Energy-Efficient Design of Immigrant Resettlement Housing in Qinghai: Solar Energy Utilization, Sunspace Temperature Control, and Envelope Optimization

Abstract

Qinghai Province urgently requires the development of adaptive energy-efficient rural housing construction to address resettlement needs arising from hydropower projects, given the region’s characteristic combination of high solar irradiance resources and severe cold climate conditions. This research establishes localized retrofit strategies through systematic field investigations and Rhinoceros modeling simulations of five representative rural residences across four villages. The key findings reveal that comprehensive building envelope retrofits achieve an 80% reduction in energy consumption. South-facing sunspaces demonstrate effective thermal buffering capacity, though their spatial depth exhibits negligible correlation with heating energy requirements. An optimized hybrid shading system combining roof overhangs and vertical louvers demonstrates critical efficacy in summer overheating mitigation, with vertical louvers demonstrating superior thermal and luminous regulation precision. Architectural orientation analysis identifies an optimal alignment within ±10° of true south, emphasizing the functional zoning principle of positioning primary living spaces in south-oriented ground floor areas while locating auxiliary functions in northeastern/northwestern zones. The integrated design framework synergizes three core components: passive solar optimization, climate-responsive shading mechanisms, and performance-enhanced envelope systems, achieving simultaneous improvements in energy efficiency and thermal comfort within resettlement housing constraints. This methodology establishes a replicable paradigm for climate-resilient rural architecture in high-altitude, solar-intensive cold regions, effectively reconciling community reconstruction needs with low-carbon development imperatives through context-specific technical solutions.

1. Introduction

1.1. Background

China is undergoing large-scale hydropower development, and Qinghai Province, a key area for such projects, faces challenges as original villages are displaced or submerged. As a result, residents must relocate to new resettlement areas. This migration involves not only social resettlement, but also the practical need for new housing design and construction.

Research on immigrant resettlement housing in Qinghai is of significant importance, particularly in the field of energy-efficient design. Located in a cold, high-altitude region, Qinghai experiences harsh climatic conditions with high heating demands in winter, yet limited energy supply. Efficient energy utilization through building design is crucial to improving residents’ quality of life and ensuring regional sustainable development. Moreover, these resettlement houses are often built in ecologically vulnerable areas. Their construction is not only a key means of improving living conditions, but also an essential measure for ecological protection and regional coordinated development. Optimizing building design to meet housing needs while reducing energy consumption (EC) has profound implications for environmental conservation.

Compared to traditional villages, the energy-efficient design of immigrant resettlement housing in Qinghai is distinctly unique. Traditional villages typically rely on local materials and architectural layouts based on experience to adapt to the natural environment, but their energy efficiency is low and winter insulation is poor. In contrast, immigrant resettlement houses leverage modern building technologies, such as solar heating, sunspace temperature control, and high-performance building envelopes, to better adapt to extreme climatic conditions. At the same time, these houses must strike a balance between cultural adaptation and modernization, preserving traditional living practices while integrating energy-saving technologies to enhance comfort.

The uniqueness of Qinghai’s immigrant housing design lies in its response to the harsh, high-altitude climate and unique ecological environment. The region enjoys long hours of sunlight and abundant solar resources but faces long, cold winters with large diurnal temperature variations. Research must focus on how to efficiently utilize solar energy and store heat. This differs fundamentally from studies of resettlement housing in warmer or lower-altitude regions, where such extreme climate conditions are not a concern. Moreover, as Qinghai is a key area for ecological protection, resettlement housing must minimize environmental impact during material selection, construction, and use.

Compared to newly constructed urban residential areas, the energy-efficient design of resettlement housing in Qinghai differs significantly. New urban developments often rely on well-established municipal infrastructure, such as central heating and power grids. In contrast, many of Qinghai’s resettlement houses are located in remote areas with limited infrastructure, requiring energy self-sufficiency through the building itself. As such, energy-efficient design in these areas relies more heavily on passive technologies, such as optimizing building orientation, enhancing envelope insulation, and using natural light and heating through sunspaces and interior layouts. Additionally, these houses emphasize harmony with the natural environment, whereas urban residential areas focus more on building density and land use efficiency.

The research primarily focused on four villages. Among them, Village 4 is the original village, while the other three are resettlement villages. Village 1 and Village 3 are already completed, while Village 2 is currently under construction. Floor plans and architectural images of the typical rural houses are shown in Figure 1 and Figure 2.

After conducting field surveys in the four villages, several noteworthy phenomena and issues were identified. The specific research topics based on these issues are outlined in

Table 1. Phenomena, issues identified in field surveys, and specific research topics.

Issues Identified in Field Surveys
(a) The thermal performance of the existing rural house envelopes is poor, with limited material options and simple construction methods. (b) The outdoor diurnal temperature range is wide, with long heating periods and generally cold temperatures. In the summer, the air temperature is mostly within the comfort zone, but radiant heat creates noticeable discomfort. (c) Both in the summer (June to August) and the heating season (15 October to 15 April), indoor thermal discomfort is common. (d) There are differences in the form, size, and construction of sunspaces. Overheating occurs inside the sunspaces in the summer, and the existing shading methods are inadequate. (e) Temperature differences between rooms in different locations are significant.
Phenomena
(a) The building orientation varies to some extent, which may impact the thermal load. (b) Differences in floor plans affect building energy efficiency, thermal comfort, and solar radiation acquisition. (c) Terraces originally designed in construction plans have been converted by residents into enclosed sunspaces, some of which serve as bedrooms, living rooms, and dining areas. (d) Some residents make slight adjustments to the functional layout and room dimensions based on the construction drawings.
Specific Research Topics
(a) Evaluation and optimization of the building envelope thermal performance. (b) Comparison of building’s EC with and without sunspaces. (c) Impact of sunspace depth on the EC. (d) Selection of shading methods for sunspaces in the summer. (e) Impact of building orientation on the EC. (f) Temperature, comfort duration, and EC differences caused by room location and floor plan variations.

