Thermal Bridges Monitoring and Energy Optimization of Rural Residences in China’s Cold Regions

Abstract

With the worldwide dissemination of the “green development” concept and the advancement of China’s new rural construction, the sustainable development of rural residences has gained significant attention within the construction industry. This article focuses on large-scale prefabricated insulation block houses used in China’s cold regions, specifically examining the case of Defa Village in Nenjiang City, Heilongjiang Province. By utilizing thermal imaging cameras, the thermal bridge parts of these houses are detected, and a finite element model is established to optimize the comprehensive heat transfer coefficient of these areas. This optimization is achieved by expanding the insulation layer and implementing low thermal bridge structures, ultimately enhancing the insulation and energy-saving efficiency of the houses. Simultaneously, an energy-saving analysis is conducted based on an optimized enclosure structure scheme, considering seven key design factors that influence building energy consumption: span, depth, clear height, and window-to-wall ratio in all four directions. Through a comprehensive experimental method, the building energy consumption is evaluated, and a scheme with optimal values is proposed. The results demonstrate that the insulation block walls and the main structures with expanded insulation layers and low thermal bridge structures are easier to construct. When compared to the original scheme, the comprehensive heat transfer coefficient of the walls is reduced by 54.82%, while that of the beams and columns is reduced by 97%. Implementing the optimal value scheme leads to a reduction of 66.83% in the building’s overall energy consumption. This research provides valuable guidance for the design and construction of large-scale insulated block rural residences, revealing the substantial potential of rural residences in terms of energy-saving and emission reduction.

1. Introduction

With the advancement of society and the growth of the economy, the principles of green and eco-friendly design, energy efficiency, and low-carbon construction have become crucial factors influencing architectural design. Prefabricated housing, characterized by its efficiency and high level of industrialization, has progressively assumed a significant role in the construction industry. In China, the rural population constitutes 40% of the total population, while rural residences account for 38% of the national construction area. Furthermore, rural housing contributes to 24% of the overall energy consumption in the country [1]. As the new rural development initiatives gain momentum, the demand for rural housing will continue to rise, necessitating more stringent energy-saving standards. Therefore, prefabricated rural housing faces unprecedented opportunities for development as well as new challenges.

This article delves into the thermal bridge optimization and energy optimization of large insulated block rural residences in the cold regions of China. Using the Northeast region as a case study is characterized by a climate with long and extremely cold winters, which poses significant challenges for architectural design. Analysis reveals that heat loss from the building envelope is substantial, accounting for over 50% of the total heat loss. Thermal bridges, in particular, tend to be concentrated areas of heat loss. Therefore, enhancing the insulation performance of the building envelope and mitigating the impact of thermal bridges are essential strategies to achieve energy efficiency in construction. Presently, many rural dwellings in the Northeast have adopted simplistic construction techniques. These measures encompass the utilization of higher-quality insulation materials, double-glazed and energy-efficient windows, sloping roofs, and wall air gaps, as well as the application of polyurethane materials for sealing. These initiatives have effectively improved insulation performance and elevated heating levels [1]. Nevertheless, despite meeting indoor comfort requirements, they still result in significant energy resource consumption. As a result, this article focuses on analyzing insulation design for the building envelope and optimizing the energy source for heating systems.

Currently, there have been significant advancements in both theoretical and practical research regarding insulation and energy-saving in rural residences. These achievements primarily concentrate on two key areas:

The first aspect pertains to the investigation of thermal performance within building envelope structures. This involves the identification of thermal bridge areas within the components of the building envelope based on their specific construction forms. Furthermore, it entails conducting measurements and analyses to assess the thermal performance, heat loss, and effects of thermal bridging under specific environmental conditions. Ultimately, the aim is to enhance the construction forms to mitigate the impact of thermal bridging and minimize heat loss. For instance, Laura et al. [2] utilized numerical simulation methods to analyze the two-dimensional heat transfer characteristics at the junction of walls and floors, as well as the L-shaped corners within a building. Ascione [3] carried out measurements to evaluate temperature distribution and heat flow fields within masonry structures. Through the analysis of the obtained results, they were able to identify areas influenced by thermal bridging based on changes in temperature and heat flow. They also investigated the variations in temperature distribution and heat flow fields in masonry structures under two-dimensional transient conditions while considering different insulation thicknesses and mortar joint thicknesses. Baldinelli et al. [4,5] employed an enhancement algorithm to optimize thermal images of three thermal bridges present in a concrete structure. These optimized images were then validated against measured temperature fields, leading to the identification of the thermal bridge areas. Zalewski [6,7,8] analyzed steel structure walls, performing tests to observe the dynamic changes in heat flow and temperature at various positions along the walls. By comparing the results obtained from measurements and simulations, they assessed the impact of heat loss.

