Daylighting Performance Simulation and Optimization Design of a “Campus Living Room” Based on BIM Technology—A Case Study in a Region with Hot Summers and Cold Winters

Abstract

In the context of green building development, the lighting design of campus living rooms in hot summer and cold winter areas faces the dual challenges of glare control in summer and insufficient daylight in winter. Based on BIM technology, this study uses Revit 2016 modeling and the HYBPA 2024 performance analysis platform to simulate and optimize the daylighting performance of the campus activity center of Hunan City College in multiple rounds of iterations. It is found that the traditional single large-area external window design leads to uneven lighting in 70% of the area, and the average value of the lighting coefficient is only 2.1%, which is lower than the national standard requirement of 3.3%. Through the introduction of the hybrid system of “side lighting + top light guide”, combined with adjustable inner louver shading, the optimized average value of the lighting coefficient is increased to 4.8%, the uniformity of indoor illuminance is increased from 0.35 to 0.68, the proportion of annual standard sunshine hours (≥300 lx) reaches 68.7%, and the energy consumption of the artificial lighting is reduced by 27.3%. Dynamic simulation shows that the uncomfortable glare index at noon on the summer solstice is reduced from 30.2 to 22.7, which meets the visual comfort requirements. The study confirms that the BIM-driven “static-dynamic” simulation coupling method can effectively address climate adaptability issues. However, it has limitations such as insufficient integration with international healthy building standards, insufficient accuracy of meteorological data, and simplification of indoor dynamic shading factors. Future research can focus on improving meteorological data accuracy, incorporating indoor dynamic factors, and exploring intelligent daylighting systems to deepen and expand the method, promote the integration of cross-standard evaluation systems, and provide a technical pathway for healthy lighting environment design in summer-hot and winter-cold regions.

1. Introduction

The hot-summer and cold-winter region is a unique climate zone. In summer, the region is often controlled by subtropical high pressure, with high temperatures and high humidity leading to heat accumulation inside buildings; in winter, there is a lack of stable heating and high air humidity. Such complex climatic conditions put forward higher requirements for thermal comfort, energy consumption control, and the indoor environmental quality of buildings, prompting building design to take into account multiple factors such as thermal insulation, heat preservation, ventilation, and lighting.

There is a large research base on natural daylighting and thermal comfort in educational buildings in similar climate zones in different countries. For example, a combined system of shading louvers and light guides was used to significantly improve indoor illuminance uniformity in the southern region of the United States [1]; and Italian studies have shown that an integrated adjustable shading system can help to reduce the cooling load by up to 30% in a Mediterranean climate with hot summers and cold winters [2]. Chinese scholars Ma, Z et al. also achieved improved winter daylight compliance for school buildings in areas such as Hangzhou through thermal-optical multi-objective optimization [3]. In addition, an empirical study in the Brisbane region of Australia showed good predictability of natural light design using BIM and meteorological data fusion modeling [4]. These studies provide valuable experience, but the synergistic optimization of dynamic shading systems and static daylighting performance in complex spaces such as campus living rooms still needs to be deepened.

As an important part of the campus public space, the campus living room plays a key role in promoting faculty and student communication, academic seminars, and cultural activities. High-quality natural lighting not only reduces the energy consumption of artificial lighting, saving 20–30% of electricity per square meter [5], but also improves the indoor light environment and enhances the sense of transparency and openness of the space. Relevant studies have shown that in a well-lit indoor space, people’s work efficiency can be increased by about 25% [6], and learning efficiency can be increased by 15–20% [7]. However, in hot summer and cold winter areas, traditional design methods have difficulties in accurately balancing the contradiction between daylighting and thermal insulation and heat preservation, resulting in insufficient daylighting performance or excessive energy consumption.

Few existing studies have systematically explored the coupling effect of daylighting and thermal regulation strategies in campus buildings in hot summer and cold winter regions, and most of them focus on the analysis of static lighting coefficients, and lack of research on the dynamic regulation of adjustable shading systems. At the same time, there is a lack of research on the optimization of daylighting for the specific space of the campus living room, especially the lack of systematic optimization strategies adapted to the climatic characteristics of the region. Therefore, the simulation and optimization of the lighting performance of the “campus living room” based on BIM technology is not only a technical demand to adapt to the local climatic conditions and improve the energy efficiency of the building but is also an inevitable choice to meet the high-quality development of modern educational spaces.

The national “Tenth Five-Year Plan” clearly proposes to improve the level of industrialization, digitalization, and intelligence of buildings, and realize green and low-carbon development [8]. In this context, the design of the campus living room is changing from focusing on the physical environment to the all-around comfortable experience of mental health and social health. This study aims to systematically evaluate the lighting and thermal comfort performance of campus living rooms in hot summer and cold winter regions under different design interventions through BIM technology, explore optimization strategies adapted to this region, and provide systematic research ideas for green campus construction.