.

1.2. Literature Review

Based on the research background and the research questions and content of this paper, the review of the existing studies was divided into three areas: building’s EC and indoor thermal comfort [1,2,3], building’s solar radiation acquisition or avoidance [4,5,6], and use of sunspaces [7,8,9].

1.2.1. Building’s EC and Indoor Thermal Comfort

In terms of building’s EC and indoor thermal comfort, Tettey and Gustavsson [10] conducted energy retrofits on the building envelopes and systems of old rural houses in Sweden. They found that through the combined use of energy-efficient windows, ventilation heat recovery, and external wall insulation, the building’s heating and cooling loads were significantly reduced, resulting in a 58% and 54% reduction, respectively. Huang et al. [11] optimized the thermal performance of building envelopes in the existing buildings in Beijing from a lifecycle cost perspective, proposing an energy-efficient retrofit plan with optimal economic benefits. Terés-Zubiaga et al. [12] selected a building in northern Spain and tested 64 energy-saving measures for the envelope using field monitoring and TRNSYS software (https://www.trnsys.com/, accessed on 22 April 2025) simulation, evaluating the building’s energy-saving potential and economic viability.

Cholewa et al. [13] explored the impact of hydraulic balancing in heating systems after retrofitting the building envelope’s thermal performance on EC and economics. Based on energy data comparisons before and after energy retrofits in 11 residential buildings in Poland, they found that the actual energy savings ranged from 8.8% to 74.8%, depending on the level of renovation, with a payback period of 3.1 to 104.8 heating seasons. Cortiços [14] proposed using mineral board-supported stretch membranes to wrap residential buildings in the Mediterranean region to achieve energy savings, improve indoor comfort, and extend building lifespan. The research showed that performance could be improved by 62.23% in inland and coastal cities with high economic feasibility over a 30-year span.

Zhang et al. [15] proposed an integrated method using a genetic algorithm and EnergyPlus software (https://energyplus.net/, accessed on 22 April 2025) to select external wall coating parameters. The results showed a conflict between energy saving and thermal comfort needs, particularly regarding solar reflectivity in hot-summer, cold-winter regions. Huang et al. [16] incorporated corn husks into concrete used for non-transparent building envelopes in Qingdao, finding that adjusting the mixing ratio significantly improved thermal performance and energy efficiency. He et al. [17] introduced the concept of net present value to determine the optimal solution considering both energy savings and costs, suggesting that energy-saving targets of 60% and 50% be applied in cold and very cold regions, respectively. Huang and Lin [18] explored the game theory perspective on the interests and potential behavior mechanisms of the government, enterprises, and residents, highlighting the key factors affecting collaboration among the three parties and providing insights for promoting energy-efficient retrofits in rural housing.

1.2.2. Building’s Solar Radiation Acquisition or Avoidance

In terms of building’s solar radiation acquisition or avoidance, Akrofi and Okitasari [19] analyzed the relationship between the community form, the rooftop photovoltaic potential, and the electricity generation costs for different income groups in Accra, Ghana, revealing that building density, compactness, roof area, and building spacing are the key factors influencing photovoltaic energy production. Freitas et al. [20] reviewed prediction methods for solar radiation acquisition on urban building surfaces, explaining the evolution of these methods. Kaleshwarwar and Bahadure [21] assessed the solar energy potential of five different urban forms in Nagpur, India, identifying buildings, density, open spaces, streets, and vegetation as significant factors. Song et al. [22] compared the impact of four urban form indicators—floor area ratio, building density, building height, and layout—on solar radiation acquisition in residential areas, providing a sensitivity ranking for each indicator. Xiao et al. [23] proposed a cross-latitude prediction model for building solar radiation acquisition, using key form indicators and latitude cosine as independent variables, and found a relatively high prediction accuracy after correlation analysis.

Liu et al. [24] reviewed the evolution of studies on the relationship between urban form indicators and solar radiation acquisition from 2011 to 2022, summarizing the common and differing conclusions. Hu et al. [25] proposed a rooftop photovoltaic estimation method based on remote sensing images and validated its accuracy. Cao et al. [26] used EnergyPlus software to simulate energy-efficient retrofits of rural houses in Tongchuan, Shaanxi, optimizing retrofit parameters such as insulation thickness, window-to-wall ratio, and window type using entropy-based methods, and highlighted the balance between energy savings, emissions reduction, and economic feasibility. Cui et al. [27] compared the thermal performance of a rural house in Shandong, China, before and after renovation using DesignBuilder software (https://designbuilder.co.uk/, accessed on 22 April 2025), confirming that the envelope’s thermal performance and the inclusion of sunspaces are the key factors for improving indoor temperature and energy efficiency.