The second aspect involves the analysis and optimization of energy consumption levels for the entire building over a specific period. This encompasses calculating the heating load, cooling load, electrical usage, domestic hot water, and other energy consumption levels under specific climatic conditions. The primary objective is typically to achieve energy efficiency while ensuring comfort levels. The optimization design of the building focuses on architectural geometric parameters and the thermal performance of the building envelope. For instance, Mithraratne et al. [9] conducted a comprehensive analysis of a group of individual buildings in New Zealand, with a specific emphasis on energy consumption during the building maintenance phase. They compared the energy consumption costs of buildings with different envelope structures. Sartori et al. [10] examined 60 case studies from the literature and determined that operational energy constitutes the majority of the total energy demand throughout the building lifecycle. Fang et al. [11,12,13] investigated the relationship between design parameters and performance in buildings, including variables such as geometric shape, window and skylight sizes, and positions. They employed mathematical methods to explore the impact of these variables on daylighting performance and energy efficiency, ultimately proposing optimization strategies. G. Verbeeck [14] assessed the economic feasibility of five types of insulation measures, glass selection, and renewable energy in Belgian residential buildings. They proposed a hierarchical approach to energy-saving measures. Payyanapotta et al. [15] integrated lightweight building systems with passive house concepts to achieve energy efficiency and thermal comfort. They identified limitations in lightweight steel systems, encompassing issues such as overheating, airtightness, thermal bridging, moisture transfer, and solar control.

The design of building envelope structures and the optimization of overall energy consumption levels play a crucial role in achieving energy-saving and emission-reduction goals in buildings. However, existing research has predominantly focused on concrete and steel structures, with limited investigations into large-scale thermal insulation blocks. This study addresses this gap by conducting thorough tests and analyses on the thermal performance and energy consumption levels of large-scale thermal insulation block systems in severe cold climate environments. Our research aims to optimize the building envelope structures and architectural forms to achieve a comprehensive wall heat transfer coefficient of 0.24 W/(m²·K) and a comprehensive building energy consumption value of 43.8 kWh/(m²·a). The outcomes of this research can serve as a valuable reference for the design of large-scale thermal insulation block buildings in severe cold regions, facilitating the achievement of ultra-low energy consumption standards.

2. Materials and Methods

2.1. Project Overview

2.1.1. Site Selection and Layout

This study chooses a single-story rural residence constructed with large-scale thermal insulation blocks, situated in Defa Village, Nenjiang City, Heilongjiang Province, China (Figure 1), as the target for on-site measurements and analysis of energy consumption. The enhancements made to the thermal performance of the building envelope structure and the energy-saving optimization measures discussed in the paper are all derived from this rural residence serving as the reference model.

For testing and analysis, the selected benchmark building in this study exhibits characteristics of a small-scale and uncomplicated spatial composition. The residential exterior is unobstructed by any other buildings, and the surrounding environment is open. The layout of the residence follows the traditional “one bright, two dark, three open spaces” arrangement commonly seen in the northeast region. It comprises two bedrooms and one living room, all interconnected (Figure 2). The house has a depth of 5 m, a span of 10 m, an interior clear height of 2.8 m, and a total floor area of 50 square meters. The south-facing windows come in two sizes: 2200 mm × 700 mm and 600 mm × 1700 mm, resulting in a window-to-wall ratio of 0.34. The north-facing windows measure 1500 mm × 1600 mm, with a window-to-wall ratio of 0.26.

2.1.2. Materials of Walls

The building envelope structure of the benchmark building is composed of large-scale thermal insulation blocks. These blocks comprise expanded polystyrene foam boards (EPS boards), a protective layer of uniformly coated polymer insulation mortar, and a thermal bridge insulation layer made of extruded polystyrene foam boards (XPS boards). They are constructed as composite insulation modules with integrated construction grooves (Figure 3). The thermal insulation blocks have dimensions of 600 mm × 600 mm × 300 mm. The detailed construction sequence from the interior to the exterior includes a 10 mm layer of polymer insulation mortar, followed by a 230 mm layer of EPS, another 10 mm layer of polymer insulation mortar, and, finally, a 50 mm layer of XPS. The construction grooves surrounding the blocks have a depth of 30 mm. The exterior wall finish consists of a detailed construction sequence from the interior to the exterior: a 45 mm three-coat two-mesh system comprising 15 mm of phenolic board adhesive, mesh fabric, another 15 mm of phenolic board adhesive, additional mesh fabric, and a final 15 mm layer of cement mortar. This is followed by a 2 mm sealer and a 2 mm layer of exterior wall latex paint. The thermal parameters of the materials can be found in

Table 1. Thermal performance parameters of building materials.

Name	Density (kg/m3)	Thermal Conductivity (W/m·K)	Specific Heat Capacity (J/Kg·K)	Heat Storage Coefficient W/(m2·K)
XPS	30	0.03	4455	0.54
EPS	20	0.04	1380	0.29
Thermal insulation mortar	800	0.29	1050	4.44
Phenolic foam adhesive	350	0.07	1380	1.57
Cement mortar	1800	0.93	1050	11.37
C20	2300	1.51	920	15.24
C30	2420	1.62	970	15

.