2. Literature Review

2.1. Current Status of BIM Technology Application in Daylighting Simulation

This study focuses on the optimization of daylighting in campus living rooms in regions with hot summers and cold winters. BIM technology serves as the core tool, and its integration with daylighting simulation is a crucial foundation. Early studies primarily focused on the compatibility between BIM and daylighting simulation softwares. Sacks, R. et al. (2011) utilized the interface between Revit and Radiance to calculate daylighting coefficients for three-dimensional models, verifying the feasibility of BIM data-driven simulation [9], laying the technical foundation for subsequent research. From the perspective of research method classification, Zhang, Jianjian et al. proposed a visualization programming framework based on Grasshopper, coupling parameters such as window-to-wall ratio and glass transmittance with genetic algorithms, which belongs to the category of parametric optimization methods, achieving multi-objective optimization of daylighting performance [10].

In the field of dynamic daylight simulation, the Daysim software developed by Dr. Christoph Reinhart and his team [11] integrates meteorological data and building models to dynamically assess the impact of shading systems on daylight uniformity. From a temporal perspective, this provides technical support for seasonal shading designs in regions with hot summers and cold winters [11]. Meanwhile, Dong, B et al. integrated BIM models with the building energy simulation software EnergyPlus [12], representing a multi-software collaborative research method, revealing the quantitative relationship between natural daylighting and artificial lighting energy consumption, and providing a quantitative basis for energy-saving designs. These studies demonstrate that the integration of BIM technology with daylighting simulation tools significantly enhances the accuracy and efficiency of building lighting environment design, closely aligning with the technical approach adopted in this study for dynamic daylighting simulation using BIM.

2.2. Key Issues in Lighting Design for Hot-Summer and Cold-Winter Areas

Based on the scope of this study, which focuses on campus living rooms in summer-hot and winter-cold regions, the climate characteristics of this region impose special requirements on daylighting designs, which are the core considerations. From a climatic data perspective, the region has an annual average sunshine duration of 1475.88 h, with an average of 6.4 h per day in summer and approximately 2.5 h in winter [13]. The seasonal variations in solar altitude angle and radiation intensity are significant, directly influencing the daylighting strategies for campus living rooms.

From the perspective of building types, lighting issues are particularly prominent in campus buildings. Sun Jing’s research on teaching buildings indicates that the daylight factor in teaching spaces generally falls below 2.0 (ideal value ≥ 3.3), primarily due to an imbalance between the height of the atrium and the window-to-wall ratio [14]. This conclusion provides valuable insights into the window design of public spaces such as campus living rooms. From a methodological perspective, Wu Ruilong et al. found through simulation that building layouts significantly affect group daylighting, and a rational layout can improve natural daylighting levels [15], providing insights for optimizing the overall layout of campus lounges.

Traditional fixed shading designs in this region often result in insufficient daylighting in winter and excessive daylighting in summer. Initial simulations indicate that the Discomfort Glare Index (DGI) for south-facing windows can reach 28–30 at noon in summer, exceeding the maximum comfort threshold (DGI ≤ 28). This suggests that the design of dynamic shading systems is critical for optimizing daylighting in campus lounges in this region, aligning closely with the optimization direction of this study.

2.3. Inadequacy of Existing Research and Breakthrough Points of This Topic

Through a systematic review of the aforementioned literature and in conjunction with the research theme of this study, the existing research has the following shortcomings: (1) In terms of research content, there is a greater focus on static daylighting coefficient analysis with limited dynamic simulation analysis, particularly lacking research on dynamic design involving changes in the angle of adjustable shading panels. Campus living rooms, however, have flexible usage times and thus require a higher dynamic daylighting performance. (2) From the perspective of research methods, optimization design primarily employs single-variable analysis, failing to fully leverage BIM’s multi-variable characteristics for comprehensive integrated optimization, making it difficult to address the complex spatial forms of campus living rooms. (3) From the perspective of research objects, studies on daylighting optimization for campus living rooms are scarce, and there is a lack of adaptive optimization strategies tailored to the climatic characteristics of regions with hot summers and cold winters.

Based on the above shortcomings, the breakthrough of this study lies in the following: combining special meteorological data from regions with hot summers and cold winters to construct a BIM-based dynamic daylighting simulation framework; introducing multi-objective optimization algorithms to establish a daylighting performance evaluation system suitable for campus public buildings; exploring innovative optimization strategies such as irregular window openings and integrated shading systems to achieve a synergistic optimization of daylighting and thermal comfort; and providing, through case studies, reference design solutions and technical pathways for daylighting design in campus living rooms in regions with similar climates.

