Research on Energy-Saving Optimization of Green Buildings Based on BIM and Ecotect

Abstract

Based on the resource conservation requirements of GB/T 50378-2019 “Green Building Evaluation Standard”, this study constructed a BIM–Ecotect collaborative analysis model and proposed a “four-dimensional integration” green performance optimization method. Taking a high-rise office building in Wuhan as an example, a LOD 300-level Revit building information model was established, and a multidisciplinary collaborative analysis was achieved through gbXML data interaction. The lighting simulation results show that the average natural lighting coefficient of the office area facing south is 2.4 (the standard 85%), while in the meeting room area, due to the optimized design of the curtain wall, the average natural lighting coefficient has increased to 2.6 (the standard 92%). In terms of energy-saving renovation, a three-dimensional collaborative design strategy was adopted. Through the optimization of the envelope structure, the cooling load of the air conditioning system was reduced by 25.3%, and the heat load was reduced by 23.6% (the u value of the exterior wall was reduced by 56.3%, the SHGC of the exterior windows was reduced by 42.9%, and the thermal resistance of the roof was increased by 150%). The ventilation optimization adopts the CFD flow field reverse design, adjusting the window opening rate of the exterior windows from 15% to 20% to form a turbulent diffusion effect. Therefore, the air change rate in the office area reached 2.5 times per hour, and the CO₂ concentration decreased by up to 27.1% at most. The innovative adoption of the “composite sound insulation curtain wall” technology in acoustic environment control has increased the indoor noise compliance rate by 27 percentage points (from 65% to 92%). The above research data indicate that digital collaborative design can achieve an overall energy-saving rate of over 20% for buildings, providing a replicable technical path for enhancing the performance of green buildings.

1. Introduction

The dual crises of global climate change and resource shortage are accelerating the transformation of the construction industry towards a green and low-carbon model. The “Green Building Evaluation Standard” GB/T 50378-2019 of China takes “Four Conservations and One Environmental Protection” as the core and, for the first time, incorporates systematic indicators such as energy conservation, water conservation and material conservation into the evaluation framework of the entire life cycle. This marks a paradigm innovation of green buildings from technology accumulation to performance orientation [1]. However, the current design methods still face the predicament of fragmented analysis in multiple physical fields: key indicators such as lighting, energy consumption, ventilation and noise make it difficult to achieve collaborative optimization, thus resulting in a significant trade-off contradiction between resource conservation and spatial comfort [2]. Studies show that relying solely on empirical decision making may result in a loss of more than 30% of building energy efficiency potential, and digital tools are urgently needed to achieve precise quantification and cross-disciplinary coupled simulation.

Building information modeling (BIM) technology provides a new path to solve this difficult problem. BIM has connected the technical link from geometric information to performance simulation through parametric modeling and data integration [3,4]. For example, BIM models can be seamlessly integrated with platforms such as Ecotect and EnergyPlus, thereby generating multi-dimensional datasets in gbXML format to support dynamic coupling analysis of energy consumption, lighting and ventilation [5,6]. The existing studies have verified its technical advantages: Gourlis et al. [7] confirmed that BIM improves the efficiency of energy consumption simulation by about 60% compared with traditional CAD. When Ryan’s team [8] compared the accuracy of the three types of tools, they found that the error rate of Ecotect in the calculation of daylighting and noise attenuation was less than 8%. Cho et al. [9] demonstrated through cross-validation of EnergyPlus and IES (VE) that BIM-driven multi-objective optimization can increase the energy efficiency of HVAC systems by 22%. Ye et al. [10] analyzed through building information modeling (BIM) and combined with life cycle carbon emissions (LCA) and life cycle cost (LCC), reducing the LCA and LCC of rural prefabricated residences by approximately 30% and 20%, respectively, in terms of carbon reduction and cost optimization. Numan et al. [11] explored the influence of architectural design and urban layout in the bay area on the energy-saving effect of residential buildings; Wong et al. [12] focused on comparing the differences in building energy consumption in Singapore under different temperature conditions. The above-mentioned scholars’ research has all promoted the realization of sustainable development goals in architecture.

However, most of the existing studies at present have focused on a single performance indicator. For instance, Perez et al. [13] took certain buildings in Spanish urban planning as the carriers and achieved the collaborative optimization of lighting and ventilation through the data interaction of BIM–Ecotect but was limited to these two indicators only. Cheng [14] discussed the overall energy consumption by comparing BIM and RTLS technologies with traditional methods to assess energy loss. In terms of environmental performance simulation, Shen Xiaodong’s team completed the thermal performance sensitivity analysis of the envelope structure based on a certain project in Chongqing, while Li analyzed the influence mechanism of wind environment on the ecological microclimate through a certain project in Shanghai [15,16]. These studies all focus on a single performance index and have not established a methodological system for the collaborative optimization of “Light—Heat—Wind—Sound”, thus limiting the completeness of the comprehensive evaluation of green buildings.