2.1.3. Construction Methods

The thermal insulation blocks used in the benchmark building incorporate EPS and XPS as insulation core materials. These blocks are joined together using insulation mortar and feature 30 mm construction grooves. During block assembly, square holes measuring 60 mm by 230 mm are formed between the blocks. Horizontal and vertical HPB300 steel bars with a diameter of 6 mm are inserted within these square holes created via the module grooves. Subsequently, C20 concrete is poured to finalize the block assembly, resulting in a concrete mesh structure inside the wall that enhances its rigidity (Figure 4). Once the wall construction is complete, formwork is installed, and structural columns are cast at the L-shaped corners. Additionally, formwork is employed at the top of the wall to cast the ring beam (Figure 5). This construction method bears resemblance to traditional masonry techniques while incorporating rural construction practices and improved efficiency. It offers several advantages, including superior insulation performance, lightweight characteristics, and ease of construction.

2.2. Methods for Environmental Analysis

Before conducting tests and analyses on building envelope structures and optimizing building energy consumption, it is essential to assess the climate and environmental conditions at the location of the reference building. This study focuses on Nenjiang County in Heihe City, Heilongjiang Province, situated in the northeastern region of China, which falls within the severe cold climate zone. The analysis was based on globally representative meteorological year data (specifically for the Nenjiang region) provided by the National Renewable Energy Laboratory of the U.S. Department of Energy. Using the meteorological data analysis model developed on the Grasshopper platform (Figure 6), statistical analysis methods were employed to examine the annual temperature, humidity, and wind direction, as well as the time range that satisfies the comfort requirements for annual temperature, humidity, and wind speed in Nenjiang City. This analysis allowed for the extraction of the fundamental climate characteristics of the region and the calculation of boundary conditions, facilitating a preliminary summary of sustainable measures that can be incorporated into the architectural design of the region.

2.3. On-Site Methods for Identifying Thermal Bridges in Building Envelope Structures

Given the presence of residential occupants in the building being examined, this study utilizes the infrared thermography method to identify thermal bridges and capture the surface temperatures of the walls (Figure 7). These measurements are conducted in a manner that does not disturb the residents’ daily routines.

A thermal bridge refers to an area within a building where thermal insulation performance is weakened, often caused by the uneven thermal conductivity of materials or insulation defects. Thermal bridges can lead to energy loss and waste, thereby diminishing the overall energy efficiency of the building.

The FLIR-E6-XT infrared thermal imager was utilized for data collection in this study. Data were gathered from the northwest wall of a reference building situated in Defa Village, Nenjiang City, Heilongjiang Province, China. The testing was conducted on 22 December 2020 at 11:35 a.m. At that time, the outdoor temperature was recorded as −13.0 °C, with a humidity level of 23.0% and wind speed ranging from 0.3 to 1.5 m/s. Inside the testing room, the temperature measured was 22.4 °C, with a humidity level of 49.3% and an indoor wind speed of 0.1 m/s. The captured thermal images were directly stored in the thermal imager and later analyzed and temperature-extracted using the accompanying FLIR-E6-XT-Tools software (version 6.0, FLIR Systems, Inc., Wilsonville, OR, USA).

2.4. Numerical Simulation Methods for Evaluating the Thermal Performance of Building Envelope Structures

Based on the results of climate analysis and thermal bridge identification in building envelope structures, the boundary conditions and analysis models for finite element analysis of building thermal bridges are determined. This study aims to utilize steady-state heat transfer calculation methods and utilize ABAQUS-6.14 finite element software to analyze and compare the heat flow fields of building thermal bridges at various locations and with different constructions. The finite element analysis was performed using ABAQUS/Standard (version 6.14, SIMULIA, Dassault Systèmes, Vélizy-Villacoublay, France).

The calculation of heat flux density follows the principles of steady-state heat transfer in solids, assuming a temperature difference between the two sides of the building wall, where heat transfers from higher to lower temperatures. This process encompasses heat conduction, convective heat transfer, and radiative heat transfer. According to the second law of thermodynamics, the heat flow passing through the wall is equal to the heat flow passing through the surface, which can be expressed as the following equation:

q = −λ·[(∂t)/(∂n)] = (t_e − θ_e )·(α_c + α_r),

(1)

q is heat flux (W/m²); λ is thermal conductivity (W/m·K); α_c is convective heat transfer coefficient (W/m²·K); α_r is radiative heat transfer coefficient (W/m²·K).

Given that the analyzed objects consist of complex structures with non-homogeneous and multi-layered configurations, it is essential to consider the impact of thermal bridge areas and conduct a quantitative evaluation of the overall thermal insulation performance of the components. To achieve this, the method outlined in the “Code for Thermal Design of Civil Buildings” (GB50176-2016) [16] will be employed to calculate the thermal resistance of the building envelope structures. By extracting the average heat flux density from the outer surface of the analysis model, the overall thermal resistance of the structure will be determined, enabling the subsequent calculation of the overall heat transfer coefficient. The thermal insulation performance of the analyzed objects will then be assessed and compared based on the overall heat transfer coefficient. The indoor calculation temperature is set at 18 °C, while the outdoor calculation temperature is set at −25 °C.