3. Research Methodology

3.1. Research Framework

This study adopts the Performance-Driven Design (PDD) methodology to construct a closed-loop research system of “modeling-simulation-assessment-optimization”. Taking the campus living room in hot-summer and cold-winter areas as the research object, the study integrates geometric modeling and physical simulation based on BIM technology and improves daylighting performance through multiple rounds of iterative optimization. The research framework follows the Building Lighting Design Standard GB 50033-2013 [16] (hereafter referred to as the Building Lighting Design Standard) and the Green Building Evaluation Standard GB/T 50378-2019 (2024 Edition) [17] (hereafter referred to as the Green Building Evaluation Standard), and combines the dynamic meteorological data with visual comfort indicators to form a lighting optimization strategy applicable to the climate zone. Climate zone’s lighting optimization strategy.

3.2. Modeling Tools and Parameter Settings

3.2.1. BIM Modeling Platforms

Construct a 3D building information model using Autodesk Revit 2016. Integrate the geometric parameters (e.g., wall thickness, door and window sizes), physical properties (e.g., material thermal parameters), and functional properties (e.g., room usage type) of the building components. The accuracy of the model reaches LOD300, including major components such as walls, floor slabs, doors, windows, roofs, etc., and divides the space (activity room, reading room, etc.) according to function to ensure the accuracy of the subsequent simulation. After the modeling is completed, it is exported in gbXML format and checked for geometric integrity (adjacency errors, hollow body exclusion) to ensure the quality of the model.

3.2.2. Meteorological Data and Geographical Location

The project location is located in Heshan District, Yiyang City, Hunan Province (28.68° N, 112.43° E). The typical meteorological year (TMY) data from the HYBPA 2024 platform is used, including hourly solar radiation, temperature, humidity, and other parameters. This dataset is compiled and calibrated based on actual meteorological data collected from 270 ground meteorological stations across China by the Meteorological Data Room of the China Meteorological Information Center from 1971 to 2003, meeting the simulation requirements for the climate characteristics of regions with hot summers and cold winters. However, it may not include extreme weather events caused by global climate change over the past two decades (such as prolonged high temperatures in summer and abnormally low temperatures in winter), which could lead to an underestimation of summer glare risks in the simulation results. Future improvements could involve integrating recent meteorological data to enhance simulation accuracy and enhance the design scheme’s adaptability to long-term climate change.

3.2.3. Material and Construction Parameters

• The materials used in the enclosure structure are shown in

  Table 1. Material construction parameters of enclosure structure.

  Material Name	Thermal Conductivity W/(m·K)	Reflection Ratio
  240 mm shale brick	0.58	0.32
  120 mm reinforced concrete slab + insulation layer	0.75	0.84

  .

2.

Door and window system: uniformly adopt 6Low-E + 12A + 6C double silver Low-E glass (visible light transmission ratio 0.76, shading coefficient 0.42); window frames are broken-bridge aluminum alloy (structural light-blocking discount coefficient 0.80), and the door light-transmitting area ratio is 0.3.

3.

Interior finishes: beige blending paint for the walls (reflection ratio of 0.70) and neutral stone for the floor (reflection ratio of 0.30) to enhance the effect of secondary lighting through the optimization of the reflection ratio.

3.3. Lighting Simulation Method and Index System

3.3.1. Simulation Platforms and Engines

HYBPA 2024, the building performance analysis platform of Quanta, was selected for daylighting simulation and analysis. The software uses Radiance as the simulation engine to simulate the indoor natural lighting, natural lighting + artificial lighting, and other working conditions in the campus living room. The software generates a dynamic lighting analysis report based on the “Building Lighting Design Standards” and “Green Building Evaluation Standards”, which provide technical bases for the evaluation and optimization of the design scheme.

3.3.2. Simulation Types and Parameters

• Static simulation: set CIE full cloudy sky conditions (outdoor illuminance 13,500 lx); grid accuracy is set to “actuarial” (0.5 m × 0.5 m); calculate the average value of the lighting coefficient and uniformity.
• Dynamic simulation: based on the annual meteorological data, calculate the annual average illuminance and the proportion of hours meeting the standard (the number of hours ≥300 lx/the total number of hours per year), and verify the requirement of “the average number of hours of lighting illuminance ≥ 300 lx ≥ 4 h/d” in the “Green Building Evaluation Standard”.
• Glare analysis: midday (12:00) on the summer solstice was selected as a typical moment to calculate the uncomfortable glare index (DGI) of the critical area and assess the visual comfort (threshold DGI ≤ 28) [18].

3.3.3. Assessment of the Indicator System

The system of assessment indicators is shown in

Table 2. System of assessment indicators.

