTTV calculations using BIM in a CDS stage, which is a process that provides solutions for unclear and incomplete problem requirements such as designing, optimizing, and changing the building types, materials, sizes, colors, shapes, forms, and opening ratios. Therefore, this study aimed to address this knowledge gap.

Methodology

Methodology had three main stages. First, develop a BIM 3D physical model using Autodesk Revit and inputting the material’s thermal properties and absorptance. Second, develop a BIM framework using VPL scripts via Dynamo. Finally, test the BIM frameworks for each coefficient’s calculation, then test these models in a CDS of building design with various design scenarios, test with different building types, test design optimization and verify the results using Thailand BEC software.

Developing a BIM physical model and data input

A commercial building case study was set based on conventional building design in the CDS. A BIM 3D model was developed as a base case design using Revit. The base case design was created in the CDS with three stories and a 2000m² floor area. The building’s dimensions are 26m. in width and 26m. in length. The building height was 12m. The window-to-wall ratio (WWR) was 9%. The building comprises opaque walls, fenestration walls, and an opaque roof. Thermal conductivity (k), density (ρ), specific heat (c_p), and heat absorptance value (α) of materials were input into the BIM model (Table 2).

Table 2.

Building envelopes and material thermal properties for the base case design.

Building envelopes	α	Materials	k (W/m °C)	ρ (kg/m³)	c_p (J/kg °C)
Opaque wall	0.3	Cement screed	0.720	1860	840
Opaque wall	0.3	Lightweight concrete block	0.346	1120	840
Fenestration wall	n/a	Tinted glass	0.913	2500	880
Opaque roof	0.3	Concrete	1.442	2400	920
		Fiberglass insulation	0.035	24	960
		Gypsum plaster board	0.230	720	1090

Developing a BIM calculation framework

There were two main steps involved: (i) VPL scripts were used for BIM Revit database extraction. Physical properties, material thickness (Δx), and the material’s thermal properties were extracted from the BIM Revit database through the 3D BIM model; (ii) VPL scripts were used to perform the BIM framework in solving the OTTV and RTTV equations based on the OTTV and RTTV equations of Thailand’s BEC.

The following steps were used in creating the OTTV and RTTV calculation models; step 1: calculate heat transfer coefficient through opaque walls (U_w) and opaque roofs (U_r); step 2: estimate window to wall ratio (WWR) and skylight to roof ratio (SRR); step 3: calculate temperature difference equivalent value (TD_eq); step 4: calculate heat transfer coefficient through fenestration windows and skylights value (U_f); step 5: determine temperature difference (ΔT); step 6: input solar heat gain coefficient value (SHGC); step 7: calculate shading coefficient (SC); step 8: estimate effective solar radiation value (ESR); step 9: calculate OTTV and RTTV.

For the whole building OTTV calculation, it is necessary to consider all the wall areas included in the building envelope using the following equation:

O T T V = \frac{(A_{w 1}) ({O T T V}_{1}) + (A_{w 2}) ({O T T V}_{2}) + \dots + (A_{w i}) ({O T T V}_{i})}{A_{w 1} + A_{w 2} + \dots + A_{w i}}

where OTTV is the overall thermal transfer value, OTTV_i is the thermal transfer value for the individual wall component, and A_wi is the individual wall area. The OTTV_i equation is a method used to evaluate the wall thermal performance of buildings and can be calculated as follows:

{O T T V}_{i} = (U_{w}) (1 - W W R) ({T D}_{e q}) + (U_{f}) (W W R) (Δ T) + (W W R) (S H G C) (S C) (E S R)

For the whole building RTTV calculation, it is necessary to consider all the roof areas that were included in the building envelope. The RTTV value can be calculated using the following equation:

R T T V = \frac{(A_{r 1}) ({R T T V}_{1}) + (A_{r 2}) ({R T T V}_{2}) + \dots + (A_{r i}) ({R T T V}_{i})}{A_{r 1} + A_{r 2} + \dots + A_{r i}}

where RTTV is the roof thermal transfer value, RTTV_i is the thermal transfer value for the individual roof component, and A_ri is the individual roof area. The RTTV_i equation is used to calculate the roof thermal performance of buildings and can be estimated as follows:

{R T T V}_{i} = (U_{r}) (1 - S R R) ({T D}_{e q}) + (U_{f}) (S S R) (Δ T) + (W W R) (S H G C) (S C) (E S R)

Testing and verification

To ensure the correctness of the findings of the BIM OTTV and RTTV calculation models, the results were tested and verified using the Thailand BEC software provided by the Ministry of Energy of Thailand in the following domains: (i) testing with five distinct design scenarios (based case design, design scenarios 1, 2, 3, and 4) to demonstrate the application of the building design in the CDS stage. (ii) testing with various building types, including commercial, office, and residential buildings, to illustrate the utilization of multiple building types; (iii) optimizing α value, density, and DSH value to test the use of optimizing designs related to building walls, roofs, material types, material thicknesses, and building colors.

The specific design scenarios were selected based on their relevance to real-world applications, representing a range of typical conditions aligned with LOD 100 in building design. These scenarios encompass different building forms, opening ratio, building types, and material conditions, which are critical variables in thermal performance studies. Additionally, the scenarios were chosen in alignment with design guidelines from Thailand BEC, ensuring that they reflect practical and common challenges in conventional buildings. By covering diverse design configurations, these scenarios provide a comprehensive framework for testing and evaluating the effectiveness of the BIM framework (Figure 1).

Figure 1.

3D BIM modeling of building geometry and BIM physical properties for five different case studies, optimizing α-value, and density and specific heat (DSH) of materials.

Results

BIM modeling and material thermal properties

The results demonstrated that BIM Revit successfully generated building geometries for OTTV and RTTV calculation, including (i) opaque and fenestration walls with a 90-degree slope in the North, South, East, and West directions, and (ii) a flat roof with a 0-degree slope (Figure 2). Additionally, BIM Revit allowed for the direct input of material thermal properties, including thermal characteristics and absorptance, into the model (Figure 3).

Figure 2.

3D BIM modeling of building geometry and BIM physical properties for the base case design.

Figure 3.

Inputting material thermal properties into the BIM model. (a) Inputting the k value, ρ value, and c_p value in Revit; (b) Inputting the heat absorptance value in Revit.

BIM-data connection and extraction

The Revit API was used for automatically accessing, connecting, and extracting the thickness value (Δx) and material’s thermal properties, such as k-value, ρ-value, c_p value, and α-value, from the BIM model and Revit material library database (Figure 4). The Revit API can be used by the Family Type Compound Structure Layers node and the Material Thermal Parameters node created by Clockwork or using Python coding.

Figure 4.

BIM material thermal properties and heat absorptance value connection and extraction. (a) k value output; (b) ρ value output; (c) c_p value output; (d) heat absorptance value output.

BIM OTTV and RTTV calculation framework

The BIM framework was first created and connected to the BIM model of the base case design (Figure 5). At the first step, the North Wall OTTV_i value was the target value. To calculate the OTTV_i value, code block nodes were used to create and connect all major nodes, such as the U-equation node, WWR node, SSR node, TD_eq equation node, ΔT node, SHGC node, SC node, ESR node, and OTTV_i equation node, to support the OTTV_i value calculation. In the second step, after completing the North Wall OTTV_i calculation, the script then repeated the function for the East, South, and West Wall and Roof to get the all-directions wall OTTV_i and roof RTTV_i value calculation. The last step involved connecting all nodes to the OTTV and RTTV equation nodes, enabling the calculation model to proceed and automatically compute the OTTV and RTTV values.

Figure 5.

Dynamo scripting logic and data flow structure for BIM OTTV and RTTV calculation frameworks.

U-value

The U-value (U_w, U_r, and U_f) was computed by the following steps: (i) calculated the value of material thermal resistance (R-value) by input Δx-value and k-value into the R-equation node (Figure 6(a)) to get the R-value (R_w, R_r and R_f), (ii) input the outer thermal resistance value (R_o), R-value and the inner thermal resistance value (R_i) into the R_t equation node (Figure 6(b)) to get the R_t value, and (iii) input the R_t value into the U equation node (Figure 6(c)) to obtain the output of U-value. The code in the R-equation node was scripted based on the following equation:

R = \frac{Δ x}{k}

Figure 6.