R = [(ΔT)/q_w] − R_i − R_e

(2)

K = 1/R

(3)

R is overall thermal resistance of the structure (m²·K/W); K is overall heat transfer coefficient of the structure (W/m²·K); ΔT is indoor–outdoor temperature difference (K); q_w is average heat flux density (W/m²); R_i is internal surface convective resistance [0.11 (m²·K/W)]; R_e is external surface convective resistance [0.04 (m²·K/W)].

2.5. Building Energy Analysis and Energy-Saving Optimization

2.5.1. Comprehensive Calculation of Building Energy Consumption

In this study, the comprehensive building energy consumption of the target building is simulated and calculated using the DesignBuilder-6.1 software (version 6.1, Designbuilder Software Limited, Gloucestershire, UK). The term “comprehensive building energy consumption” refers to the total energy consumed by the building over a specific period, usually one year, encompassing heating, cooling, electricity, and fuel consumption. DesignBuilder is a specialized building energy simulation software that employs EnergyPlus as its computational engine and provides a wide range of energy simulation calculation modules. To facilitate the comparison of energy performance among different scenarios, the annual specific energy consumption (kwh/m²·a) is adopted as the benchmark for comparison.

2.5.2. Single-Parameter Analysis of Building Energy Consumption

After conducting optimization of the building envelope structure, this study examines the geometric design factors that impact building energy consumption. These factors encompass building orientation, window-to-wall ratios in the four primary directions, building aspect ratio, depth, net height, and other relevant design considerations. By employing single-parameter analysis, this study investigates the correlation between these design factors and building energy consumption. Specifically, the research focuses on examining the influence of design parameters on the building’s winter heating load, taking into account solar radiation gains. The goal is to explore the relationship between design parameters and energy consumption and establish an initial range of optimal variable values for each parameter, considering their individual effects. The initial value for each design parameter can be found in

Table 2. Range of design parameter values.

Parameter Name	Range of Parameter Values
Building Orientation	0~3.1 rad
Window-to-Wall Ratio	0~0.9
Building Depth	6~20 m
Building Span
Building Height	2.8~4.4 m

.

This study utilizes the integrated Grasshopper platform (Figure 8) for the parametric analysis process. Unlike the comprehensive calculation of building energy consumption, the primary focus of the parametric analysis is to investigate the relationship between design elements and design objectives. Consequently, the residential building is simplified into a box model, where the roof, walls, floor, and glass are identified as vital components of the building envelope system. The material properties of these components are defined based on real-world conditions. The windows are chosen with a heat transfer coefficient of 2 W/m²·K, utilizing triple glazing. For insulation, the roof incorporates lightweight composite gray limestone panels with a thickness of 150 mm and a thermal conductivity of 0.022 W/m·K. The floor is composed of a 100 mm crushed stone concrete layer and a 100 mm sand bedding layer. The occupancy rate, calculation temperature, and equipment efficiency are set to ideal values, while the indoor temperature is constrained within the range of 18 °C to 25 °C. The equipment’s coefficient of performance (COP) is set to 1, and the operation time is 24 h.

2.5.3. Analysis of Building Energy Consumption Sensitivity and Optimization of Energy-Saving Measures

Based on the results of the single-parameter analysis, the value ranges for the key design parameters are determined, and the subset with lower energy consumption levels under each factor is chosen as the range of values. These key parameters include window-to-wall ratios in the four cardinal directions (N/W/S/E), as well as the length (X), width (Y), and height (Z) of the building envelope structure (

Table 3. Design parameter value range.

Name	X/m	Y/m	Z/m	N	W	S	E
Range	7–10	6–9	3–4	0–0.2	0–0.3	0.3–0.6	0–0.3

).

A computational model is established using the Grasshopper analysis platform to calculate the cooling load, heating load, total load, and solar gains (Figure 9) for the target building. Comprehensive simulation calculations are conducted for all possible combinations of the seven variables, and the results are recorded in the Design-Explorer visualization platform.

Once all simulation calculations are completed, this study will produce operational scenarios for all variables and various combinations of values. The Pearson correlation analysis method will be used to examine the correlation between each variable, including building aspect ratio, depth, net height, and window-to-wall ratios in the four cardinal directions, and each calculation objective, including cooling load, heating load, total load, and solar heat gain. This analysis aims to provide a comprehensive assessment of the strength and variability of the influence of different design elements on building energy consumption.

The Pearson correlation analysis method employed in this study is based on the Pearson correlation coefficient, which is a statistical measure that quantifies the strength and direction of the relationship between two variables. Correlation analysis utilizes covariance to evaluate the overall relationship between the variables and determine if they demonstrate synchronous changes, indicating whether they increase or decrease simultaneously. To mitigate the impact of units, the covariance is standardized, resulting in the correlation coefficient. This standardization enables comparisons and interpretations of the correlation independent of the specific units used.