Indicator Category	Core Indicators	Standard Limit	Data Sources
Daylight level	Lighting factor (DF)	Class III rooms ≥ 3.3%	Static simulation
Uniformity	Lighting uniformity	Type III rooms ≥ 0.6	Static simulation
Dynamic applicability	Proportion of annual hours of compliance	≥60%	Dynamic simulation
Visual comfort	Discomfort Glare Index (DGI)	≤25–28	Critical Moment Simulation
Energy efficiency	Reduction rate of energy consumption for artificial lighting	≥20%	Energy consumption linkage analysis

below.

3.4. Optimization Strategies and Iterative Methods

3.4.1. Multi-Objective Optimization Framework

Adopting the iterative model of “parameterization adjustment-simulation verification-comprehensive evaluation”, the optimization objectives include the following: improving lighting uniformity; controlling summer glare and winter daylight balance; and reducing artificial lighting energy consumption.

3.4.2. Iterative Optimization Process

• Problem diagnosis: based on the initial simulation results, identify the lighting shortage areas (e.g., DF < 2% in the inner zone), glare exceeding points (DGI > 28), and energy consumption bottlenecks.
• Strategy generation:

  (1)

  Window opening optimization: adjust the window–wall ratio, sill height (e.g., from 0.6 m to 0.2 m), and window opening ratio (height/width optimized from 2:1 to 1:2).

  (2)

  Spatial intervention: add 8 m × 12 m lighting courtyard and introduce top light guide tube (tube diameter 300–400 mm, bending angle 12°).

  (3)

  Sunshade design: adopt adjustable inner louvers (45° during the summer solstice, 90° during the winter solstice), combined with fixed sunshade boards (width of 2 m, spacing of 2 m).

• Simulation verification: update Revit model after each optimization, re-export the gbXML file and import it into HYBPA 2024, and compare the DF, DGI, and hours of compliance before and after optimization.
• Termination condition: terminate the iteration [19] when all rooms have a DF of ≥3.3% (Class III) [16] and a DGI of ≤25 [16], and the proportion of annual compliance hours is ≥60% [19].

3.5. Description of Methodological Limitations

• Meteorological data accuracy: The current simulation is based on typical annual data and does not take into account the effects of extreme weather (e.g., continuous overcast).
• Simplification of the dynamic environment: the shading effect of indoor furniture and people’s activities on lighting is not fully quantified.
• Multi-physical field coupling: unlinked energy consumption simulation (e.g., the interaction between lighting and air-conditioning loads), which can be deepened subsequently in combination with EnergyPlus.

4. Case Design

4.1. Design Overview

The Campus Activity Center Design Project is located in Hunan City College, Heshan District, Yiyang City, Hunan Province, with a planned site area of 6200 m² and a total building area of about 3000 m², and the area indexes of each functional space are shown in

Table 3. Main functional components and design requirements of the building.

Typology	Room Name	Planned Room Area (m2)	Note
Space for maneuver	Function room	900 square meters	Art and calligraphy, music and dance, editorial and creative writing, etc.; 5–6 rooms
Administration building	Departmental offices	80 square meters	4–6 rooms, 20 m2/room
Exhibition space	Showroom	600 square meters
Leisure space	Teahouse	120 square meters
Reading space	Library reading room	120 square meters
Academic exchange	Multi-purpose hall	300 square meters	300 m2/room
Auxiliary space	Ancillary rooms	1100 square meters	Transportation space, restrooms, etc., depending on design
Add up the total	3200 square meters (±10%)

. The project base (Figure 1) is surrounded by Qingshan Lake on the north side, the development site on the south side, a natural mountainous area on the west side, and an artificial greenland and teaching buildings on the east side, which is in a good condition of the natural environment. Heshan District, Yiyang City, belongs to the monsoon humid climate in the transition from central subtropical to northern subtropical (Figure 2), with four distinct seasons—hot and rainy summers, cold and wet winters, late springs, short autumns—as well as more southerly winds in the summer, and northerly winds as the dominant winds during other seasons, with a large yearly difference in temperatures and a small day-to-day difference, with obvious regional differences.

As an educational public building, the Campus Activity Center is designed in strict accordance with the requirements of the “Building Lighting Design Standards” and “Green Building Evaluation Standards” to reduce energy consumption from the selection of materials, layout planning, and other aspects, scientifically designing the location, area, and form of lighting outlets to ensure that the natural light uniformly penetrates into the indoor areas and to create a comfortable activity space for teachers and students.

4.2. Design Options

4.2.1. Construction Program

The architectural program adheres to the principles of green building design, combined with the demand for use, and follows the design concepts of low-carbon building, a sponge city, and smart building, adapting to local conditions. The program selects steel as the structural material; the column network is 12 m × 12 m, and double side lighting is adopted. Activity rooms, display rooms, and other rooms adopt an open layout, and a courtyard, with a depth of 8 m is reserved between the north and south, spans 12 m. Initially, to avoid other factors affecting the daylight simulation data, this space is assumed to be enclosed and without natural light. For details, refer to the first-floor plan (Figure 3) and the second-floor plan (Figure 4).