Visual programming script and coding for the U-value (U_w, U_r, and U_f) calculation. (a) R-equation coding for the R-value; (b) R_t equation coding for the R_t value; (c) U-equation coding for the U-value.

The code in the R_t equation node was scripted based on the following equation:

R_{t} = R_{o} + \frac{{Δ x}_{1}}{k_{1}} + \frac{{Δ x}_{2}}{k_{2}} + R_{a} + \frac{{Δ x}_{n}}{k_{n}} + R_{i}

The following equation served as the basis for scripting the code in the U-equation node.

U = \frac{1}{R_{t}}

WWR and SRR

The WWR value was calculated by inputting all window areas (fenestration materials) and the wall area (opaque materials) into the WWR equation node to obtain the output of the WWR value. The code in the WWR node was scripted based on the following equation:

W W R = \frac{w i n d o w a r e a s}{w a l l a r e a}

The SRR value was calculated by inputting all skylight areas (fenestration materials) and the roof area (opaque materials) into the SRR equation node to obtain the output of the SRR value. The scripted code in the SRR node relied on the following equation:

S R R = \frac{s k y l i g h t a r e a s}{r o o f a r e a}

TD_eq value

TD_eq value was computed by the following steps: (i) calculated the value of individual density and specific heat of material (DSH_i) for wall and roof by input the ρ_i-value, c_pi value and Δx_i-value into the DSH_i equation node to get the DSH_i value (Figure 7(a)), and then the values were sent into the DSH equation node) to get the DSH value (Figure 7(b)), and (ii) input the DSH value, α-value, direction, inclination, and the building type into the TD_eq equation node to obtain the output of TD_eq value (Figure 7(c)). The scripted code for the DSH_i equation node relied on the following equation:

{D S H}_{i} = (ρ_{i}) (C_{p i}) ({Δ x}_{i})

Figure 7.

Visual programming script and coding for the TD_eq value. (a) DSH_i equation coding for the DSH_i value; (b) DSH equation coding for the DSH value; (c) TD_eq equation coding for the TD_eq value.

While the code in the DSH equation node was scripted based on the following equation:

D S H = ({D S H}_{1}) + ({D S H}_{2}) + \dots + ({D S H}_{n})

The Thailand Ministry of Energy provided the database for the scripted code in the TD_eq equation node, which computed the result from a regression analysis of the TD_eq value. The TD_eq equation node could calculate the TD_eq values for all three major building types in the BEC, such as commercial, office, and residential buildings. The regression analysis showed that the R² values for commercial, office, and residential buildings were 0.9507, 0.9583, and 0.9605, respectively (Figure 8). After receiving all variables values such as DSH value (X), α-value (Y), direction (Z), inclination (W), and the constants value (a_n to p_n) from the regression analysis, the TD_eq equation node could be automatically computed the TD_eq value by the following regression equation:

{T D}_{e q} = a + b X + c Y + d Z + e W + f X Y + g X Z + h Y Z + i X W + j Y W + k Z W + l X Y Z + m X Y W + n X Z W + o Y Z W + p X Y Z W

Figure 8.

Regression analysis of the TD_eq value for the three building types. (a) Regression analysis of the TD_eq value for commercial buildings; (b) Regression analysis of the TD_eq value for office buildings; (c) Regression analysis of the TD_eq value for residential buildings.

ΔT, SHGC, SC and ESR value

The coefficient’s values, such as ΔT, SHGC and SC, were added to the BIM calculation framework by a number in a Code Block node (Figure 9). The Thailand BEC regulation referenced this number. The SC value referred to the shading coefficient value of shading devices. In terms of the ESR value, it was computed by inputting building type, inclination, and direction values into the ESR node to obtain the output of the ESR value. The code in the ESR node was scripted to record the ESR database of each building type, inclination, and direction given by the Thailand BEC for the ESR value calculation. In the database, the ESR node used a matching technique to find the correct output of the ESR value.

Figure 9.

Visual programming script and coding for the ΔT, SHGC, SC, and ESR values. (a) ΔT coding for the ΔT value; (b) SHGC coding for the SHGC value; (c) SC coding for the SC value; (d) ESR coding for the ESR value.