In response to the above gaps, this study proposes an integrated analysis framework based on BIM–Ecotect collaborative work to simultaneously analyze the four major indicators of lighting, energy consumption, ventilation and noise: Firstly, a digital twin of the building is constructed in Revit, and geometric attributes, material thermal parameters and equipment system data are extracted; secondly, the energy consumption algorithm is calculated by coupling the EnergyPlus core through the Ecotect platform. Meanwhile, combined with the CFD ventilation model and the acoustic attenuation module, multi-physics field parallel simulation and interactive feedback are achieved. Finally, based on the hierarchical evaluation mechanism of the “Standards”, a quantitative index system covering the natural lighting compliance rate, energy consumption intensity per unit area, air age threshold and noise attenuation (dB) is constructed to provide theoretical guidance for construction.

2. Research Process and Greening Evaluation Indicators

2.1. BIM Model and Ecotect Software

In the domain of building information modeling (BIM), Revit has shown substantial advantages due to its comprehensiveness, information interactivity and broad applicability, encompassing the entire lifecycle management from design through construction to operation and maintenance [17]. For green building analysis, Ecotect has emerged as one of the leading tools because of its precision. Its capabilities include light environment simulation, thermal performance evaluation, acoustic modeling, solar radiation calculation and visibility analysis. It also presents results via visual data network charts, thereby significantly enhancing the efficiency of the analysis [18,19]. This experiment employs a collaborative workflow between Revit 2021 and Ecotect Analysis 2011 on a Windows 10 (64-bit) operating system, leveraging a high-performance computing platform equipped with an Intel Xeon W-2295 processor (18 cores, 36 threads; Intel Corporation, Santa Clara, CA, USA), 128 GB DDR4 memory, and an NVIDIA Quadro RTX 6000 graphics card (24 GB VRAM; NVIDIA Corporation, Santa Clara, CA, USA). Through the gbXML format, it achieves seamless integration of BIM models with energy consumption, daylighting, ventilation and noise analysis, while utilizing measured data related to light, heat, wind and sound for the assessment of green buildings.

The experimental operation process is structured as follows. Initially, a BIM information model was constructed in Revit based on the design drawings. Components such as columns, walls, doors, windows and roofs are sequentially modeled, with their material properties (e.g., heat transfer coefficients, dimensions) accurately defined. Concurrently, rooms are partitioned across each floor and labeled with room numbers to establish foundational data for subsequent green building analysis. Upon completing the modeling phase, the BIM model undergoes optimization by disabling the visibility of elements not pertinent to the analysis (e.g., indoor fixtures, terrain, stairs), thereby reducing file size and enhancing data transfer efficiency. Specific sections of the building are analyzed using the section box feature to isolate and export targeted areas. Subsequently, the optimized BIM model is exported in gbXML format and imported into Ecotect Analysis 2011. Leveraging its advanced capabilities, precise simulations of the light environment, thermal performance, wind behavior and acoustic conditions are conducted. Finally, the building is comprehensively evaluated and analyzed according to the green degree evaluation index system (Figure 1).

Figure 1. Framework of the technical route.

2.2. Green Degree Evaluation Index System and Calculation

The green degree serves as a comprehensive evaluation index for assessing the impact of buildings on the ecological environment and resource consumption throughout their entire life cycle. It is designed to evaluate whether building projects meet national green building standards and sustainable development goals, thereby reflecting the practical effectiveness of implementing green concepts. The green degree evaluation system based on BIM technology adopts GB/T 50378-2019 “Green Building Evaluation Standard” as its core framework and integrates specialized regulations for civil buildings to construct a multi-level evaluation model across four dimensions: light environment, thermal environment, wind environment and acoustic environment. By utilizing the data integration capabilities of BIM technology and the simulation functions of green analysis software, the evaluation parameters are categorized into two primary groups: building environmental quality (Q) and load performance (L). The former emphasizes spatial comfort, health and environmental friendliness, while the latter focuses on energy consumption, carbon emissions and environmental load performance indicators [20]. The detailed evaluation framework is presented in Table 1.

Table 1. Evaluation Index System for the Green Degree of Green Buildings.

Primary Indicators $(\mathbf{Weight} {\mathit{\omega }}_{\mathit{i}})$	Secondary Indicators $(\mathbf{Weight} {\mathit{\omega }}_{\mathit{i}\mathit{j}})$	Classification Number
Light environment (L1, Q1)	The daylight factor complies with the national standard “Standard for Daylighting Design of Buildings”.	Q11
	The daylighting rate complies with the national standard “Standard for Daylighting Design of Buildings”.	Q12
	Sunshine duration	Q13
	The indoor illuminance complies with the national standard “Code for Lighting Design of Buildings”.	L11
	Sun shading measures for exterior windows	Q14
	The surface reflectance of the main functional rooms indoors complies with the national standard.	Q15
Thermal environment (L2, Q2)	Direct solar radiation time	Q21
	Diffuse solar radiation time	Q22
	The heating facilities comply with the national standards.	L21
	The annual heat gain of the indoor space	Q23
	The thermal performance of the interior walls and floor slabs complies with the national energy-saving standards.	L22
	The energy efficiency of the cooling and heating source units of the air conditioning system is superior to the national standards.	L23
Wind environment (Q3)	Dominant wind direction	Q31
	The operable parts of exterior windows and glass curtain walls can provide good ventilation for the building.	Q32
	Building orientation	Q33
Acoustic environment (Q4)	The noise level of the main functional rooms complies with the national standards.
	“Code for Sound Insulation Design of Civil Buildings”	Q41
	The noise level conforms to the national noise zoning standards.	Q42
	The sound insulation performance of interior walls, floor slabs and doors and windows complies with the national standards.
	“Code for Sound Insulation Design of Civil Buildings”	Q43
	Sound insulation and noise reduction measures are adopted.	Q44