1.2.3. Use of Sunspaces

In terms of sunspaces, Wang et al. [28] proposed adding a sunspace to the roof to address heating issues in cold-region rural houses. Simulation with DesignBuilder revealed that the optimal energy-saving solution involved a roof tilt angle of 28°, with the glass-to-roof ratios of 0.5 and 0.6 for the front and rear roofs, respectively. Additionally, a solar thermal water system on the north wall significantly improved the indoor temperature. Ma et al. [29] further explored ways to enhance the energy-saving effect of sunspaces in cold-region rural houses, examining the influence of glass type, filling gas type, and gas layer thickness on EC. They concluded that a sunspace with 9 mm double-glazed glass filled with krypton gas was the most efficient solution. Vukadinović et al. [30] studied energy performance optimization for standalone houses with sunspaces in the humid subtropical climate of Niš, Serbia, using DesignBuilder and EnergyPlus software. By applying a non-dominant sorting genetic algorithm, they found that the window-to-wall ratio had the greatest impact on energy performance. They recommended using triple low-emissivity argon-filled glass and increasing the thickness of concrete or brick walls while minimizing or eliminating shading devices on the south façade.

Yao et al. [31] focused on the shape, size, and structure of transparent building envelopes for rural houses in China’s cold climate zone. They found that multi-objective optimization significantly improved performance in terms of daylighting, heating and cooling loads, and thermal comfort. The research provided optimal window-to-wall ratios for different orientations with and without a sunspace, as well as the thermal performance benefits of the sunspace. Ma et al. [32] proposed an energy-efficient retrofit for sunspaces in cold-region rural houses using phase change materials, adding silica aerogel to the walls and glass. After balancing energy savings and indoor temperature, they recommended using a 9 mm layer of silica aerogel. Li et al. [33] developed a multi-factor, multi-objective optimization method for sunspace retrofits based on orthogonal testing and entropy weighting, highlighting the key role of glass type and window-to-wall ratio in energy efficiency. The research provided optimal selections for the window-to-wall ratio, sunspace depth, and insulation board thickness, along with their respective weights.

Li et al. [34] found that adding a sunspace in rural houses in cold regions of China could reduce the EC during the heating season (EC_hs) by about 20%, with the addition of phase change blinds further saving approximately 5%. Zhao et al. [35] proposed a control strategy for actively opening or closing internal windows to better manage the heat transfer in sunspaces. Simulations for northern China rural houses using TRNSYS software showed that this method significantly improved the indoor thermal comfort and contributed to energy savings and emissions reductions. Ma et al. [36] conducted experimental research on sunspace energy efficiency in Miyazaki, Japan, and found that sunspaces could reduce building’s EC by 12%.

1.3. Research Gap

Current studies on energy-efficient housing in cold climates lack integrated strategies addressing extreme diurnal temperature variations, prolonged heating demands, and resettlement-driven spatial constraints. While passive solar design and envelope retrofits are explored, systematic frameworks combining adaptive shading, optimized solar utilization, and sociocultural adaptation in infrastructure-limited contexts remain underdeveloped. The existing approaches often prioritize theoretical models or urban settings, neglecting the unique challenges of high-altitude regions with limited energy infrastructure under hydropower displacement. This study bridged these gaps by proposing a synergistic retrofitting strategy, validated through field data and simulations, to balance energy efficiency, thermal comfort, and spatial functionality in solar-rich cold regions.

1.4. Research Purpose

The purpose of this research was to analyze the key factors affecting building energy efficiency, solar radiation management, and thermal comfort in Qinghai’s immigrant resettlement housing. It aimed to understand how these factors influence building performance and residential comfort. The goal was to provide a scientific foundation for designing more efficient resettlement housing, optimizing energy use, reducing consumption, enhancing thermal comfort, and promoting green building practices for sustainable development.

1.5. Research Significance

1.5.1. Theoretical Significance

The research analyzed the key factors of building energy efficiency, solar radiation acquisition and avoidance, and thermal comfort enhancement, thereby enriching the theoretical framework of building energy performance and sustainable design, particularly in the context of cold, high-altitude climates. It offers new insights into the relationship between building forms and solar energy utilization, providing a theoretical foundation for further exploration in this area. The research advances the understanding of the interaction between architectural design and the environment, contributing to the deeper integration of environmental factors into building design practices.

1.5.2. Practical Significance

This research holds significant practical implications, particularly for the construction of immigrant resettlement housing in high-altitude, cold regions such as Qinghai. By optimizing building design and energy use, it not only effectively reduced the EC and dependence on external energy sources, but also enhanced residential comfort and improved the quality of life for residents. Furthermore, the findings contribute to the implementation of green building practices and energy-saving policies, providing a scientific basis for sustainable architectural design. This research supports the development of energy-efficient, environmentally responsible housing solutions, facilitating the realization of long-term sustainability goals.

2. Methodology

2.1. Research Path and Method

This research adopts the following systematic approach. First, aligned with the phenomena, issues, and research framework delineated in Table 1, technical tools and methodological frameworks were carefully selected to implement modeling and simulation studies. Subsequently, simulation-derived results underwent thorough processing and systematic analysis. Ultimately, focusing on solar energy utilization efficiency, EC control, and thermal comfort performance, this study synthesized targeted construction strategies for immigrant resettlement housing in Qinghai Province, addressing the core challenges identified in preliminary studies. The comprehensive research workflow and methodological architecture are explicitly illustrated through the technical roadmap in Figure 3.