The floor height of the building is designed to be 4.2 m, and the window design is based on the change in solar altitude angle of the base. The bathroom bay height is 1.5 m and the window height is 1.9 m; for other types of rooms, the bay height is 0.6 m and the window height is 2.8 m; via the formula:

$$\mathrm{p}\mathrm{r}\mathrm{o}\mathrm{j}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{o}\mathrm{f} \mathrm{s}\mathrm{u}\mathrm{n}\mathrm{l}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}={\displaystyle \frac{\mathrm{H}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{w}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{o}\mathrm{w} \mathrm{t}\mathrm{o}\mathrm{p}-\mathrm{H}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{w}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{o}\mathrm{w} \mathrm{s}\mathrm{i}\mathrm{l}\mathrm{l}}{\tan \theta }},$$

(1)

calculate the indoor projection length of the sun’s rays of the openings at noon; the results show that the projection length of each major time of the day are in line with the expected requirements (

Table 4. Indoor projected length of sun rays for building scheme window opening design.

Main Time of Day	Sun Altitude Angle (θ)	Height of Window Top − Height of Window Sill (m)	Indoor Projection Length of Sun Rays (m)	Compliance with the Indoor Projection Length Standard of Sun Rays	Indoor Projection Length Standard of Sun Rays (m)
Vernal Equinox (20 March)	61.4°	2.8 m	1.58 m	Yes	1 m–2 m
Summer Solstice (21 June)	84.9°	2.8 m	0.25 m	Yes	0.2 m–0.5 m
Autumnal Equinox (23 September)	61.4°	2.8 m	1.58 m	Yes	1 m–2 m
Winter Solstice (21 December)	37.9°	2.8 m	3.59 m	Yes	>3 m

Note: The units of the indoor projection length of sun rays, top height of window and sill height in the formula are m. θ refers to the sun altitude angle.

).

4.2.2. BIM Modeling

Based on the preliminary design scheme, model in Revit 2016 according to the above research method, export to gbXML format file after completion, and then import into HYBPA 2024 for lighting simulation analysis.

4.2.3. Setting Simulation Parameters

Set the project location as Heshan District, Yiyang City, Hunan Province, in the software to call local meteorological data. According to the lighting requirements of the different functional rooms on each floor, the room information is batch modified in the lighting simulation interface. In order to satisfy the lighting coefficient calculation formula:

$$C={\displaystyle \frac{E_n}{E_w}}$$

(2)

in the “Building Lighting Design Standards”, the simulation parameters are set as follows: sky conditions are CIE overcast, the reflectance ratio of the external surface of the building is 0.32, the reflectance ratio of the ground is 0.3, the windows and doors use 6Low-E + 12A + 6C double-silver Low-E glass [20], the door transmittance ratio is 0.3, the window structural light-blocking reduction coefficient is 0.80, the ceiling reflectance ratio is 0.84, the wall reflectance ratio is 0.85, the window structural light-blocking reduction coefficient is 0.80, and wall reflection ratio 0.70. See

Table 5. Reference limits for simulation parameters for each room.

Space Name	Room Name	Daylight Rating	Preset Lighting Types	Daylight Saving Factor Limit Value (%)	Lighting Compliance Area Ratio Limit (%)	Lighting Uniformity Limit	Glare Index Limit (DGI)	Window-to-Ground Ratio (WFR)
Lobbies	Walkways (educational buildings)	V	Side lighting	≥1.100	≥60%	≥0.300	≤28	0.100
Aisles	Walkways (educational buildings)	V	Side lighting	≥1.100	≥60%	≥0.300	≤28	0.100
Patio	Walkways (educational buildings)	V	Side lighting	≥1.100	≥60%	≥0.300	≤28	0.100
Teahouse	Specialized classrooms (educational buildings)	III	Side lighting	≥3.300	≥60%	≥0.600	≤25	0.200
Function room	Specialized classrooms (educational buildings)	III	Side lighting	≥3.300	≥60%	≥0.600	≤25	0.200
Auditorium	Lecture halls (educational buildings)	III	Side lighting	≥3.300	≥60%	≥0.500	≤25	0.200
Flight of stairs	Stairwells (educational buildings)	V	Side lighting	≥1.100	≥60%	≥0.300	≤28	0.100
Restrooms	Restrooms (educational buildings)	V	Side lighting	≥1.100	≥60%	≥0.300	≤28	0.100
Reading rooms	Reading rooms (library building)	III	Side lighting	≥3.300	≥60%	≥0.600	≤25	0.200
Showroom	Exhibition halls (museum buildings)	IV	Side lighting	≥2.200	≥60%	≥0.600	≤27	0.167
Conference room	Treasury (museum building)	V	Side lighting	≥1.100	≥60%	≥0.200	≤28	0.100
Business premises	Office (office building)	III	Side lighting	≥3.300	≥60%	≥0.500	≤25	0.200
Janitorial office	Office (office building)	III	Side lighting	≥1.100	≥60%	≥0.400	≤25	0.100