2.3. Calculation of Green Degree and Grading Standards

The green degree is calculated through Ecotect Analysis 2011 green software, in combination with the various green building evaluation indicators in Table 1. The results are scored according to the national green building standards. The weights of each quality and load indicator are determined based on the group decision making of experts [21,22,23,24,25]. After obtaining the weights of each secondary indicator and the actual scores, the actual scores of the quality $Q_i^1$ and load $L_j^1$ of each primary indicator are obtained through the weighted average summation method by using Equations (1) and (2).

$$Q_{\mathrm{i}}^1=Q_{\mathrm{ij}}^2{{\displaystyle \sum _{\mathrm{j}}{\left({\omega }_Q\right)}_{\mathrm{ij}}}}^2$$

(1)

$$L_{\mathrm{i}}^1=L_{\mathrm{ij}}^2{{\displaystyle \sum _{\mathrm{j}}{\left({\omega }_L\right)}_{\mathrm{ij}}}}^2$$

(2)

In the formula, ${\omega }_Q$ represents the quality weight corresponding to the first-level indicator; ${\omega }_L$ represents the load weight corresponding to the first-level indicator; the actual scores $Q_i^1$ and $L_j^1$ of the first-level indicators obtained are then substituted into Formulas (3) and (4) to obtain the actual quality and load values of the building.

$$Q=Q_{\mathrm{i}}^2{{\displaystyle \sum _{\mathrm{i}}{\left({\omega }_Q\right)}_{\mathrm{i}}}}^1$$

(3)

$$L=L_{\mathrm{j}}^1{{\displaystyle \sum _{\mathrm{j}}{\left({\omega }_L\right)}_{\mathrm{j}}}}^1$$

(4)

By performing a ratio operation between Equation (3) (actual building mass) and Equation (4) (load), the greenness evaluation value $\mathrm{\alpha }$ of the project can be constructed.

$$\alpha =Q/L$$

(5)

According to the green degree value calculated using Formula (5), in conjunction with the classification thresholds specified in GB/T 50378-2019 “Green Building Evaluation Standard”, the green performance is categorized into five levels: poor (≤0.5), relatively poor (0.5–1.0), average (1.0–1.5), good (1.5–3.0) and excellent (≥3.0). This classification framework aims to quantitatively assess the overall performance of buildings in terms of energy conservation, environmental protection, health and other relevant aspects, thereby providing clear optimization directions for the design, construction and operation phases of green buildings [26,27,28,29]. The detailed scoring criteria are presented in Table 2. The “poor” level signifies significant deficiencies in areas such as energy consumption, resource utilization and environmental friendliness, necessitating systematic improvements. A building rated as “low” indicates that certain indicators have not yet reached the required standards, requiring targeted enhancements. The “average” level suggests that the building generally complies with environmental protection standards but still has potential for further improvement. The “good” level reflects a relatively high level of performance in energy conservation and environmental protection, approaching optimal conditions. Finally, the “excellent” level demonstrates exceptional green performance, fully satisfying the stringent requirements of sustainable development.

Table 2. Greenness Grading Standards.

Rating	$\mathbf{Greenness} \mathbf{Value} \left(\mathit{\alpha }\right)$
Excellent	(≥3.0)
Good	(1.5–3.0)
Average	(1.0–1.5)
Relatively poor	(0.5–1.0)
Poor	(≤0.5)

3. Experimental Verification

3.1. Background

This study takes an eight-story office building in Wuhan Development Zone as the research object. The building is located in Donghu District of Wuhan, close to the urban greenway, with convenient transportation around it (Figure 2). The building adopts the frame-shear wall structure system, with a total construction area of 48,000 square meters (120 m long × 50 m wide, the ceiling height on each floor is 4 m, and 32 m is the total height of the building). Its functional layout includes four main modules: office area (60%), corridor area (20%), meeting room (15%) and computer room (5%). The design capability can support up to 1800 people working together (Figure 3). This project integrates the Green GMV6 inverter multi-split air conditioning system, 600 × 600 LED panel lighting equipment and intelligent fire linkage devices, forming a modern electromechanical equipment system.

Figure 2. Project construction location diagram.

Figure 3. BIM Information Model Diagram.