The Pearson correlation coefficient is a widely used statistical measure for evaluating the strength and direction of the relationship between two variables. It falls within the range of −1 to +1, with the following interpretations:

• A coefficient close to +1 indicates a strong positive correlation, suggesting that the two variables change in the same direction, either increasing or decreasing simultaneously;
• A coefficient close to 0 signifies no correlation, indicating that there is no apparent relationship between the two variables;
• A coefficient close to −1 signifies a strong negative correlation, indicating that when one variable increases, the other variable decreases.

Subsequently, the correlation between different design elements and building energy consumption is assessed and ranked. Among all the operational scenarios, the optimal design solution is chosen based on the objective of achieving the lowest energy consumption.

3. Results

3.1. Climate and Environmental Characteristics

The Nenjiang region experiences a maximum annual temperature of 36 °C, with the lowest temperature dropping to −38.4 °C (Figure 10). In the driest period of winter, the relative humidity reaches a low of 6%. However, for the majority of the time, the relative humidity stays around 65% (Figure 11). Additionally, the maximum wind speed during winter can reach 15 m/s (Figure 12).

An analysis of the climate comfort in the Nenjiang region reveals significant findings. The recommended temperature range for human comfort is between 15 °C and 25 °C, while the optimal range for relative humidity falls between 45% and 65%. Wind speed is considered comfortable below 5 m/s. The results of this analysis have been presented visually and summarized in

Table 4. Outdoor environmental comfort time analysis table for Nenjiang City.

	Min-T	Max-T	Comfort	Min-H	Max-H	Comfort	Min-Wind	Max-Wind	Comfort	Overall Comfort
All year	−38.4 °C	36 °C	22.4%	6%	100%	26.2%	0 m/s	12 m/s	59.9%	3.2%
Winter	−38.4 °C	2.2 °C	0%	26%	96%	31.4%	0 m/s	12 m/s	28.9%	0%
Summer	4.3 °C	36 °C	76.3%	19%	100%	20.6%	0 m/s	12 m/s	29.8%	74.9%

. It is noteworthy that the time spent in outdoor temperatures within the comfort range accounts for only 22.4% of the entire year, with no comfort time during the winter season (Figure 13). The duration within the comfortable range for relative humidity amounts to 26.2% of the year (Figure 14). Moreover, the period spent within the comfortable range for wind speed constitutes 59.9% of the year (Figure 15). Interestingly, the overlap of temperature, humidity, and wind speed falling within the comfortable ranges is merely 3.2% of the entire year, with no comfort time during the winter season.

The data indicate that the region’s uncomfortable environment is primarily due to the combination of low temperatures and strong winds experienced during winter. Therefore, ensuring high-quality insulation in housing is a critical factor to be taken into account during architectural design to mitigate these challenges. Furthermore, considering the region’s favorable climate conditions in summer, it is advisable to minimize the reliance on cooling equipment and instead promote the utilization of natural ventilation from the southwest and northeast directions as an effective means to achieve optimal comfort levels.

3.2. Thermal Bridge Identification in Buildings

During the test, the outdoor temperature was −13 °C, and the indoor temperature was 21 °C. To mitigate the impact of solar radiation, the test was conducted at the northwest corner of the building.

The thermal imaging conducted at the northwest corner provides a clear visualization of the temperature distribution within the interior wall of the building envelope (Figure 16), offering insights into the wall composition and its relationship with the main structure. The analysis of the results reveals that the insulation blocks exhibit a main body temperature of approximately 12 °C, while the temperature at the joints of the blocks is around 7.6 °C. Furthermore, the beam positions exhibit temperatures of approximately 6.8 °C, the column positions record temperatures of around 5.2 °C, and the intersection of beams and columns shows temperatures of approximately 3.7 °C. These findings highlight significant temperature variations among different structural positions, with lower temperatures indicating higher heat loss and inferior insulation performance. Moreover, larger temperature differentials indicate greater disparities in insulation effectiveness across various locations. Even in walls with an overall higher insulation performance, notable temperature differences persist across different positions (Figure 17). When considering the interior as a whole, the maximum temperature difference on the inner wall can reach up to 11.3 °C, underscoring the non-uniformity of the building envelope’s thermal performance. This non-uniformity directs the flow of heat towards areas with lower insulation performance, exacerbating heat loss and leading to the formation of thermal bridges.

Based on the aforementioned analysis, it can be deduced that the benchmark building exhibits three primary types of thermal bridges: the joint thermal bridge generated via the concrete sections between blocks (Figure 18a), the thermal bridge caused by concrete columns (Figure 18b), and the thermal bridge resulting from concrete beams (Figure 18c). The temperature differential between the surfaces of these three types of thermal bridges and the building envelope ranges from 4 °C to 11.3 °C. By creating structural models at the locations of the thermal bridges, it can be concluded that the primary cause of their formation is the considerably lower thermal insulation performance of concrete in comparison to the insulation materials employed in the benchmark building, in addition to the substantial volume of concrete used.

3.3. Analysis and Optimization of Building Thermal Bridges

Calculation analysis was performed on the three thermal bridges of the benchmark building, and the comprehensive heat transfer coefficient was determined using the calculation method outlined in Section 2.4 (

Table 5. The comprehensive heat transfer coefficients of various thermal bridge sections.