OTTV and RTTV

OTTV and RTTV were computed by the following steps: (i) input all direction’s walls OTTV_i (North, East, South, and West) and roof RTTV_i coefficients such as U-value, WWR and SRR value, TD_eq value, U_f value, ΔT-value, SHGC value, SC value, and ESR value into the OTTV_i and RTTV_i equation node to get the OTTV_i and RTTV_i value, and (ii) input the OTTV_i, RTTV_i, A_wi and A_ri values into the OTTV and RTTV equation node to obtain the output of OTTV and RTTV values (Figure 10).

Figure 10.

Visual programming script and coding for the OTTV and RTTV values. (a) OTTV_i and RTTV_i equation coding for the OTTVi and RTTVi values; (b) OTTV and RTTV equation coding for the OTTV and RTTV values.

Testing and verification

Base case design

The BIM framework was tested using the base case design. The results showed the OTTV_i values for the building walls: 30.22W/m² for the North wall, 31.90W/m² for the East wall, 35.04W/m² for the South wall, and 33.54W/m² for the West wall. The overall OTTV value for the walls was 32.68W/m². The RTTV_i value for the building roof in all directions was 5.71W/m², with the overall RTTV value for the roof being 5.71W/m² (Table 3).

Table 3.

OTTV and RTTV testing results for the base case design.

OTTV and RTTV coefficients	OTTV				RTTV
OTTV and RTTV coefficients	North	East	South	West	All directions
U_w and U_r value	2.080	2.080	2.080	2.080	0.451
WWR and SRR value	0.130	0.130	0.130	0.130	0
TD_eq value	9.220	9.004	9.652	9.436	12.654
U_f value	5.862	5.862	5.862	5.862	n/a
ΔT value	5	5	5	5	n/a
SHGC value	0.54	0.54	0.54	0.54	n/a
SC value	1	1	1	1	n/a
ESR value	133.52	162.04	189.27	173.98	n/a
OTTV_i and RTTV_i value	30.22	31.90	35.04	33.54	5.71
A_wi and A_ri value	362.04	362.04	362.04	362.04	688.538
OTTV and RTTV value	32.68				5.71

Different design scenarios for commercial buildings

The results of testing different design scenarios showed close agreement between the OTTV values of the BEC software and the BIM framework. In the base case design, the OTTV values were 32.79W/m² (BEC) and 32.68W/m² (BIM). For Design Scenarios 1–4, the OTTV values ranged from 27.07 to 30.82W/m² in the BIM framework and 27.19 to 30.84W/m² in the BEC software. The RTTV values were 5.71W/m² (BIM) and 5.54W/m² (BEC). All scenarios met Thailand’s BEC Regulation requirements (OTTV ≤ 40W/m² and RTTV ≤ 8W/m²). The differences between the OTTV values ranged from 0.02 to 0.21W/m², while the RTTV difference was 0.17W/m². (Figure 11).

Figure 11.

OTTV and RTTV results and verifications of the BIM framework and BEC software for commercial building designs in the CDS.

Office buildings and residential buildings

For office buildings, all scenarios met Thailand’s BEC Regulation requirements (OTTV ≤ 50W/m² and RTTV ≤ 10W/m²). The OTTV values differed between the BEC software and the BIM framework by 1.13–1.30W/m², while the RTTV difference was 0.58W/m². (Figure 12(a)). For residential buildings, all scenarios also complied with the BEC Regulation (OTTV ≤ 30W/m² and RTTV ≤ 6W/m²), with OTTV differences of 0.17–0.22W/m² and an RTTV difference of 0.13W/m² between the two methods (Figure 12(b)).

Figure 12.

OTTV and RTTV results and verifications of the BIM framework and BEC software for the other building types in the CDS. (a) OTTV and RTTV results and verifications of the BIM framework and BEC software for office buildings; (b) OTTV and RTTV results and verifications of the BIM framework and BEC software for residential buildings.

Optimizing and verification

Absorptance

In the α-value optimization, the base case design emerged as the best scenario, yielding the lowest OTTV and RTTV values. The OTTV values were 32.79W/m² (BEC software) and 32.68W/m² (BIM framework), while the RTTV values were 5.54W/m² (BEC) and 5.71W/m² (BIM). The differences between the BEC software and BIM framework ranged from 0.01 to 0.25 for OTTV and 0.17 to 0.77 for RTTV across all cases (Figure 13).