3.2. Model Establishment

A new project file was created using Revit software 2021, with the project location (geographical coordinates: 30°35′ N, 114°17′ E, Wuhan region) and basic project information (eight-story office building, China Construction Third Engineering Bureau, etc.) pre-configured. Based on the architectural drawings of the office building, elevations and axes were established within the software to ensure consistency with the design drawings. Subsequently, modeling was performed sequentially for columns, walls, doors and windows, floor slabs and roofs. All components’ dimensions, materials and structural information were strictly set according to the drawings. The column section size is 600 mm × 600 mm, walls are constructed using 200 mm thick aerated concrete blocks, door and window dimensions and materials are determined based on functional area requirements, and the floor slab thickness is 150 mm. The roof features a double-layer insulation and waterproof structure. Ultimately, a complete BIM information model was developed, as illustrated in Figure 3, providing precise data support for subsequent green building analysis.

4. Test Results

4.1. Daylighting and Solar Radiation Analysis Using Ecotect

In green building design, the use of BIM-based daylighting and solar radiation simulations allows for a quantitative evaluation of indoor and outdoor light environment performance, thereby providing a robust scientific foundation for energy-efficient design [30,31]. In this study, an eight-story office building (with a floor height of 4 m, total height of 32 m and a floor area of 800 m² per level) was selected as a case example. Following the “Standard for Daylighting Design of Buildings”, a BIM model was developed using Revit software and subsequently imported into Ecotect for analysis. The light climate parameters specific to Wuhan (Class IV region, K = 1.1, outdoor critical illuminance of 4500 lx) were configured, along with key optical properties of the building envelope, including a south-facing window-to-floor ratio of 1:6, glass transmittance of 0.6, exterior wall reflectance of 0.65, roof reflectance of 0.50 and ground reflectance of 0.30. The simulation grid resolution was set at 0.5 m × 0.5 m to ensure high accuracy. By integrating the CIE overcast sky model with typical meteorological year (TMY) data, the annual dynamic daylight distribution across all 8760 h and the solar radiation duration on the winter solstice were successfully simulated. The detailed model parameters are summarized in Table 3.

Table 3. Parameters of the Basic Model.

Parameter Name	Parameter Value	Note
Number of building floors	8th floor	The floor height is 4 m. Total height 32 m.
Functional areas	Office (60%), Corridor (20%), Conference room (15%), Equipment room (5%)	The functional distribution of each floor is consistent. The area of each floor is 800 square meters.
Window-to-floor ratio	1:6 (Southward), 1:8 (Northward)	The VLT of the glass is 0.6.
Reflectivity of the envelope structure	External walls 0.65, roof 0.50, floor 0.30	Reflectivity of the envelope structure
Light climate parameters	K = 1.1, outdoor critical illuminance 4500 lx	Wuhan belongs to the IVth light climate zone.
Simulation grid accuracy	0.5 m × 0.5 m	Ecotect grid division
Sky model	CIE overcast sky model	Dynamic correction based on TMY meteorological data

The daylight factor is obtained by Equation (6):

$$C=E_S/E_T$$

(6)

In the formula, C represents the daylight factor, E_S is the plane illuminance at a certain point inside the room generated by natural light (Unit: lx), and E_T indicates the total illuminance of natural light on the unobstructed horizontal surface outdoors at the same time.

Based on the BIM model constructed above, Ecotect Analysis software was imported to conduct quantitative analysis for the lighting performance of each functional area of the office building (Figure 4), and the following key data were obtained as shown in Table 4.

Figure 4. Analysis of indoor lighting.

Table 4. Results of Daylighting Analysis.

Functional Area	Lighting Coefficient (C)	Achievement Rate (C ≥ 2.0)	Illumination Uniformity (Emin/Emax)
Office (South)	2.4	85%	0.68 → 0.72
Office (North)	2.1	78%	0.55 → 0.65
Corridor	1.2	40%	0.30 → 0.35
Conference Room (South)	2.6	92%	0.70 → 0.75

(Note: E_min and E_max are the minimum and maximum illuminance values within the area, respectively).

This study employed a collaborative simulation approach using BIM and Ecotect to quantitatively evaluate the daylighting performance of various functional areas in an eight-story office building. The findings indicate that south-facing areas exhibit optimal daylighting performance, attributed to abundant natural light and well-designed shading strategies. Specifically, the daylight factor for south-facing offices is 2.4 (compliance rate 85%), while that for south-facing meeting rooms reaches 2.6 (compliance rate 92%). Both values meet the three-star requirements of the “Green Building Evaluation Standard” (≥80%). In contrast, north-facing offices, influenced by orientation and external shading, have a daylight factor of 2.1 (compliance rate 78%) and may benefit from supplementary lighting via reflectors or light pipes. The corridor area, lacking direct exterior windows, demonstrates a low daylight factor of 1.2 (compliance rate 40%) and requires optimization through artificial lighting integration or light wells. Regarding illuminance uniformity, south-facing meeting rooms achieved an improvement from 0.70 to 0.75 by optimizing the window-to-floor ratio (1:6) and utilizing diffuse reflection materials, effectively mitigating glare. Similarly, north-facing offices enhanced uniformity from 0.55 to 0.65 through the application of light-colored walls (reflectance ≥ 0.7) and high-transparency glass (VLT = 0.65).