Note: C refers to the lighting coefficient; E_n refers to the diffuse light irradiation in the full cloudy sky, indoor diffuse light from the sky at a given point on the plane of the illuminance (lx); E_w—diffuse light irradiation in the full cloudy sky, and indoor illuminance at a certain point at the same time, same place, in the outdoor unobstructed level by the diffuse light from the sky produced by the outdoor illuminance (lx):, i.e., 13,500.0 lx.

for details of the specific settings.

4.2.4. Simulation Results and Discussion

Lighting simulation results show that the distribution of lighting coefficients and illuminance in each space shows obvious gradient changes: the lighting coefficients and illuminance in the area close to the external wall are higher, up to 8–10% and 800–1000 lx; the internal area away from the windows is lower, only 0–2% and 0–200 lx (Figure 5 and Figure 6). This suggests that the traditional single large-area glass curtain wall window opening form, although allowing good lighting in the exterior wall area, leads to serious uneven lighting in the room and fails to meet the lighting needs of the building’s interior space.

4.3. Optimization of Design Measures

In order to solve the problem of uneven indoor lighting, the initial window design was modified with reference to the recommended window-to-ground ratio of each functional room. A variety of window ratios are set and analyzed in simulation one by one to compare the lighting coefficients and illuminance distributions, and a scheme with good lighting uniformity is selected. On this basis, a courtyard is added to meet the lighting needs of non-external wall lighting rooms.

4.4. Simulation of Optimized Design of Facade Window Openings

4.4.1. Exterior Window Opening Program Design

Except for special rooms, the height of the window sill of the rest of the rooms is uniformly set at 0.6 m. For rooms with a depth greater than 6 m, the window height is maintained at 2.8 m, and for rooms with smaller depths—bathrooms, lecture halls, and so on—the height of the window is set at 1.5 m. A centralized window opening scheme is proposed and four window openings with different aspect ratios are proposed (

Table 6. Four window opening options with different aspect ratios.

Window Program	Opening Height (m)	Opening Width (m)	Wall Between Windows (m)	Approximate Ratio of Window Openings (Height/Width)
Option 1	2.8	1.5	0.9	2:1
Option 2	2.8	3.0	1.5 (large face width) 0.3 or 0.9 (small face width)	1:1
Option 3	2.8	4.0	1.5 (large face width) 0.3 or 0.9 (small face width)	2:3
Option 4	2.8	5.5	1.5 (large face width) 0.3 or 0.9 (small face width)	1:2

). Among them, the centralized window opening is a single large-area window for each major functional room, the other schemes are decentralized window openings with different aspect ratios, and the wall between the windows is set at 0.3 m, 0.9 m, or 1.5 m, according to the room width.

4.4.2. Discussion of Simulation Results

The centralized window opening scheme, due to the single centralized window opening, leads to uneven lighting in each room and lower lighting efficiency, with the average value of the lighting coefficient below the limit value in most rooms. Among the four window openings with different aspect ratios, the mean value of the lighting coefficient and the illuminance compliance rate of Scheme 4 (1:2 window opening ratio) are higher, and the lighting uniformity is better, but the activity room on the north side still has the problem of the lighting area ratio not meeting the standard and insufficient lighting on the inside of each room (

Table 7. Average Daylight Factor Simulation Diagram.

Design Scheme	Average Daylight Factor Simulation	Indoor Illuminance Simulation
Centralized
Scheme 1
Scheme 2
Scheme 3
Scheme 4

).

4.5. Secondary Optimization Design Simulation

4.5.1. Adjustment of Sill Height

On the basis of Scheme 4, the height of the window sill is adjusted downward by 0.4 m to increase the window height, while the lecture hall keeps the height of the window sill unchanged and adjusts the top of the window upward by 0.4 m. High-transmittance Low-E glass, with a transmittance ratio of 0.76 and a reflectance ratio of 0.11, is used to enhance the visible light transmittance rate. The simulation results (Figure 7 and Figure 8) show that the lighting coefficients and illuminance values of the rooms against the outer wall have been improved, but a few rooms such as the exhibition hall and activity room still do not meet the lighting standards, so it was decided to set up a courtyard.