Figure 13.

OTTV and RTTV results and verifications of the BIM framework and BEC software for optimizing the heat absorptance of the base case design of commercial buildings in the CDS.

DSH value

In the DSH value optimization, design scenario four was identified as the best. The OTTV values were 20.98W/m² (BEC software) and 21.06W/m² (BIM framework), while the RTTV values were 4.54W/m² (BEC) and 4.22W/m² (BIM). Across all scenarios, the differences between the BEC software and BIM framework ranged from 0.08 to 0.17 for OTTV and 0.02 to 0.37 for RTTV (Figure 14).

Figure 14.

OTTV and RTTV results and verifications of the BIM framework and BEC software for optimizing the DSH value of the base case design of commercial buildings in the CDS.

BIM framework verification

The verification results demonstrated the percentage differences between the BEC software and the BIM framework. For commercial buildings, the differences ranged from 0.06% to 0.68% for OTTV and 3.02% for RTTV. For office buildings, the differences were 3.01% to 3.98% for OTTV and 10.49% for RTTV. For residential buildings, OTTV differences ranged from 0.93% to 1.37%, and RTTV differences were 3.75%. In absorptance optimization, OTTV differences ranged from 0.03% to 0.50% and RTTV from 3.02% to 5.33%. For DSH optimization, OTTV differences were 0.34% to 0.42%, and RTTV ranged from 0.34% to 7.41%. (Figure 15).

Figure 15.

OTTV and RTTV results and verifications of the BIM framework.

Discussion

The result of this study found that the method of integrating BIM and VPL provided a flexible system that can be utilized for calculating the OTTV and RTTV performance of buildings in the CDS. Furthermore, this study provided a prototype of a BIM framework based on Thailand’s BEC. The study findings are summarized as follows: The 3D BIM model in Revit, including walls, windows, and roof, was necessary information for the BIM framework. The 3D BIM physical model contains specific values that could be used in calculations. Moreover, the Revit materials library also provided a dataset of materials that comprised the material’s thermal properties, such as k-value, ρ-value, and c_p value, which were required for the calculation.

Compared to the limitations of the BEC software, the BIM framework offered several advantages. First, it provided a more straightforward and user-friendly interface for building professionals. The BIM framework simplified the process by requiring only two steps: selecting a building type and choosing walls, windows, and roof. Once these inputs were provided, the BIM framework automatically extracted and computed the necessary data, thereby reducing the time required and minimizing the potential for human error in comparison to the BEC software. Additionally, the BIM model incorporated a method for calculating TD_eq values through regression analysis. This technique enhanced the flexibility of TD_eq computation by enabling the manipulation of variables such as DSH, absorptance, direction, slope, and building type. Furthermore, this model supported design optimization by facilitating the modification of materials, material thickness, and building colors across all three building types recognized by the Thailand BEC standards.

The model’s calculations yielded results comparable to those from the Thailand Ministry of Energy’s Thailand BEC software. The results demonstrated that the BIM framework were efficient and accurate in computation. However, there was a slight difference of less than 1% in the OTTV calculation and less than 4% in the RTTV calculation. This discrepancy may be attributed to differences in computational techniques. The BIM framework employed regression analysis for TD_eq prediction, whereas the BEC software relied on fixed values based on predefined data. In addition, variations in the number of decimal places used in the calculations could also contribute to the differences in the output results. However, this minor variation is deemed acceptable. In practice, a difference of less than 10% is generally considered within acceptable limits. For practical implementation, this framework could be utilized in place of BEC software, providing efficient performance since the conceptual design stage. In addition, the BIM framework could assist designers in simultaneously optimizing building designs and monitoring thermal performance. Furthermore, the BIM framework could help designers in decision making. For example, during the CDS, the BIM framework can be used to calculate and report the OTTV and RTTV based on the building’s shape, openings, and materials input by the designers. This process was user-friendly and could be utilized by designers who may not specialize in thermal performance. Moreover, the BIM framework allowed designers to identify the most efficient design in terms of heat protection by comparing the building thermal performance of various design scenarios. This enhanced decision-making efficiency, enabling designers to select the most suitable scenario for further design development. To our knowledge, this is the first study investigating the value of OTTV and RTTV in the CDS using BIM and coding with regression analysis. When compared to previous studies,^1,7,11,20,32 this study fills the research gap by investigating the thermal transfer value in the CDS.

