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Article

Study on the Influence of the Energy Intensity of Residential District Layout on Neighborhood Buildings

by
Junle Yan
1,
Hui Zhang
1,2,*,
Yunjiang Li
3,
Xiaoxi Huang
1,
Shiyu Jin
1,
Xueying Jia
1,
Zikang Ke
1 and
Haibo Yu
1
1
School of Civil Engineering, Architecture and Environment, Hubei University of Technology, Wuhan 430068, China
2
College of Design and Engineering, National University of Singapore, Singapore 117566, Singapore
3
School of Civil Engineering and Architecture, China Three Gorges University, Yichang 443002, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(21), 15307; https://doi.org/10.3390/su152115307
Submission received: 18 September 2023 / Revised: 24 October 2023 / Accepted: 24 October 2023 / Published: 26 October 2023
(This article belongs to the Special Issue Energy Efficiency and Multi-Objective Optimization in Building)
Browse Figures
Versions Notes

Abstract

:
Numerous prior studies have substantiated the influence of residential layout on building energy consumption; however, their principal emphasis has predominantly been on urban and neighborhood contexts. Nevertheless, research conducted at the cluster scale has the potential to augment the well-being of neighboring communities and render the objective of a reduction in energy consumption more pertinent to residents’ daily lives. Furthermore, there is a shortage of more robust metrics capable of quantifying the degree of mutual shading among individual buildings within residential neighborhoods. This shading factor constitutes a pivotal element impacting the energy consumption of individual structures. This study utilizes the VirVil-HTB2 tool to calculate solar radiation intensity for individual buildings, serving as a shading metric. Correlation and linear regression analyses are employed to quantify the causal relationship, allowing us to investigate the impact of residential complex layouts on the energy efficiency of individual buildings. The findings of this study indicate that solar radiation serves as a precise metric for gauging shading intensity among buildings, and building energy consumption exhibits a distinct block-like distribution pattern within the residential complex. Furthermore, through an analysis of the level of inter-building shading and a judicious optimization of the layout, it is feasible to achieve a reduction of up to 4.03% in heating energy consumption and a maximum reduction of 4.39% in cooling energy consumption.

1. Introduction

Buildings constitute approximately one-third of the world’s energy consumption and carbon emissions, thus playing a critical role in carbon emissions reduction. The unusually high temperatures witnessed in numerous regions of China in both 2022 and 2023 serve as a stark indicator of the urgent need to develop carbon reduction strategies closely aligned with the climate crisis. For the past four years, China has consistently held the distinction of being the largest contributor to global two-dimensional carbon emissions, achieving a substantial 30.9% share in 2022, with more than half of these emissions originating from the comprehensive lifecycle of buildings [1]. In 2022, China’s urban residential structures were responsible for emitting 410 million tce, representing 39% of the overall carbon emissions attributed to buildings [2]. This percentage is poised to further escalate in tandem with China’s economic expansion and urban sprawl.
The configuration and dimensions of the built environment play a crucial role in shaping diverse urban configurations, which are key factors determining urban energy consumption and influencing both outdoor and indoor climates [3,4,5]. Among these urban configurations, communities and neighborhoods stand out as particularly significant. The arrangement of buildings in clusters with diverse shapes and sizes creates dynamic mutual shading effects. These effects have a direct impact on the absorption of solar radiation by buildings, resulting in substantial variations in building energy consumption and carbon emissions [6,7,8,9]. Numerous studies have investigated the effects of diverse urban geometries on building solar gain. Additionally, the impact of building design features, such as spacing and orientation, on the energy consumption of building clusters has been extensively explored in the literature. However, there is a lack of research focusing on the interplay between individual buildings within building clusters and their impact on building energy consumption and carbon emission characteristics. For instance, a notable study by Strømann-Andersen et al. demonstrated that the geometry of urban layouts plays a crucial role in influencing building energy consumption, particularly in terms of heating, cooling, and lighting requirements [10]. Deng et al. conducted a study utilizing computer simulation techniques to quantitatively assess the influence of building spacing and orientation on building heating energy consumption. Their findings demonstrated that well-planned and efficient space utilization can significantly contribute to the reduction in building energy consumption [11,12]. Urban microclimates also affect building carbon emissions. Ye Hong et al. showed that through reasonable planning and space utilization, building carbon emissions can be reduced [9]. In conclusion, the unique layout and microclimate characteristics generated by the geometry and scale of cities have a profound impact on building energy consumption and carbon emissions, and these aspects have been widely studied [10,13,14,15]. The interbuilding shading phenomenon exerts a notable influence on solar radiation absorption by buildings, thereby exerting a substantial effect on the thermal behavior of said buildings [16,17,18,19,20]. Nevertheless, while various scholars have conducted quantitative analyses of different layout indicators and building energy consumption and carbon emissions, there is a lack of discussion and research on how urban layouts affect buildings’ solar radiation acquisition and cause varied energy consumption and carbon emission characteristics among different buildings.
This study centers its investigation on the influence of diverse residential building complex layouts on the energy intensity of an individual building, employing the quantity of solar radiation received by the surface of a single building as an indicator of shading relationships. This extends from extensive prior research conducted by scholars in the field and leverages established computational principles embedded in the VirVil-HTB2 tool. This study amalgamates preliminary research with computer simulations to scrutinize the aforementioned phenomenon, deliberately excluding variables such as greenery, road layout, building size, and diverse building typologies. The quantification of building energy consumption and solar radiation data was accomplished using SPSS 27 data analysis software. The research was executed at the community level, with a particular focus on the residential buildings situated in Wuhan, a city emblematic of China’s regions experiencing hot summers and cold winters. While the observed energy consumption distribution in this study remains subject to regional factors like climatic conditions and building types, the research methodology serves as a valuable reference point for analogous building complex planning and energy consumption concerns worldwide. Furthermore, the exploration of single-building energy intensity distribution characteristics within a settlement offers insights into enhancing the quality of life within neighboring communities.