Figure 5 illustrates the distribution of sunshine hours for south- and north-facing rooms across different floors of an eight-story office building on the winter solstice. Owing to their advantageous orientation and the trajectory of the sun, the sunshine hours for south-facing rooms increase markedly with floor elevation: from 2.5 h on the first to second floors, rising to 3.8 h on the third to sixth floors and reaching 4.2 h on the seventh to eighth floors. These values meet the basic daylighting requirements outlined in the “Green Building Evaluation Standard” (GB/T 50378-2019) (reference value ≥ 2 h). Conversely, north-facing rooms, constrained by their orientation and surrounding obstructions, exhibit significantly lower sunshine hours: only 1.2 h on the first to second floors, increasing to 2.0 h on the seventh to eighth floors, yet remaining below 50% of those for south-facing rooms. In the upper floors (seventh to eighth), reduced obstructions and optimized solar altitude angles narrow the daylighting disparity between south- and north-facing areas. However, the lower floors (first to second) require supplementary lighting via reflectors or light pipes to compensate for insufficient natural illumination. The data demonstrate that building height and orientation are critical factors influencing daylighting performance. Therefore, in design considerations, high daylighting demand areas should be prioritized for placement on south-facing middle and upper floors, while material and construction optimizations should enhance the light environment in lower areas to achieve a balance between energy efficiency and occupant comfort.

Figure 5. Sunlight Analysis Diagram.

This study adopted a controlled experiment method to verify the reliability of the Ecotect analysis software in simulating the sunlight duration of buildings. Through the TES-1332A high-precision digital illuminance meter (with a range of 1–20,000 lux and a systematic error of ±3%), Data collection was carried out under typical clear sky conditions (cloud cover index ≤ 0.1) in accordance with the ISO/CIE 19776:2014 standard [32]. After the instrument was calibrated by the provincial metrology testing center, in the area where the geographical coordinates of the measurement point (30°35′ N, 114°17′ E) are located, six groups of horizontal plane solar intensity samples were continuously obtained at intervals of 2 h within the time range from 08:00 to 18:00. After arithmetic averaging processing, the benchmark data set was formed, and the TMY2 meteorological data parameters shown in Table 5 were simultaneously referred to. In the numerical simulation stage, a three-dimensional parametric model was constructed based on the Ecotect Analysis 2011 platform. The optical parameters of the building envelope structure were configured strictly in accordance with the “Building Daylighting Design Standard” (GB 50033-2021) [33], and the sunlight simulation data were generated through the spatial discretization algorithm. Finally, the UTC time axis calibration mechanism was adopted to compensate for the time-domain deviation and eliminate the outliers in the spatial dimension. The measured data and the simulation results were compared and analyzed in time (for details, see Table 6).

Table 5. Meteorological Data Table of Wuhan on the Winter Solstice.

Time	Dry Bulb Temperature (°C)	Relative Humidity (%)	The Amplitude of Direct Sunlight (W/m2)	Solar Scattering Amplitude (W/m2)	Wind Speed (m/s)
8:00	5	70	200	100	4.0
10:00	8	60	450	180	5.5
12:00	10	50	550	220	6.5
14:00	9	48	400	175	5.3
16:00	6	65	150	80	4.0
18:00	2	80	0	0	2.6

Table 6. Analysis Table of Sunlight Error.

Parameter	Analog Value	Measured Value	Error Rate
The daylight factor of the south-facing office	2.4	2.3	4.3%
Sunshine duration on the Winter Solstice (1st floor)	2.5 h	2.4 h	4.0%
Uniformity of illuminance (conference room)	0.75	0.72	4.0%

The data in Table 6 indicates that the sunshine duration calculated by Ecotect Analysis software is close to that measured by the TES-1332A high-precision digital illuminance meter, with the maximum error rate not exceeding 4.3%. This result further validates the reliability of the numerical values.

4.2. Heat and Energy Consumption Analysis

Based on the thermodynamic simulation advantages of Ecotect software, the study adopted a spatial zoning strategy to divide the building model into eight thermal sub-regions and successively defined the indoor environmental control parameters of each zone as the assessment benchmarks for thermal comfort and light environment [34,35,36]. By setting the heat transfer coefficients of the envelope structure in Experiment A (the U value of the exterior wall = 0.8 W/(m²·K), the U value of the exterior window = 3.0 W/(m²·K) and the U value of the roof = 1.2 W/(m²·K)) and the COP value of the variable frequency multi-split central air conditioning system (3.2 for cooling/2.8 for heating), it was compared with the heat transfer coefficients of the envelope structure set with different materials in Experiment B, U value of exterior wall = 0.35 W/(m²·K), U value of exterior window = 1.8 W/(m²·K) and U value of roof = 0.24 W/(m²·K). The COP value of the central air conditioning system is 3.2 for cooling and 2.8 for heating. This study explores the influence of different thermal parameters of the envelope structure on the annual energy consumption of the building. The comparison results of the experiments are detailed in Table 7.