4.5.2. Addition of Courtyard Space

A courtyard with a ratio of 1:1 (height/width) is set up in the interior of the building, and three scenarios are proposed: scenario A without shading, and scenarios B and C with steel shading panels of different sizes and densities. The simulation results (

Table 8. Simulation of three scenarios for the design of additional courtyard space.

Design Proposal	Courtyard Illustration	Simulation of the Average Value of the Lighting Coefficient	Indoor Illumination Simulation
Scenario A	sunless
Scenario B	Shades with a width of 2 m and a length of 8 m were used with a spacing of 2 m.
Scenario C	Shades with a width of 1 m and a length of 8 m were used with a spacing of 1 m.

) show that Scenario A has an outstanding lighting effect, and Scenarios B and C are not suitable due to the decrease in the quality of the light environment in the surrounding rooms caused by the sun shading panels. However, the rooms such as the north-facing office in Scheme A still fail to meet the lighting requirements due to the limitation of the north-facing lighting conditions, and the inner area of the space with a large depth on the north side has insufficient lighting.

4.5.3. Adding Top Lighting

Because light guide tubes have the advantages of introducing natural light, avoiding direct sunlight, and reducing heat gain [5], light guide tubes are used instead of traditional skylights for the top lighting design. Light guide tubes of different diameters and spacing are set according to the room area, with an installation height of 2.75 m, a bending angle of 12°, and a light inlet angle of 30° [21], in order to adapt to the climatic characteristics of the hot-summer and cold-winter regions.

After two lighting simulations, static and dynamic, the dynamic lighting simulation is more reasonable. The results of the dynamic daylighting simulation (Table 7) show that the annual average hours of most of the rooms in over 60% of the area reach 4–10 h, which meets the requirements of the Green Building Evaluation Standards. However, the offices near the inner court have only 1.56–2.06 h/d of annual average daylight hours, ranging from 38.54% to 51.19% of the area that meets the standard, which has not yet met the standard. Therefore, the ratio of window openings on the outer wall of the office was expanded, the size of the window opening was enlarged from 1600 mm × 3200 mm to 3200 mm × 3200 mm, and the annual average illuminance and sunshine hours of the main functional spaces met the requirements after optimization (

Table 9. Schematic diagram of dynamic simulation after adding top lighting.

Addition of Top Lighting	Annual Average Illuminance Simulation	Simulation of Average Annual Hours	Achievement Determination Schematic Diagram
Dynamic simulation
Dynamic simulation optimization

).

4.5.4. Glare Simulation and Sunshade Design

The results of the glare simulation at 12:00 noon on the summer solstice (

Table 10. Calculated Values for Scenario A Glare Simulation.

Lighting Zones to Which the Room Belongs		Room Name	Analog Point Name	DGI Calculated Values	DGI Limits	Whether or Not the Conditions Are Met
Calculated values for first floor planar glare simulation	III	Office 2	Simulation point 1	21.128342	25	fulfillment
		Activity room 2	Simulation point 2	0	25	fulfillment
		Reading rooms	Simulation point 3	0	25	fulfillment
			Simulation point 4	0	25	fulfillment
		Auditorium	Simulation point 5	16.303476	25	fulfillment
			Simulation point 6	17.658905	25	fulfillment
			Simulation point 7	16.886646	25	fulfillment
			Simulation point 8	17.610703	25	fulfillment
	IV	Exhibition Hall 1	Simulation point 9	24.230068	27	fulfillment
		Exhibition Hall 2	Simulation point 10	0	27	fulfillment
			Simulation point 11	0	27	fulfillment
			Simulation point 12	0	27	fulfillment
	V	Stairwell 1	Simulation point 13	0	28	fulfillment
		Aisle 1	Simulation point 14	0	28	fulfillment
			Simulation point 15	0	28	fulfillment
		Lobbies	Simulation point 16	22.777187	28	fulfillment
			Simulation point 17	24.129299	28	fulfillment
		Stairwell 2	Simulation point 18	19.306282	28	fulfillment
		Aisle 2	Simulation point 19	17.782026	28	fulfillment
			Simulation point 20	0	28	fulfillment
			Simulation point 21	22.873875	28	fulfillment
Calculated values for second floor planar glare simulation	III	Activity room 4	Simulation point 22	0	25	fulfillment
			Simulation point 23	0		fulfillment
			Simulation point 24	13.862787		fulfillment
			Simulation point 25	16.069291		fulfillment
		Activity room 6	Simulation point 26	0	25	fulfillment
		Teahouse	Simulation point 27	0	25	fulfillment
			Simulation point 28	0	25	fulfillment
	IV	Exhibition Hall 3	Simulation point 29	0	27	fulfillment
			Simulation point 30	0	27	fulfillment
			Simulation point 31	0	27	fulfillment
	V	Stairwell 4	Simulation point 32	0	28	fulfillment
		Stairwell 5	Simulation point 33	16.806961	28	fulfillment
		Aisle 3	Simulation point 34	12.465942	28	fulfillment
			Simulation point 35	0	28	fulfillment
			Simulation point 36	11.904585	28	fulfillment