2. Methods

2.1. Sample Selection Method

Neighborhoods constitute fundamental entities within urban ecosystems, and issues encountered at the citywide level often result from the cumulative effects of inadequate planning at the community level [21]. Decisions made on a community scale are intricately intertwined with overall quality of life. As a result, we undertook a comprehensive examination of the residential neighborhood layout characteristics in Wuhan, drawing upon existing research within the literature and widely adopted computational tools, such as Google Earth, Baidu Street View, and Geographic Information Systems (GISs). This analysis synthesizes empirical observations from the physical world with satellite imagery. Furthermore, we evaluated the architectural configurations within the residential built-up areas under investigation.
Based on the framework of six fundamental urban forms proposed by Rode et al., we abstracted and selected two distinct layout classifications for 30 residential districts located in the Jianghan, Jiang’an, and Wuchang districts of Wuhan [22]. These classifications include the ‘Parallel Row Layout (PRL1)’, with buildings oriented in both east–west and north–south directions, as well as the ‘Staggered Row Layout (SRL2),’ characterized by east–west alignment but staggered in both east–west and west–north spacing, with a 1/2 building offset. Moreover, in terms of the overall statistical distribution, these two layout forms constitute 50% and 33% of the recorded layout forms, respectively, signifying their prevalence in Wuhan. As outlined in Table 1, drawing upon the statistical findings from our study, we established two typical residential building layouts for simulating and calculating building energy consumption and carbon emissions.
Each single building within the layout types was numbered in order from south to north and from west to east, and the building numbers are displayed in the diagram presented in Table 1. This method was employed to analyze the energy consumption characteristics of each individual building and to explore the impact of different residential cluster layout forms on the energy consumption of single buildings.