Previous studies^11,20 connected the BIM software to a spreadsheet database file to retrieve coefficient values such as TD_eq and ESR values which could not be extracted from the BIM model. If the users do not have the database files or the databases are not in the right format, this linkage may cause the computation to fail. This current study found that using JavaScript coding in the visual programming software could record the TD_eq and ESR databases inside the BIM environmental files. This eliminates the need to link spreadsheet files, which reduces the error and simplifies the computation approach. Furthermore, this study has the advantage of providing a method for calculating OTTV and RTTV values during the CDS. Calculating OTTV and RTTV in this stage can cover designing, optimizing, and changing the building types, materials, sizes, colors, shapes, forms, and opening ratios, whereas previous studies⁷ focused on the calculation in the design stage and could only change material types.

This study has limitations worthy mentioned. Firstly, the BIM prototype in OTTV and RTTV calculations in this study is based on the calculation of Thailand BEC which provided TD_eq and ESR databases that are specific to the location of Thailand. Secondly, even though BIM could automatically calculate the OTTV and RTTV values, users still need to manually update the material thermal properties value in the BIM material library before operating the calculation. Finally, some data, such as coefficient values, could not be directly extracted from the 3D BIM model. Thus, such data must be manually entered into the visual programming software.

Future studies

For future research, we recommend incorporating various thermal simulation software or integrating AI/ML into the calculation model which can improve computational efficiency and enhance the accuracy of building performance predictions. Furthermore, since building energy regulations varied across countries, future research should examine building energy codes from different country, taking into account their specific regulatory frameworks. Finally, to investigate the potential challenges and limitations of the BIM framework, its implementation in real-world projects should be undertaken. This would allow for a more comprehensive evaluation of the framework’s effectiveness and areas for improvement.

Conclusions

This study concludes that integrating BIM into OTTV and RTTV calculations at the CDS could automatically evaluate different building design scenarios. The developed model framework was efficient and accurate, with negligible differences in OTTV and RTTV values when compared to the BEC used for compliance in Thailand. Moreover, integrating BIM and VPL into OTTV and RTTV calculations also provided a flexible method for manipulating the variables related to OTTV and RTTV calculations. The implementation of the BIM framework during the CDS could help the building design professionals make decisions for different building types, materials, sizes, colors, shapes, forms, and fenestration ratios that are appropriate for compliance with the BEC. Building professionals could benefit from the practical implications of this BIM framework, particularly in simplifying the reduction of thermal transfer in buildings. Furthermore, the Thai government would benefit from the BIM framework’s ability to enhance building design efficiency, particularly in terms of energy usage. In the future, public authorities could raise the design standards in BEC to promote overall energy reduction at the national level.

Footnotes

Acknowledgments

Thanasarn Changnawa is supported by the Royal Thai Government scholarship. However, this scholarship was not associated with this current study and the final manuscript.

Author contributions

Thanasarn Changnawa: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – Original Draft, Visualization. Juan Carlos Baltazar: Conceptualization, Methodology, Validation, Formal analysis, Writing – Review and Editing, Supervision.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Thanasarn Changnawa

Data availability statement

Data will be made available on request.

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A BIM-integrated Thailand building energy code framework for building envelope thermal requirements in a conceptual design stage

Abstract

Keywords

Introduction

Literature reviews

Building energy performance in Asia

OTTV and RTTV in Thailand BEC

BIM and visual programming

CDS in building design process

Previous studies

Methodology

Developing a BIM physical model and data input

Developing a BIM calculation framework

Testing and verification

Results

BIM modeling and material thermal properties

BIM-data connection and extraction

BIM OTTV and RTTV calculation framework

U-value

WWR and SRR

TDeq value

ΔT, SHGC, SC and ESR value

OTTV and RTTV

Testing and verification

Base case design

Different design scenarios for commercial buildings

Office buildings and residential buildings

Optimizing and verification

Absorptance

DSH value

BIM framework verification

Discussion

Future studies

Conclusions

Footnotes

Acknowledgments

Author contributions

Declaration of conflicting interests

Funding

ORCID iD

Data availability statement

Appendix

References

TD_eq value