Name	Wall	Column	Beam
Value-W/(m2·K)	0.54	10.24	7.25

). The column position exhibits the poorest thermal insulation performance, whereas the beam position shows better insulation performance compared to the column position. The wall section demonstrates the highest insulation performance, aligning with the findings from the thermal imaging analysis. Notably, there is a significant disparity in heat loss between the joint of the blocks and the main body of the blocks, indicating a considerable loss of heat energy on a different scale (Figure 19a). At the column position, a substantial heat loss is observed compared to the surrounding building envelope structure (Figure 19b). Conversely, in the case of the beam position, the presence of a single concrete beam facilitates the smooth dissipation of relatively large heat flow through the beam (Figure 19c).

To further enhance the insulation performance, structural modifications have been implemented for the three types of thermal bridges.

To mitigate the thermal bridge problem at the block joints, structural enhancements have been introduced during the block assembly phase. By incorporating XPS insulation strips and utilizing spray polyurethane foam for sealing, heat loss has been effectively reduced. For the horizontal joints of the walls, XPS insulation strips measuring 600 mm × 40 mm × 20 mm have been implemented (Figure 20a). Likewise, for the vertical joints of the walls, XPS insulation strips measuring 640 mm × 40 mm × 20 mm have been employed (Figure 20b).

To address the thermal bridge issue at the beam position, several methods have been implemented. Firstly, galvanized steel pipes with a thickness of 1.5 mm and a cross-section of 20 mm × 40 mm have been utilized to replace the longitudinal reinforcement of the beam (Figure 21a). This substitution helps minimize heat transfer. Secondly, XPS boards with a uniform application of 75 mm thick magnesium oxychloride cement on the outer surface have been employed as removable formwork (Figure 21b). These XPS boards act as insulation and provide support during the construction process. They are firmly secured on both sides of the beam reinforcement cage using self-tapping screws. To further enhance the insulation performance, XPS blocks measuring 40 mm × 40 mm × 20 mm have been strategically placed at the anchor points of the self-tapping screws (Figure 22). This additional insulation layer helps reduce heat loss. Finally, concrete is directly poured between the removable formwork, ensuring proper structural integrity and achieving the desired shape.

To mitigate the thermal bridge problem at the column position, we employ galvanized steel pipes with matching specifications as alternatives to the longitudinal reinforcement of the columns. Taking a corner column as an illustration, we utilize removable formwork and insulation blocks of identical specifications. These components are firmly attached to the outer side of the column reinforcement cage using self-tapping screws (Figure 23). Ultimately, concrete is poured directly between the removable formwork to attain the desired structural configuration.

Following the aforementioned structural improvements, detailed construction drawings were generated for the block joints, column positions, and beam positions, incorporating the design principles of low thermal bridges (Figure 24).

By conducting a calculation analysis on the improved thermal bridges of the three types and utilizing the calculation method outlined in Section 2.4, the comprehensive heat transfer coefficients (

Table 6. The optimized overall heat transfer coefficients have been obtained.

Name	Wall	Column	Beam
Value-W/(m2·K)	0.24	0.27	0.20

) were determined. The results demonstrate a significant reduction in the overall heat transfer coefficients, indicating a substantial enhancement in insulation performance following the implementation of measures to mitigate thermal bridges. Specifically, the overall thermal resistance of the walls increased by a factor of 2.25 compared to the original design, while the thermal resistance at column positions increased by a factor of 38 and at beam positions by a factor of 36. In comparison to the original design, the overall heat transfer coefficient of the walls decreased by 54.82%, and the heat transfer coefficient at beam and column positions decreased by 97%. These improvements align with the requirements specified in the “General Specification for Building Energy Efficiency and Renewable Energy Utilization” (GB55015-2021) [17].

Based on the thermal flow map, it is apparent that there has been a substantial reduction in heat loss at the three types of thermal bridges (Figure 25). Thermal imaging tests were carried out on a residential building in Nenjiang, which was constructed with the implemented improvement measures (Figure 26). A comparison with the reference building showed that the wall surface temperatures in the original thermal bridge areas have become more uniform, resulting in a significant decrease in temperature differentials. These findings indicate excellent insulation performance.

3.4. Single-Parameter Analysis of Building Energy Consumption

A comparative analysis was conducted to evaluate the solar radiation received on the surfaces of residential blocks with improved insulation in the Nenjiang region, taking into account different orientations (Figure 27a). The vertical axis represents the amount of winter/summer radiation, while the horizontal axis denotes the angle between the normal of the south-facing facade and the north–south axis (with a clockwise direction as positive). It is hypothesized that positioning the building in a north–south orientation would be advantageous if it leads to higher radiation during winter and lower radiation during summer (Figure 27b).