2.2. Research Tools and Methods

The spatial configuration of a neighborhood exerts influence on the energy consumption of buildings by interfacing with climatic factors. This interaction encompasses various aspects, such as the shading effects resulting from inter-building positioning, the direct impact of solar heat, and the wind environment on building surfaces, as well as the indirect influence stemming from the microclimate environment shaped by the collective building mass [23]. Presently, prevailing research methodologies primarily involve statistical analyses grounded in empirical data, simulation-based investigations employing computational tools, and hybrid approaches combining statistical and simulation techniques. In this study, our initial approach involves employing a statistical research method to analyze the layout of residential buildings in Wuhan using empirical data. Subsequently, building upon this foundation, we construct an idealized model of these residential structures. Leveraging the VirVil-HTB2 tool, we then engage in simulation-based research to assess both energy consumption and solar radiation patterns within these buildings.
In this investigation, a two-step approach was taken. Firstly, using actual investigation and research methods, residential clusters in Wuhan city situated in the hot summer and cold winter climatic region were categorized and organized, and the typical layout forms of residential clusters were summarized. Secondly, based on the climatic conditions of the region, a sensitivity analysis was conducted utilizing VirVil-HTB2 17 energy consumption simulation software to examine the correlation between the solar radiation reception of buildings and building energy consumption and carbon emissions under different residential cluster layout forms. The purpose of this analysis was to observe changes in the role of solar radiation as a crucial factor in the influence of various building cluster forms on building energy consumption (Figure 1). The HTB2 10 software, developed by the Welsh School of Architecture, Cardiff University, UK, is a comprehensive energy simulation software that includes the VirVil-HTB2 dynamic energy simulation plugin that operates on the commonly used SketchUp platform, capable of performing building energy simulations on an urban scale [24,25,26]. The accuracy and reliability of the simulation results were verified by experts in the field, including Alexander and others [27].
The energy consumption intensity of buildings is influenced by a combination of factors, including the layout of building groups, the architectural design of individual buildings, the spatial configuration of the group, and the presence of greenery, road planning, and building equipment, among others. In this particular study, we aim to isolate the building group layout as the sole variable of investigation. Consequently, we will exclude the impact of factors such as plant greening, road planning, and building equipment. Additionally, we will standardize parameters like building density, floor area ratio, and the architectural typology of individual buildings to maintain consistency, enabling us to conduct simulations of building energy consumption using an idealized model.
HTB2 software represents a comprehensive energy simulation tool meticulously crafted by the Welsh School of Architecture at Cardiff University, United Kingdom. Its primary purpose lies in conducting dynamic simulations to assess the thermal performance of buildings. Conversely, VirVil-HTB2 stands as a dynamic energy simulation plugin ingeniously built upon the widely utilized design tool SketchUp. This innovative plugin facilitates an intuitive approach to energy prediction and analysis during the conceptual phases of design. HTB2 seamlessly interfaces with SketchUp through the VirVil Plugin, treating buildings as simplified geometric constructs primarily to facilitate energy simulations at the planning scale [15,24]. VirVil-HTB2 utilizes direct, diffuse, and direct normal solar radiation data to compute solar radiation incidents on exterior surfaces. When integrated with SketchUp 2018 software, it offers precise simulations and calculations for the solar radiation received on each façade of a building [28]. In the context of this study, where we exclusively examine the complex layout of buildings as the singular variable, disregarding the influence of factors such as greenery, roads, and building equipment, the primary determinant impacting solar radiation reception is mutual shading among the buildings. This metric serves as an indicator of mutual shading within building clusters, with higher solar radiation reception values indicating reduced shielding by neighboring structures. Initially, climate data specific to the Wuhan region were acquired from the Energy Plus website. Subsequently, these data were imported into the VirVil-HTB2 software to establish the model’s geographic location and account for climate factors.
VirVil-HTB2 is a simulation and computational software that builds upon the foundation of HTB2 and SketchUp, centering its attention on energy simulations at the planning level while offering the flexibility of selecting varying simulation cycles. Within these simulations, local geographic coordinates and climate parameters were incorporated into the software. A year-long simulation cycle was chosen, enabling the software to model energy consumption throughout different months in accordance with local climatic conditions. The necessary material parameters were then programmatically supplied to the model. Building upon these parameters, energy simulations of the building cluster were conducted. As can be seen in Table 2, the simulated solar radiation and energy consumption levels have been categorized into four distinct color codes, each reflecting their respective intensities. In this context, the red hue signifies the highest values, the yellow hue indicates values of greater magnitude, the blue hue represents lower values, and the white hue designates the lowest values.
In recent years, the utilization of linear regression analysis has significantly advanced in studies pertaining to building energy consumption analysis. To comprehensively assess the impact of building cluster shading on energy consumption and internal distribution patterns, it is imperative to quantify and analyze these effects [29]. Consequently, in this study, we employ the linear regression analysis method to precisely quantify research data. Using SPSS software, we conducted correlation and multiple linear regression analyses on the simulated data. Initially, we sought to establish whether a correlation exists between solar radiation values, representing the mutual shading conditions among building clusters, and building energy consumption. Subsequently, based on this preliminary analysis, we conducted a linear regression analysis to quantify the causal relationship.