) show that the calculated DGI values of the rooms with various levels of lighting are all within the limits, indicating that the main functional spaces of the building are less affected by the uncomfortable glare, and the visual comfort of the indoor environment [18] can be guaranteed. However, the illuminance in the window area of each room exceeds 1000 lx, which can easily lead to visual fatigue, so the design adopts low-reflection vertical internal shutters as a shading measure, with a shutter length of 0.20 m and a spacing of 0.15 m.

Comparison of the static (

Table 11. Comparison of static daylighting simulations for schemes.

Design Proposal	Static Simulation	Simulation of the Average Value of the Lighting Coefficient	Indoor Illumination Simulation
Option A	summer solstice
	winter solstice
Option B	summer solstice
	winter solstice

) and dynamic daylighting simulation shows that the shutters achieve a good shading effect during the summer solstice, and although the winter solstice reduces the lighting, the main functional spaces still meet the average lighting coefficient requirements and avoid the problem of excessive winter lighting in the window areas. The dynamic lighting simulation results of Scheme B (Figure 9) show that the uniformity of indoor light illumination [22] improves throughout the year, and the area above 1000 lx decreases significantly, which is in line with the requirements of the Green Building Evaluation Standards.

5. Conclusions and Outlook

5.1. Main Findings

In this study, with the help of BIM technology and a building performance analysis platform, we conducted multiple rounds of iterative simulation and optimization for daylighting design of campus living rooms in hot-summer and cold-winter areas and came up with the following conclusions:

(1)

Traditional empirical daylighting design methods are difficult to adapt to green building needs. The quantitative analysis of indicators such as mean value of lighting coefficient [23], indoor illuminance [24], uncomfortable glare index [25], and average annual sunshine hours [26] based on BIM technology provides a scientific reference for the lighting design of living rooms in campus in hot-summer and cold-winter areas.

(2)

A single large-area external window lighting method cannot meet the lighting requirements of green buildings, and the use of a hybrid lighting method of the side [27] and top [28] and the setting of appropriate shading measures according to the simulation results can significantly improve the indoor light environment.

(3)

Single static simulation has limited support for building energy efficiency and comfort improvement. The combination of static and dynamic daylighting simulation and glare numerical calculation can provide more comprehensive data support and optimization direction for building design.

5.2. Research Limitations

This study has the following limitations: first, it primarily relies on the “Building Daylighting Design Standards” and the “Green Building Evaluation Standards,” with insufficient integration of international healthy building standards; second, the meteorological data used in the simulation software lacks precision, failing to adequately consider the long-term dynamic changes in regional climate and local microclimate effects; third, the impact of dynamic factors such as indoor pedestrian flow distribution and furniture arrangement on daylighting effects has not been thoroughly investigated; and finally, the exploration of intelligent daylighting systems is insufficient.

5.3. Research Outlook

Future research could be deepened in the following directions:

(1)

Promote the integration of cross-standard evaluation systems: In optimization strategies, further deepen the synergistic application of WELL standards with domestic standards. This should not only reference their higher-level requirements for “visual comfort” (e.g., DGI ≤ 25) and introduce new indicators such as “per capita effective daylighting area” (≥3.5 m²/person), but also incorporate detailed indicators from WELL v2, such as “circadian rhythm lighting design” and “the impact of light environments on cognitive performance” [29], to establish a multi-tiered evaluation system covering basic performance, health benefits, and human adaptability and achieve systematic application in campus buildings in regions with hot summers and cold winters.

(2)

Improve the accuracy and adaptability of meteorological data. Future research should focus on the long-term impact of climate change on building light environments, conduct future light environment prediction simulations for the site where the building design is located, such as incorporating future climate scenario prediction data to simulate light environment changes around 2050, and assess the long-term adaptability of building designs to help buildings better adapt to climate change.

(3)

Incorporate dynamic indoor environmental factors. Convert dynamic factors such as pedestrian flow and furniture arrangement into corresponding daylighting simulation parameters to further enhance the practicality and comfort of daylighting design.

(4)

Explore intelligent building daylighting systems. With the help of the Internet of Things [30], artificial intelligence [31], and other technologies, further explore the application of intelligent control systems in building daylighting design, such as automatically adjusting window shading [32], light intensity [33], and indoor temperature and humidity [34] based on weather conditions, light changes, and usage requirements to achieve real-time and accurate control of the light environment.