Based on the initial thermal imaging analysis, it is apparent that the transparent envelope structure often exhibits weaker thermal insulation characteristics. A comparison was conducted to assess the impact of different window-to-wall ratios (south, west, east, north) on winter heating loads (Figure 28). The findings indicate that the south-facing facade achieves the lowest specific heating energy consumption per unit area when the window-to-wall ratio is 0.3. In cases where the window-to-wall ratio is less than 0.5, the energy consumption increment is negative compared to having no windows. On the other hand, for the remaining three orientations, the winter heating energy consumption demonstrates a nearly linear increase as the window-to-wall ratio rises. Specifically, opening windows on the north-facing side exerts a greater influence on energy consumption compared to the east and west sides. It is noteworthy that when the window-to-wall ratio is below 0.25 for the west-facing facade, 0.2 for the east-facing facade, and 0.1 for the north-facing facade, the incremental energy consumption remains below 5% in comparison to having no windows.

Finally, a study was conducted to examine the impact of length, width, and height on heating load per unit volume by maintaining a fixed window-to-wall ratio of 0.5 on the south side and 0.1 on the north side (Figure 29). The results revealed a consistent decrease in heating load per unit volume as the dimensions of the building envelope increased. This reduction can be attributed to the exceptional thermal insulation performance of the structure, which effectively limits the increase in energy consumption relative to the expansion in volume. In the absence of windows, both the building’s depth and span exhibited similar effects on energy consumption. However, in the presence of windows, an increase in the building’s span resulted in a larger external window area and increased solar heat gain, thereby causing a slower decline in energy consumption compared to an increase in depth. Conversely, an increase in building height consistently led to a decrease in energy consumption, albeit at a slower rate than the scenario without windows. This can be attributed to the fact that an increase in height also enlarges the external window area, contributing to additional solar heat gain and moderating the rate of decrease in energy consumption. Overall, in terms of the influence on heating energy consumption per unit volume, the order of significance is height > depth > span.

3.5. Sensitivity Analysis of Building Energy Consumption and Optimization for Energy Savings

Based on the results, it is clear that p < 0.01 signifies a significant correlation between each variable and the analysis objectives. The absolute values presented in

Table 7. Pearson correlation analysis (** p < 0.01).

	Cooling (kwh/m2)	Heating (kwh/m2)	All Energy (kwh/m2)	Solar (kwh/m2)
X	−0.203 **	−0.317 **	−0.303 **	−0.122 **
Y	−0.351 **	−0.441 **	−0.435 **	−0.398 **
Z	0.426 **	0.629 **	0.606 **	0.410 **
N	0.100 **	0.293 **	0.265 **	0.107 **
W	0.618 **	0.299 **	0.365 **	0.389 **
S	0.449 **	0.178 **	0.233 **	0.620 **
E	0.132 **	0.273 **	0.253 **	0.292 **

reflect the strength of the correlation, where larger absolute values indicate a stronger correlation. The building’s span and depth exhibit a negative correlation with the analysis objectives, whereas the remaining design parameters show positive correlations. When the objective is winter heating, the influence of building scale surpasses that of the window-to-wall ratio. Notably, building height exerts the most significant impact, followed by depth (which outweighs span). The effect of the window-to-wall ratio on the south-facing facade is relatively weak, as it is compensated via solar radiation for heating purposes. The impacts of other factors are relatively similar. In terms of solar radiation heat gain, the opening of windows on the south side yields the greatest influence, while building height also plays a substantial role due to its ability to widen the incidence angle. Regarding summer cooling, the presence of west-facing windows has a pronounced effect, indirectly highlighting the importance of addressing the impact of western sun exposure during this season.

We analyzed the relationship between the window-to-wall ratio and both winter heating load and solar radiation heat gain for the four distinct orientations while maintaining a constant building scale (

Table 8. Pearson correlation analysis (* p < 0.05, ** p < 0.01).

	Heating (kwh/m2)	Solar (kwh/m2)
N	0.796 **	0.061 **
W	0.404 **	0.174 **
S	0.025 *	0.965 **
E	0.425 **	0.163 **

). The focused parameters employed in this analysis provide a more comprehensive understanding of the disparities in window placement compared to the previous round. Notably, there is a strong correlation between south-facing window openings and solar radiation heat gain, suggesting that variations in the window-to-wall ratio have minimal influence on winter heating. Conversely, the north-facing window openings, which receive minimal sunlight, exert the greatest impact on the heating load among the orientations, while the effects on the heating load for the east and west orientations are similar.

Based on the aforementioned analysis, clear directions can be identified for selecting optimization strategies. In cold regions, the primary focus should be on winter heating and solar radiation heat gain. Firstly, it is crucial to control the building height to prevent excessive height while also considering an appropriate increase in the building span. Secondly, the window-to-wall ratio on the south side can be moderately increased, but strict control is necessary for window openings on the north side. Careful consideration should be given to the design of window openings on the west side to mitigate heat gain during summer.

Consequently, a selection of optimal solutions was made among the 9216 simulated cases (Figure 30) that exhibited lower energy loads. After careful comparison, it was determined that a design with a length of 10 m, width of 9 m, height of 3 m, a window-to-wall ratio of 0.4 on the south side, and a window-to-wall ratio of 0.1 on the north or east side is more favorable (

Table 9. Refined optimal solutions.