2.2. Research Tools

The study employed a purpose-built toolchain optimized for high-altitude housing analysis, with the selection criteria summarized in

Table 2. Tool selection rationale.

Tool	Functional Role	Key Advantage over Alternatives
Rhinoceros + Ladybug/Honeybee	Parametric solar optimization Climate-bound energy modeling	Enables real-time design–simulation feedback loops (vs. static workflows in EnergyPlus/DesignBuilder)
SPSS	Nonparametric analysis of small-sample behavioral data	Preconfigured rural survey protocols (vs. scripting-dependent R/Python)
Excel + Origin	Time-series synthesis of energy–behavioral correlations	High-resolution thermal data interoperability (vs. visualization-focused BI tools such as Tableau)

.

2.3. Variable Definition

Table 3. Correspondence between independent and dependent variables.

	Independent Variables	Independent Variables Range or Variation Forms	Dependent Variables
1	Heat transfer coefficient of walls and roofs Single-glazed vs. double-glazed windows and air gap thickness	Heat transfer coefficient: actual value/code limit Window type: single-glazed/double-glazed Air gap thickness: 3 mm, 6 mm, 9 mm, 12 mm, 16 mm, 24 mm, 30 mm	EChs; EC per unit area during the heating season (ECpua)
2	Presence or absence of the sunspace	Presence of the sunspace: yes/no	EChs; ECpua
3	Depth of the sunspace	Depth of the sunspace: 0.9 m to 3 m	EChs; ECpua
4	Shading type for the sunspace in the summer	Shading type: panel/louvers/combined Louver deflection angle: 0°, 30°, 45°, 60°, 90°	Average operating temperature on the winter solstice (Tao); proportion of the comfort time
5	Building orientation	Orientation angle: from −30° to 30° (0° represents south-facing, positive values are clockwise, negative values are counterclockwise)	EChs; ECpua
6	Room location in the building and comparison of floor plan variations	Explanation based on the actual situation	Tao of each room without heating; EChs; ECpua

systematically defines independent variables, dependent variables, and operational parameter ranges aligned with research themes (Table 1, Section 1.1). The parameter ranges were strategically expanded beyond empirical baselines to harmonize theoretical rigor with practical feasibility, addressing Qinghai’s environmental specificity and sustainable design principles.

Rationale for the control-variable approach:

(1) Policy-driven prioritization: isolates absolute energy impacts of individual parameters to establish actionable retrofit benchmarks, directly serving policymaking objectives.

(2) Context-bounded exploration: parameter ranges integrate regional constraints, rendering sensitivity analysis redundant within predefined implementation boundaries.

(3) Methodological coherence: controlled parameter variations ensure traceability between design interventions and energy outcomes, validated through alignment with regional energy standards and field observation protocols.

This methodology ensures transparent, policy-actionable insights into parameter-specific energy impacts under constrained real-world conditions.

2.4. Samples and Data Sources

To balance analytical depth with feasibility, the floor plan with the largest building area in each village served as the representative case for detailed investigation. This selection criterion not only ensured spatial representativeness, but also inherently enhanced the comparability of energy performance outcomes across heterogeneous housing typologies.

For simulations addressing sunspace depth optimization and summer shading configurations, simplified geometric models systematically isolated thermal and energy interactions. This controlled methodology prioritized parametric precision over contextual complexity, thereby strengthening causal inference between design variables and performance outcomes. Conversely, analyses of other influencing factors employed actual rural house prototypes to maintain site-specific architectural authenticity.

Data generation followed a systematic parametric protocol: independent variables underwent incremental adjustment within predefined ranges (Table 3), with corresponding dependent variables recorded under standardized boundary conditions. While this approach intentionally excludes socioeconomic variables such as occupant adaptation patterns, it ensures high internal validity by eliminating confounding factors unrelated to the built environment’s performance.

2.5. Simulation Verification of Software Application Accuracy

A standardized rectangular prototype (8 m × 4 m, Figure 4) served as the parametric simulation framework, with its thermal properties and disturbance parameters rigorously aligned with national building energy codes [37,38] (

Table 4. Thermal and thermal disturbance parameter settings.

Thermal Parameters
Name	Set Value	Name	Set Value
Window-to-wall area ratio	0.45	Roof heat transfer coefficient	0.2 W/(m2·K)
Window heat transfer coefficient	1.4 W/(m2·K)	External wall heat transfer coefficient	0.3 W/(m2·K)
SHGC	0.32	Latent heat recovery efficiency	0.7
Indoor Thermal Disturbance Parameters
Personnel occupancy rate	0.04 people/m2	Lighting power	5 W/m2
Equipment power	3.8 W/m2	Heat point	18 °C

). Crucially, the solar heat gain coefficient (SHGC) values were derived through an inverse correlation with the window thermal transmittance (U-value) thresholds specified in Appendix D of the Technical Standard for Nearly Zero Energy Buildings [38], reflecting the empirical trade-off between insulation performance and solar gain regulation in commercially available glazing systems.

To ensure methodological rigor, simulated annual EC underwent benchmarking against code-prescribed regulatory baselines, demonstrating adherence to the standardized energy performance thresholds. Occupancy patterns and HVAC operational parameters were iteratively calibrated through Qinghai-specific field observation protocols, reconciling code compliance with regionally representative usage scenarios. This integrated validation paradigm—synthesizing regulatory baselines with contextual operational parameters—established a robust foundation for policy-responsive scenario projections while preserving thermodynamic traceability.