Table 7. Parameter Settings of Energy Consumption Analysis Model.

Parameter Name	Parameter Value (A Test)	Parameter Value (B Test)	Note
Building floor count	8th floor	8th floor	Floor height: 4 m Total construction area: 12,800 m2
Thermal performance of building envelope	External wall U-value = 0.8 W/(m2·K) Exterior windows U-value = 3.0 W/(m2·K) Roof U-value = 1.2 W/(m2·K)	External wall U-value = 0.35 W/(m2·K) Exterior windows U-value = 1.8 W/(m2·K) Roof U-value = 0.4 W/(m2·K)	The exterior wall of Test A is made of concrete and cement mortar, while Test B features a 100 mm rock wool insulation layer. The SHGC for single-pane glass is 0.7 in Test A and 0.3 in Test B. Test A lacks roof insulation, while Test B features a 50 mm extruded polystyrene board. (In Test B, the glass adopted the low-emissivity (Low-E) coating technology).
Air conditioning system	Variable frequency multi-split air conditioning system (COP = 3.2/2.8)	Variable frequency multi-split air conditioning system (COP = 3.2/2.8)	Design temperature: 26 °C in summer and 20 °C in winter.
Simulation grid accuracy	Zone-by-zone hourly load analysis	Zone-by-zone hourly load analysis	Ecotect + EnergyPlus

By using Ecotect software, the thermal performance parameters of different envelope structures in the experiment were replaced, and the air conditioning energy consumption results of test A and B and the thermal analysis results of passive components were obtained as follows:

It can be seen from the data in Table 8 and Figure 6 and Figure 7 that optimizing the thermal performance of the building envelope can significantly improve the energy efficiency. Specifically, in a three-dimensional coordinated renovation scheme, this includes the addition of 100 mm rockwool insulation to the exterior wall (reducing the u value from 0.8 to 0.35 W/(m²·K)), upgrading the exterior window to Low-E double glazing (optimizing the u value to 1.8 W/(m²·K) and controlling the SHGC at 0.3), and installing 50 mm extruded polystyrene panels on the roof (increasing the u value to 0.4 W/(m²·K)). The energy-saving efficiency of the heating and cooling load on the air conditioning system is 25.3% and 23.6%, respectively. In addition, when combined with the 20.3% reduction in lighting energy consumption resulting from natural daylighting optimization, the total energy consumption ended up being reduced by 20.1%. This empirical study shows that the limitations of traditional energy saving techniques can be overcome through a comprehensive strategy of controlling the heat transfer coefficient of the envelope structure (U-value optimization rate of 39.7% to 40.6%) and managing solar heat gain (SHGC reduction to 60% of the baseline value). This method provides a quantifiable technical approach to improve the energy efficiency of green buildings, especially the low-carbon transformation of public buildings in hot summer and cold winter areas.

Table 8. Energy consumption simulation results table.

Energy Consumption Type	Annual Energy Consumption (A Test)	Annual Energy Consumption (B Test)	Energy- Saving Rate	Standard Limit Value
Air conditioning cooling load (kWh/m2)	39.2	29.3	25.3%	≤75 (Three-star)
Air conditioning heat load (kWh/m2)	64.58	49.32	23.6%	≤40 (Three-star)
Energy consumption for lighting (kWh/m2)	18.2	14.5	20.3%	≤15 (Three-star)
Total energy consumption (kWh/m2)	121.98	93.12	23.1%	≤130 (Three-star)

Figure 6. Changing trend in air conditioning energy consumption results.

Figure 7. Thermal analysis of passive components.

4.3. Noise Analysis

This study established a three-dimensional noise field model with 1 m × 1 m precision using the CadnaA acoustic modeling software(CadnaA 4.6), taking an eight-story public building (Total height of 32 m) as the research subject. A comparative acoustic experiment was conducted to analyze the combined sound environment consisting of 70 dB(A) traffic noise at 15 m from the main road and 55 dB(A) equipment noise from the outdoor air conditioning unit. Under the condition that the sound insulation performance of the walls, the NRC value of the gypsum board ceiling (NRC = 0.4) and other boundary conditions remained unchanged, two sound insulation schemes were designed for comparative analysis. In the C test, a single-layer glass window with Rw = 30 dB was used as the baseline condition; in the D test, the south-facing window system was upgraded to a double-layer argon-filled insulating glass window with Rw = 40 dB, and an additional sound insulation barrier for the outdoor air conditioning unit (noise reduction ≥ 10 dB) was installed to form the optimized condition (Table 9).

Table 9. Parameter settings of the noise analysis model.