X	Y	Z	N	W	S	E	Energy (kwh/m2)	Solar (kwh/m2)
10	7	3	0	0	0.3	0	168	86
10	9	3	0	0	0.4	0	162	89
10	9	3	0.1	0	0.4	0	166	94
10	8	3	0	0	0.3	0.1	167	86
10	9	3	0	0	0.4	0.1	165	100

Note: The bold number is the recommended value for the optimal solution.

). There are two groups of optimization schemes. (Figure 31) This optimized solution, with an energy load of 165 (kWh/m²·a), achieved a significant 66.83% reduction compared to the original design’s energy load of 497.38 (kWh/m²·a).

It is important to note that the simulation calculations focused on optimizing building load and did not consider the presence of partition walls within the building. Additionally, the equipment was assumed to operate 24 h a day, which may have slightly overestimated the data. Nevertheless, the results still provide valuable insights into the impact of various design elements on building load.

4. Discussion

• This article offers a comprehensive analysis of the climatic conditions in the Nenjiang region and provides pertinent suggestions for integrating local climatic characteristics into architectural design. The proposed strategies, including harnessing winter solar radiation and leveraging summer natural ventilation, possess broad applicability.
• This article presents structural enhancements to the thermal bridging areas of large-scale insulated block buildings, resulting in a significant improvement in insulation performance and a reduction in thermal bridging effects. Furthermore, the refined construction methods have effectively increased construction efficiency. In practical applications, the proposed enhancements were implemented by the authors, and the primary structural elements were constructed using demountable formwork, achieving a remarkable completion time of just one day with a 100% formwork utilization rate. Additionally, the load-bearing capacity of the beams was enhanced by 3–6 times. The construction of the enclosure structure was efficiently accomplished in a mere three days, significantly expediting the overall construction process.
• Furthermore, there is still room for further improvement in this type of insulated block. In practical applications, the authors have replaced the EPS (expanded polystyrene) in the blocks with XPS (extruded polystyrene), resulting in a thermal resistance of 10 m²·K/W. Considering actual energy usage conditions (equipment not running all day), simulated calculations indicate a comprehensive energy consumption value of 43.8 kWh/m²·a, which is 73.45% lower than the ideal load value mentioned in this article (assuming equipment runs all day). This design achieves a 32.62% energy savings compared to the requirements outlined in the “Heilongjiang Province Ultra-Low Energy Residential Building Energy Efficiency Design Standard” (DB23/T 3337-2022) [18], thus fully meeting the standard’s requirements.
• In comparison to the ultra-low energy consumption residential system utilizing porous bricks and EPS insulation boards [19], the rural housing system proposed in this study exhibits a thermal load of 29.3 kwh/m²·a, representing a 51.32% reduction compared to the thermal load of 60.19 kwh/m²·a, observed in such ultra-low energy consumption rural housing with porous bricks. Moreover, compared to ultra-low energy consumption rural housing equipped with a solar-air source heat pump coupled heating system, the annual cumulative thermal load index achieves a similar level [20]. This demonstrates that in practical applications, houses built with these improved insulated blocks will deliver even better performance and possess significant energy-saving potential.

5. Conclusions

This article provides an analysis of a particular type of insulated block residential housing in extremely cold rural areas of China, focusing on three aspects: the climate environment, thermal bridging in the enclosure, and energy consumption. It thoroughly examines the design requirements, construction characteristics, thermal performance, and energy consumption characteristics of the housing and proposes optimization strategies. Based on the analysis, the following conclusions are drawn:

• The cold climate conditions in the Nenjiang region play a crucial role in the absence of comfort during winter, resulting in a 0% comfort time. Conversely, summer offers a relatively higher comfort time, constituting 76.3% of the total annual comfort time. Hence, it is essential to prioritize the utilization of natural ventilation during the design phase, particularly in the summer. While ensuring effective insulation in the enclosure structure for winter, equal attention should be given to maximizing solar radiation heat gain to reduce the building’s energy demand.
• Corresponding solutions have been proposed for three types of thermal bridging in large-scale insulated block residential buildings, leading to a remarkable 54.82% reduction in the overall heat transfer coefficient of the walls compared to the original design. Moreover, the overall heat transfer coefficient at the beam and column locations has been significantly reduced by 97%. These solutions have not only enhanced the construction efficiency of the main structure and enclosure but also yielded significant improvements in thermal performance.
• The impact of building scale on energy consumption load surpasses that of window size. In the case of residential buildings in extremely cold regions, ample solar radiation aids in reducing the building load. Hence, it is recommended to incorporate larger windows on the south side. Conversely, smaller windows with a window-to-wall ratio of approximately 0.1 are advisable on the north side. Windows on the west side are not recommended.
• To improve energy-saving effectiveness in building forms like insulated blocks, which already exhibit good insulation performance, it is essential to use more efficient insulation materials, implement measures to reduce thermal bridges, adopt a more suitable architectural exterior design, and establish a rational energy utilization plan. These collective efforts will contribute to further enhancing energy efficiency on the existing foundation.