According to the simulation results, EC_pua was 37.65 kW·h/m², while the standard specifies 138 MJ/m² (which is equivalent to 38.33 kW·h/m²). The difference between the two values was approximately 1.75%. Since the error was controlled within 2%, the results obtained from subsequent EC studies could be considered reliable and accurate.

3. Results and Discussion

3.1. Overview of Weather Conditions

Qinghai experiences distinct seasons, with a climate classification of cold zone 1C according to China’s thermal zoning. Winters are cold and long, with temperatures often dropping below freezing and frequent snowfall. Summers are relatively short and warm, with temperatures generally not reaching extreme highs, but with significant diurnal temperature variations. The spring and autumn seasons show more pronounced temperature changes, with gradual warming or cooling. Due to its plateau location, Qinghai has dry air and relatively low precipitation, with the annual rainfall mainly concentrated in the summer months.

The research focuses on solar radiation and temperature based on meteorological data obtained from the EnergyPlus official website. The data were analyzed using the Ladybug plugin in Rhinoceros 8 software and visualized in Origin 2025 software.

3.1.1. Temperature

The typical daily hourly temperature profile for Qinghai is shown in Figure 5. On the summer solstice, the dry bulb temperature ranges from 8.2 °C to 24.8 °C, while on the winter solstice, it ranges from −18.1 °C to 0.2 °C. The radiant temperature on the summer solstice varies between 3.89 °C and 50.83 °C, and on the winter solstice, it ranges from −24.81 °C to 20 °C. The highest temperatures on both solstices occur at 4 P.M., while the lowest temperatures occur at 6 A.M. on the summer solstice and 8 A.M. on the winter solstice.

From 6 A.M. to 8 P.M. on the summer solstice, and from 8 A.M. to 5 P.M. on the winter solstice, solar radiation has a strong influence on the outdoor human thermal sensation. The peak radiant temperature occurs at 4 P.M. on the summer solstice and at 11 A.M. on the winter solstice. Overall, solar radiation has a positive and significant effect on improving the outdoor thermal environment in winter. However, in the summer, it can lead to overheating, and solar radiation also contributes to a significant increase in the diurnal temperature range.

3.1.2. Solar Radiation

Using the Grasshopper feature in Rhinoceros software and its Ladybug plugin, the research developed a program for calculating the slope’s solar radiation acquisition, a program for determining the optimal azimuth and altitude angles of the slope from the perspective of solar radiation (where 0° is horizontal and 90° is vertical), and a program for calculating solar energy resource classification indicators. According to the solar energy resource classification standards (GB/T 31155-2014) [39], solar energy resource classification is based on three indicators: total annual solar radiation, stability, and direct ratio.

The results show that the optimal azimuth angle for the entire year is 10° west of south, and the optimal altitude angle is 62.09°. During the heating season, the optimal azimuth angle is 15° west of south, and the optimal altitude angle is 46.03°. The solar energy resource classification for this region is shown in

Table 5. Solar energy resource level in Qinghai.

Total Annual Solar Radiation [kW·h/(m2·a)]	Direct Ratio	Stability
1566.35	0.688	0.596
1750 > X ≥ 1400, very abundant	>0.6, very high	≥0.47, very stable

. The results confirm that this area achieved the highest classification for all three indicators used in determining solar energy resources.

The solar radiation on the horizontal plane and the optimal slope in the region are 1376.99 kW·h/(m²·a) and 1566.35 kW·h/(m²·a), respectively. However, according to the annual report [39], the average solar radiation on the horizontal plane and the optimal slope in Qinghai are 1798.11 kW·h/(m²·a) and 2105.48 kW·h/(m²·a), respectively. Although the region enjoys abundant solar resources, there is still a noticeable gap when compared to the average levels in the province.

3.2. Verification of the Effect of Retrofitting the Thermal Performance of Rural Houses According to the Standard Limits

Building 5 was selected as the sample, with the constructed model of the house shown in Figure 6. The house was retrofitted for energy efficiency according to the standard limits [40], and the changes in the thermal performance of the envelope structure and the overall building before and after the retrofit were compared. The original materials and construction parameters of the rural house envelope are listed in

Table 6. Original construction methods of the rural house.

Envelope Structure Component	Construction
Roof	38 mm cement mortar + 120 mm graphite EPS board + 18 mm cement mortar + 200 mm concrete roof slab
Exterior wall	18 mm cement mortar + 100 mm graphite EPS board + 18 mm cement mortar + 240 mm coal gangue porous brick + 18 mm cement mortar
Exterior window	5 mm + 12A + 5 mm
Sunspace window	5 mm
Interior window	5 mm + 12A + 5 mm

and

Table 7. Thermal performance parameters of materials in the original rural house.

Material	Thermal Conductivity, W(/m·K)	Dry Density, kg/m3	Specific Heat, J/(kg·K)
Cement mortar	0.93	1800	1050
Graphite EPS board	0.33	180	1380
Concrete roof slab	1.74	2500	920
Coal gangue porous brick	0.81	1800	1050
Window	Default parameters

.

Analysis in Rhinoceros software revealed that the heat transfer coefficients of the external wall and roof were 0.66 W/(m²·K) and 1.45 W/(m²·K), respectively. According to the standard limits, these values should not exceed 0.3 W/(m²·K) for the external wall and 0.2 W/(m²·K) for the roof. These results demonstrate that the thermal performance of the envelope structure of this rural house failed to meet the required standards prior to retrofitting.