Parameter Name	Parameter Value	Note
Building floors count	8th floor	Floor height: 4 m
The main noise sources	Traffic noise (daytime equivalent sound level Leq = 70 dB(A)), equipment noise (air conditioning outdoor unit Leq = 55 dB(A))	The main road is 15 m away from the building facade.
Sound insulation measures(C test)	Single-pane glass window (weighted sound insulation Rw = 30 dB)	The sound insulation of the wall Rw = 45 dB.
Sound insulation measures(D test)	Double-layer hollow soundproof window (Rw = 40 dB)	The south-facing windows will be replaced with double-pane soundproof windows (Rw = 40 dB, filled with argon gas); sound insulation barriers will be added to the outdoor air conditioner units (Noise reduction ≥ 10 dB).
Sound-absorbing materials	Ordinary gypsum board ceiling (NRC = 0.4)	____
Simulation grid accuracy	1 m × 1 m	Cadna A acoustic model

By utilizing the Ecotect software and performing replacement experiments with various sound insulation measures, the following monitoring results were obtained for the daytime period (from 7:00 to 22:00):

By analyzing the data in Table 9 and Table 10, it is evident that the D test, which utilized double-glazed insulating soundproof windows and installed sound barriers around the outdoor units of air conditioners, significantly outperformed the C test, which used single-layer glass. These measures effectively decreased the indoor noise levels. Specifically, the overall noise compliance rate in the C test was 65%, whereas after implementing these improvements, the compliance rate rose to 92%, demonstrating a marked enhancement.

Table 10. Results of daytime noise analysis.

Functional Area	Noise Level in the C Test Room Leq [dB(A)]	Noise Level in the D Test Room Leq [dB(A)]	Standard Limit Value
South-facing office (fronting the street)	48	38	≤40 (GB 55038-2025) [37]
North-facing office (back street)	42	35	≤40 (GB 55038-2025)
conference room	40	32	≤35 (GB 55038-2025)
Corridor	45	37	≤45 (GB 55038-2025)
Overall compliance rate	65%	92%

4.4. Ventilation Analysis

This study employs the coupling analysis method of Ecotect environmental simulation and ANSYS Fluent CFD (2024 R2) to conduct an opening optimization comparison test for the “natural ventilation + mechanical assistance” composite ventilation mode in an eight-story public building during the transitional season. Under the condition of maintaining constant operating parameters of the all-air system (Air change rate of 2 times/hour, wind speed ≤ 0.3 m/s), two opening configuration scenarios are established. In the E test, a benchmark scheme with a 12% opening ratio on the south side is adopted (window height of 1.5 m without wind-guiding components); in the F test, an optimized scheme is developed by increasing the facade opening ratio to 20% (including 8% skylight area) and incorporating adjustable wind-guiding louvers, as well as an openable skylight in the atrium (Table 11).

Table 11. Parameter settings for ventilation analysis model.

Parameter Name	Parameter Value	Note
Ventilation mode	Natural ventilation + mechanical assistance	Natural ventilation is mainly used during the transitional seasons.
Open mouth design (E test)	South-facing single-sided window opening (Area ratio: 12%)	The window is 1.5 m high and has no wind deflector.
Open mouth design (F test)	The opening rate has been increased to 20%. The skylight area accounts for 8%.	Add adjustable louvered vents to the south-facing side, and install operable skylights on the top of the atrium.
Air conditioning system	All-air system (air change rate: 2 times per hour)	Design wind speed ≤ 0.3 m/s
Simulation tool	Ecotect + ANSYS Fluent	CFD air flow organization analysis

By employing the Ecotect software and performing parametric substitution experiments with varying opening configurations, the following indoor ventilation results were obtained:

Based on the data analysis in Table 11 and Table 12, it is evident that compared to the E test’s benchmark scheme featuring a south-facing single-side opening ratio of 12% (window height of 1.5 m without wind-guiding components), the F test adopts an optimized design by increasing the facade opening ratio to 20% (including 8% skylight area) and incorporating adjustable wind-guiding louvers, as well as an openable atrium skylight. This approach significantly enhances indoor ventilation performance. Specifically, after optimization: the CO₂ concentration in the south-facing offices decreases from 720 ppm to 550 ppm, representing a 23.61% increase in ventilation efficiency; the CO₂ concentration in the central meeting room drops from 850 ppm to 620 ppm, corresponding to a 27.06% improvement in ventilation efficiency; and the CO₂ concentration in the corridor falls from 680 ppm to 570 ppm, indicating a 16.17% enhancement in ventilation efficiency. These findings demonstrate that increasing the opening ratio and integrating skylights effectively reduces CO₂ concentrations, providing valuable insights for green building design.

Table 12. Ventilation analysis results.

Functional Area	Air Change Rate (Times per Hour) E Test	Air Change Rate (Times per Hour) F Test	The Concentration of CO2 (ppm)	Standard Limit Value
South-facing office	1.2	1.2	720 → 550	≤800 (GB/T 18883-2022) [38]
Middle conference room	0.8	0.8	850 → 620	≤800 (GB/T 18883-2022)
Corridor	1.5	1.5	680 → 570	____

4.5. Green Degree Evaluation

Based on the “Green Building Evaluation Standard” GB/T 50378-2019 [39] and the BIM simulation analysis results of the project, this study developed an evaluation system comprising 4 first-level indicators and 19 s-level indicators through expert consensus-based decision making. Additionally, the importance and scoring criteria for each indicator were quantified (see Table 13) to provide a data-driven approach for assessing the green performance of the building. The core methodologies and data sources are outlined as follows:

Table 13. Weights and scores of the green building index system.

Primary Indicators	Secondary Indicators	Indicator Type	Indicator Assignment Methods	Weighting Factor	Score
Light environment (L1, Q1)	Q11	Scoring items	Daylighting and Sunlight Exposure	0.1	100
	Q12	Scoring items	Daylighting and Sunlight Exposure	0.2	100
	Q13	Scoring items	Daylighting and Sunlight Exposure	0.2	100
	L11	Control item	Illumination	0.3	80
	Q14	Scoring items	Spatial visualization	0.1	40
	Q15	Control item	Set the wall material in the BIM model.	0.1	100
Thermal environment (L2, Q2)	Q21	Scoring items	Solar radiation	0.2	100
	Q22	Scoring items	Solar radiation	0.2	93
	L21	Scoring items	Indoor temperature	0.2	100
	Q23	Scoring items	Heat environment loss	0.1	83
	L22	Control item	Set the wall material in the BIM model.	0.2	81
	L23	Control item	Indoor air conditioning distribution	0.1	100
Wind environment (Q3)	Q31	Scoring items	Airflow simulation	0.3	57
	Q32	Control item	window-to-wall ratio	0.4	80
	Q33	Scoring items	Weather tool software simulation	0.3	95
Acoustic environment (Q4)	Q41	Scoring items	Time simulation	0.3	100
	Q42	Scoring items	Time simulation	0.3	100
	Q43	Control item	Set the wall material in the BIM model.	0.2	25
	Q44	Scoring items	Material usage, site greening	0.2	80

In accordance with relevant national standards, such as GB50189-2015 “Energy Efficiency Design Standard for Public Buildings” and others [40], the benchmark values of each secondary indicator are clearly defined. The actual values of each secondary indicator are obtained through the assessment results of BIM software and green building analysis software. The calculation is carried out according to the formula: Score = 100 − (Benchmark Value − Actual Value)/Benchmark Value × 100. Finally, the data of the primary indicators in Table 14 are converted through the above method.

Table 14. Weights and scores of first-level indicators for green buildings.

Primary Indicator	Secondary Indicator	Weight Coefficient	Score
Lighting analysis	$L_1^1$	0.15	24
	$Q_1^1$	0.20	64
Energy consumption analysis	$L_2^1$	0.15	46
	$Q_2^1$	0.20	47
Ventilation analysis	$Q_3^1$	0.20	78
Noise analysis	$Q_4^1$	0.10	81

The hierarchical evaluation system results indicate that after adjusting the core indicators of building quality and environmental load with weight coefficients, their actual scores are derived through normalization using Formulas (3) and (4). Subsequently, Formula (5) is applied to calculate the comprehensive green performance index, yielding a score of 4.76. In accordance with the green degree classification standard outlined in Table 2 and corroborated by relevant scholarly research [41,42,43], this building achieves an excellent green rating. This demonstrates that the building satisfies the three-star certification requirements for green buildings across key dimensions such as resource conservation and environmental friendliness.

5. Conclusions

This study systematically verified the significant effect of data-driven strategies on the performance optimization of green buildings by integrating BIM and digital performance simulation tools and revealed three conclusions:

(1) The collaborative design of architectural form and envelope structure can break through the traditional energy efficiency bottleneck. By adopting external wall insulation (U = 0.35 W/(m²·K)), Low-E glass (SHGC = 0.3) and roof insulation technology, combined with the south-facing lighting layout (C ≥ 2.0 area accounting for 83%), a reduction of over 20% in air conditioning load and a decrease of 23.1% in total energy consumption is achieved.

(2) The refined control of the window opening rate (20%) and the proportion of skylights (8%) significantly improved the natural ventilation efficiency (air change rate ≥ 2 times/h), reduced the CO₂ concentration in the south office area by 23.6–27.1% and resolved the contradiction between thermal comfort and air quality.

(3) The passive noise reduction system composed of double soundproof windows and sound barriers, in coordination with the mechanical system regulation, increased the noise qualification rate by 27 percentage points to 92%, confirming the crucial role of multi-objective coordinated control in indoor environmental health.

The research has established three major technical paths. The first one is that the interactive performance simulation based on BIM can accurately quantify the coupling effect of light, heat and wind. Secondly, the thermal parameters of the envelope structure need to be optimized in a directional manner in combination with the spatial functions (such as in the south-facing lighting sensitive area). Thirdly, the integration of ventilation and noise reduction technologies is a necessary means to achieve the standards of healthy buildings. This achievement provides clear technical guidance for the development of green buildings; by using digital tools to precisely control performance parameters, relying on spatial function-oriented envelope structure optimization strategies, and integrating multi-system collaborative control technologies, it can simultaneously achieve energy consumption control targets of over 23% energy savings and healthy building environment indicators. The research has confirmed that data-driven collaborative design methods can substantially promote the carbon neutrality process in the construction field. It is suggested that BIM performance simulation be incorporated into the green building standard system and the in-depth application of machine learning algorithms in parametric design and dynamic performance optimization be promoted.