The energy-saving retrofit of the windows was not carried out in the same way as of the exterior walls and roof, where the heat transfer coefficients were directly adjusted to meet the standard limits. This is because windows also involve factors such as the SHGC and visible light transmittance, both of which need to be tailored based on the specific construction of the window. Therefore, the retrofit for the windows focused on using single- or double-glazed windows and adjusting the thickness of the air layer.

The results of the energy-saving retrofit are shown in

Table 8. Comparison of the thermal performance before and after retrofitting the envelope structure.

Plan	Retrofitting	East Bedroom	Living Room	West Bedroom	Middle Bedroom	Overall Building	Overall Energy Savings
		kW·h/(m2·a)					(%)
1	Original rural house	132.72	85.68	117.45	86.38	104.10	0
2	Exterior wall retrofit	76.03	59.02	71.63	59.62	66.02	36.58
3	Roof retrofit	88.77	42.43	71.74	43.19	60.15	42.22
4	Exterior wall + roof retrofit	32.16	24.21	33.62	24.67	28.31	72.80
5	Exterior window changed to single glazing	150.36	94.18	135.06	94.9	116.8	−12.20
6	Exterior window changed to 5 + 15A + 5 double glazing	132.22	85.66	116.93	86.36	103.85	0.24
7	Exterior window changed to 5 + 18A + 5 double glazing	131.98	85.65	116.7	86.35	103.74	0.35
8	Exterior window changed to 5 + 24A + 5 double glazing	132	85.65	116.73	86.35	103.75	0.34
9	Interior window changed to double glazing	133.07	86.13	117.82	86.87	104.52	−0.40
10	Sunspace changed to double glazing	132.11	75.28	111.89	76.06	97.12	6.71
11	Exterior wall + roof + sunspace double-glazed retrofit	29.93	12	25.88	12.44	19.46	81.31

. It is clear that the exterior walls and roof were the main focus of the retrofit. Compared to the use of single-glazed windows, the application of double-glazed windows in the sunspace offers a certain energy-saving benefit. The original 5 + 12A + 5 exterior windows and 5 mm interior windows did not require any modifications. After retrofitting the exterior walls, roof, and sunspace with double-glazed windows, the overall energy-saving potential reached 81.31%, demonstrating the significant potential for energy-saving retrofits in traditional rural houses in this region.

3.3. Comparison of the EC with and Without a Sunspace

Using Building 5, this research explored the impact of having a sunspace on the building’s EC, both before and after the retrofit. Two scenarios from Table 8, Plan 1 (no sunspace) and Plan 2 (with a sunspace), were selected for a comparative analysis. The comparison models are shown in Figure 7, with the simulation results presented in

Table 9. EC_pua analysis of the comparison models.

Plan 1
	With a Sunspace (Original House) (kW·h/m2)	Without a Sunspace (kW·h/m2)	Energy Savings Rate (%)
East bedroom	133.12	122.54	−7.95
Living room	85.88	71.44	−16.81
West bedroom	116.84	132.7	11.95
Middle bedroom	86.6	63.45	−26.73
ECpua	104.17	95.02	−8.78
Plan 2
	With a Sunspace (Original House) (kW·h/m2)	Without a Sunspace (kW·h/m2)	Energy Savings Rate (%)
East bedroom	30.58	28.16	−7.91
Living room	10.25	32.57	68.53
West bedroom	25.44	34.22	25.66
Middle bedroom	10.66	30.33	64.85
ECpua	18.56	31.28	40.66

and Figure 8.

The observations reveal that the impact of the sunspace on the EC produced opposite results before and after the retrofit. Due to the poor thermal performance of the original rural house envelope, adding a sunspace actually increased the EC of the rooms on the south façade that were enveloped by the sunspace. However, the adjacent west bedroom saw a significant reduction in the EC because part of the external wall was converted into an internal wall.

In the retrofitted scenarios, the sunspace proved to bring significant energy savings, especially for the rooms on the south façade enveloped by the sunspace, where the energy savings rate could reach over 60%. The next highest energy savings were found in the rooms adjacent to the sunspace. However, it is important to note that the addition of a sunspace could block sunlight for certain rooms, as observed in the east bedroom, where the energy savings rate decreased. It may be considered to extend the existing sunspace to include the south façade of the east bedroom to mitigate this issue. Overall, it can be concluded that a sunspace should only be added when the thermal performance of the envelope structure meets the required standards.

3.4. Depth of the Sunspace and Shading Types

In the model established in Section 3.2, a sunspace was added to the south side of the rectangular building, forming the model shown in Figure 9. The thermal parameters from Retrofit Plan 2, derived in Section 3.3, were applied. Considering the actual dimensions of the sunspace, the depth of the sunspace was set to range from 0.9 m to 3 m. The impact of the sunspace depth on EC_pua is shown in Figure 10. From the EC_pua values, it can be seen that the depth of the sunspace had little effect on EC_pua. The model with a 1.2 m deep sunspace exhibited the lowest EC_pua. The depth of the sunspace can be adjusted based on the user’s specific needs and the overall form of the rural house.

Field investigations revealed that some sunspaces serve multiple functions, such as dining rooms, living rooms, bedrooms, and recreational spaces. However, during the summer, overheating is a common issue in these sunspaces. Local residents often use simple internal shading devices, such as blinds on the top of the sunspace, to reduce direct sunlight. Although direct radiation on the occupants is effectively reduced, significant temperature fluctuations still occur throughout the day, with particularly intense heat felt from midday to the afternoon.

The research explored the use of external shading as an alternative to traditional internal shading. Compared to internal shading, external shading offers clear advantages in terms of energy efficiency and indoor comfort. First, external shading devices can block most of the heat before it enters the window, significantly reducing indoor heat gain, lowering air conditioning cooling loads, saving energy, and cutting operational costs. Secondly, they help protect windows from thermal stress, extending their lifespan. Additionally, external shading reduces indoor glare, improves light quality, and effectively blocks ultraviolet rays, protecting furniture and decor from fading. Considering both the esthetics and functionality of the building, external shading generally provides a better overall solution.

The research used a rectangular model with a 1.2 m deep sunspace as an example and proposed a comprehensive external shading strategy using panels and blinds on the exterior of the sunspace’s glass. A panel shading system was installed on the top of the sunspace, and blinds were applied to the vertical windows facing each direction. Due to the spacing between the shading components and the window, panels extended beyond the edges of the windows, providing full shading for the sunspace. The proposed external shading system operated adaptively: fully deployed in the summer to block solar heat gain and completely retracted in the winter to maximize passive solar heating. Blinds allow angle adjustments for precise solar control in the summer, while all components fold flush against the building’s facade during winter months. The rural house with the added shading is shown in Figure 11.

To better illustrate the impact of the internal thermal environment of the sunspace on occupants, the research focused on the T_ao as the key outcome. The effects of different shading methods on the T_ao inside the sunspace during the summer solstice are shown in Figure 12. Here, comprehensive shading refers to the comprehensive use of the mentioned shading methods.

Compared to a sunspace without shading, the addition of a top shading panel significantly reduced the sunspace’s daytime T_ao, confirming that solar heat gain through the skylight is the main cause of excessive temperatures in the sunspace during the summer. The installation of vertical blinds further lowered the indoor T_ao, with its effect second only to the top shading panel. In contrast, the angle of the blinds had little effect on the sunspace’s T_ao.

Without shading, the T_ao inside the sunspace reached its highest value of 54.79 °C at 3:00 P.M. After the top shading panel was installed, the maximum T_ao at 4:00 P.M. was reduced to 33.19 °C. When vertical blinds were used, the highest T_ao in the sunspace occurred at 3:00 PM, with the temperature varying between 45.84 °C and 48.03 °C depending on the angle of the blinds. When both shading methods were applied in combination, the maximum T_ao occurred between 4:00 P.M. and 6:00 P.M., with values ranging from 22.02 °C to 25.78 °C, depending on the angle of the blinds.

To verify the effectiveness of various shading methods in improving the thermal comfort of the sunspace during the summer, the research used the adaptive predicted mean vote (APMV) concept, proposed by the Evaluation Standard for Indoor Thermal Environment in Civil Buildings [41].

APMV = PMV/(1 + λ·PMV)

(1)

where APMV—adaptive predicted mean vote, λ—adaptive coefficient, and PMV—predicted mean vote.

It is considered that the indoor environment is more comfortable when the summer APMV of the sunspace is less than 1. The results are shown in Figure 13. This confirms again that the top shading panel and vertical blinds are the primary shading components for improving thermal comfort. After applying the combined shading methods, the T_ao during the hottest period of the sunspace ranged from 18 °C to 26 °C, achieving thermal comfort throughout the day. Therefore, the proposed shading solution effectively addresses the overheating issue of the sunspace in the summer.

3.5. Building Orientation, Room Location, and House Type

Field research found that in the four villages surveyed, the deviation of the rural house orientation from due south was within 20°. To determine the most energy-efficient orientation, the research used the parameters from Retrofit Plan 11 in Section 3.2 as the basis. Five house types from the four villages were selected as simulation samples. The orientation was set with due south as 0°, with negative values for westward deviations and positive values for eastward deviations. The research investigated a range of orientations from −30° to 30°.

As the building orientation changes, EC_pua for each rural house is shown in Figure 14. The actual orientations for building 1–5 are southeast 12°, southeast 12°, southwest 20°, southwest 2°, and southwest 11°, respectively. The optimal orientations for these rural houses are southeast 7°, due south, southwest 2°, southwest 3°, and southeast 8°. Comparing the actual orientations to the optimal orientations, it is found that building 2, building 3, and building 5 deviate significantly from the optimal orientation. In the future, during the relocation process, these house types can be oriented according to the optimal directions.

Further analysis was conducted on T_ao and EC_pua for each room in the different house types. The analysis of T_ao was conducted assuming that heating facilities were not turned on, while the EC analysis was based on the assumption that heating systems were in use. The modeling information for each house type, the T_ao of each room on the winter solstice, and the EC_pua of each room are shown in Figure 15, Figure 16, Figure 17, Figure 18 and Figure 19.

The EC_pua for the rural houses in Building 1–5 was as follows: 14.22 kW·h/(m²·a), 10.98 kW·h/(m²·a), 19.3 kW·h/(m²·a), 26.43 kW·h/(m²·a), and 19.46 kW·h/(m²·a), respectively. Among these, Building 4 had the highest EC_pua, while Buildings 1–2 were significantly more energy-efficient compared to the other house types. This indicates that the residents’ adjustments to the house types indeed resulted in significant energy savings.

In the case of two-story buildings, the EC_pua of the rooms on the first floor was significantly lower than that of the corresponding rooms on the second floor. Compared to the T_ao in the sunspace, rooms on the south-facing façade, which are enclosed by the sunspace, generally had a higher T_ao. Rooms in the middle positions tended to have lower EC_pua and higher temperatures than those on the sides of the building.

In Buildings 3–4, the rooms in the northeast corner required significantly more EC_pua than other rooms. Even on the winter solstice, some rooms may experience discomfort due to high temperatures. Among these, the sunspace exhibited the greatest variation in T_ao. The sunspace’s T_ao exceeded 26 °C between 11:00 AM and 2:00 PM, with the peak temperature reaching 50 °C at 1:00 PM. From 8:00 AM to 1:00 PM, the indoor T_ao increased significantly, while between 1:00 PM and 7:00 PM, the temperature gradually decreased. Outside of these hours, the T_ao fluctuated less and hovered around 10 °C.

3.6. Innovation and Contribution

(1) Dynamic validation framework for climate-responsive retrofits:

This study developed a dual-benchmark evaluation system integrating national energy standards and localized climate adaptation objectives. By analyzing pre- and post-retrofit thermal performance, it proposed a phased retrofit strategy prioritizing passive solar design, envelope upgrades, and renewable integration, bridging standardized compliance with localized climatic needs.

(2) Dual-season optimization of sunspaces:

The research established quantitative relationships between sunspace configurations (depth, shading) and the annual EC. It resolves the conflict between winter heating efficiency and summer overheating prevention, advancing sunspace design from qualitative guidelines to climate-adaptive quantitative solutions.

(3) Multidimensional assessment model for rural settlements:

A systematic framework evaluates interactions among building orientation, spatial layout, and courtyard morphology. It addresses the tension between traditional vernacular patterns and modern energy efficiency, providing design principles that balance cultural continuity with technical feasibility for high-altitude cold regions.

These innovations collectively enhance theoretical and methodological approaches to energy-efficient design in extreme climates, offering scalable strategies for performance optimization and sustainable rural housing development.

3.7. Limitations

This study established thermal optimization principles through heat transfer analysis but did not evaluate material-specific implementation aspects—including availability, cost-effectiveness, and sustainability trade-offs—due to article length and workload constraints.

The proposed strategies may encounter practical constraints involving land resources, pre-existing spatial configurations, and multi-stakeholder priorities. While our analysis identifies energy efficiency opportunities through field-derived models, real-world implementation requires balancing these with broader sociotechnical considerations.

4. Conclusions

The research, using five typical rural houses from four villages in Qinghai as case studies, explored the impact mechanisms of solar energy and building’s EC on the construction of migrant resettlement housing in Qinghai. The research addressed several key issues identified during field investigations from a green building perspective. The specific conclusions are as follows:

(1) The thermal performance of the existing envelope structures of rural houses is generally poor, with significant potential for energy-saving renovation. The walls and roofs are the primary focus for energy-efficient upgrades, and the improvement of window energy performance is also important. It is recommended to select materials with better insulation properties or increase the thickness of the existing materials to ensure that the heat transfer coefficient of the walls and roofs meets the required standards. Except for replacing the single-glazed windows in the sunspace with double-glazed windows, no changes are necessary for other windows. After renovation, the energy-saving rate of the rural houses can reach approximately 80%.

(2) The addition of a sunspace can significantly improve the energy performance of rooms on the south-facing façade that are enclosed by the sunspace. Rooms with external walls wrapped by the sunspace also benefit from some energy savings. However, it is important to note that the installation of the sunspace may reduce direct daylight in some rooms. Therefore, careful consideration should be given to how the sunspace is integrated with the main building to optimize both energy efficiency and natural lighting.

(3) The depth of the sunspace has a relatively small impact on the EC. This suggests that the energy savings from the sunspace are more influenced by its design and orientation than by its depth.

(4) Top shading panels and vertical blinds can effectively solve the problem of overheating in the sunspace during the summer. These methods are more advantageous compared to the local practice of using internal shading curtains on the skylights. The angle of the vertical blinds has a limited effect on regulating the T_ao inside the sunspace, but it can serve as a fine-tuning element for balancing temperature and daylighting.

(5) The optimal orientation for different house types does not align consistently with due south, but all orientations deviate from due south by no more than 10°. This suggests that small deviations from the south-facing orientation can still result in energy-efficient designs, but the most energy-efficient orientation should be carefully considered for each specific house type.

(6) Building 2 is more energy-efficient due to the rational placement of functional rooms, the relationship between the upper and lower floor rooms, and the effective layout of the sunspace. This indicates that the renovation not only improved the esthetic quality of the homes, but also significantly enhanced their energy performance.

(7) Primary functional rooms where people are most likely to spend time should be located in the middle of the first floor on the south side, with the south-facing façade enclosed by the sunspace. Rooms located in the northeast and northwest corners, where people are less likely to stay, should be designated for auxiliary functions such as stairs, storage rooms, or bathrooms.

Future research priorities:

(1) Technology transferability: develop climate zoning adaptation factors to extend these design principles to other Qinghai–Tibet Plateau regions with varying solar regimes.

(2) Behavioral integration: investigate occupant interaction dynamics with sunspaces and shading systems through longitudinal post-occupancy evaluations.

(3) Policy synergies: explore incentive mechanisms linking energy-saving performance metrics to local carbon-trading frameworks.