2.3. Parameter Setting

In this study, monolithic buildings with identical basal areas conforming to the scale of residential buildings were utilized for the purpose of data comparison and variable control. The parameters required for the model were established in accordance with various relevant regulations, such as the Design Standard for Energy Efficiency of Residential Buildings in Hot Summer and Cold Winter Zones (JGJ134-2010) [30], Design Standard for Residential Buildings of Low Energy Consumption (DB42/T559-2022) [31], Code for Thermal Design of Civil Buildings (GB 50176-2016) [32], and Standard for Urban Residential Area Planning and Design (GB 50180-2018) [33], to ensure consistency in building density, volume ratio, and body shape coefficient. Specifically, the base area was set to 40 m × 10 m, and the number of building floors and floor height were set to 10 and 3 m, respectively. Energy consumption and carbon emission data were simulated for different spatial layouts of the residential area by changing the layout of the residential spaces, with the parameters of roof, walls, and windows outlined in Table 3.
Based on the climatic data acquired from the official website of Energy Plus, the VirVil-HTB2 was used to compute the building energy consumption and carbon emissions for four types of typical settlement models. In the VirVil-HTB2 data, the energy consumption for the purpose of heating in hot summer and cold winter areas of China was calculated for five months, namely January, February, March, November, and December, and the energy consumption for the purpose of heating was calculated for nine months, namely March, April, May, June, July, August, September, October, and November. The average operational energy consumption data were determined by averaging the sum of energy consumption for cooling, heating, and other energy consumption purposes. Subsequently, the energy consumption and carbon emission data of hot summer and cold winter areas were observed and discussed [28].

3. Discussion and Results

3.1. Analysis of the Relationship between the Influence of Layout Form on the Total Solar Radiation (TSR) and Average Operating Energy Consumption (AOEC) of a Single Building

The primary aim of this study is to systematically investigate the influence of residential cluster layouts on the energy consumption of individual buildings. For this investigation, we conducted a comprehensive characterization of energy consumption. Furthermore, the solar radiation intensity for individual buildings was determined using simulation software, specifically VirVil-HTB2, and employed as a reference metric to quantify the extent of mutual shading among clusters of buildings. The energy consumption of the parallel-row layout (PRL1) is influenced, to some extent, by the wind and thermal environment. The parallel arrangement not only creates favorable conditions for the site’s wind environment but also facilitates efficient ventilation within contiguous buildings [34]. Based on the analysis of the data presented in Table 4 and Figure 2, the monolithic buildings within the parallel-row arrangement are categorized into groups of four buildings, each based on their energy consumption levels, as shown in Table 2. A distinct regional distribution can be observed, with each set of four buildings forming a specific cluster. When comparing this variation in solar radiation with the corresponding building energy consumption patterns, as depicted in Table 2, it becomes evident that solar radiation levels alone do not precisely align with energy consumption levels within the parallel-row layout. However, it is noteworthy that buildings situated in the northeast, northwest, southeast, and southwest regions (P04, P13, P16, and P01) receive the most total solar radiation, measuring 3285.881 kWh/m2-a, 3264.79 kWh/m2-a, 3283.95 kWh/m2-a, and 3297.078 kWh/m2-a, respectively. These findings suggest that buildings located in the northeast, northwest, southwest, and southeast corners of the parallel-row layout, as well as those situated directly to the north and south, have a greater impact on reducing building energy consumption. Conversely, the four central buildings receive the lowest solar radiation and consequently exhibit the lowest energy consumption. Additionally, a distribution pattern of solar radiation and building energy consumption can be observed within the layout, with higher values for peripheral buildings and lower values for interior buildings.
Within the confines of this residential complex, the P01 building stands out with the highest total solar radiation, boasting solar radiation of 3297.078 kWh/m2-a. In contrast, the P11 building exhibits the lowest total solar radiation at 3004.284 kWh/m2-a, resulting in a maximum deviation of 9.75% in total solar radiation. Additionally, we employ the solar radiation value received by each individual building as an index to gauge the extent of mutual shading between structures. This index offers a more accurate representation of the distribution of high and low energy consumption. Beyond individual buildings, which can be influenced by their ventilation environments, we observe a direct correlation between the more solar radiation received and reduced shading, leading to elevated energy consumption values.
Furthermore, a comparable distribution pattern was discerned in the comparative analysis of the staggered-row layout (SRL2) dataset, as illustrated in Table 5 and Figure 3. The most significant total solar radiation is attributed to building S01, recording a total solar radiation value of 3319.504 kWh/m2-a, while the lowest total solar radiation is observed in building S07, registering a value of 3041.107 kWh/m2-a. This disparity results in a maximum deviation of 9.15% in the total solar radiation across the structures.

3.2. Analysis of the Relationship between the Influence of Layout Form on Solar Radiation in the Heating Months (HSR) and Heating Energy Consumption (HEC) of a Single Building

Through the aforementioned analysis, the distribution characteristics of solar radiation and energy consumption during the heating months were observed in a row-by-row layout, exhibiting higher values in peripheral buildings and lower values in internal buildings. To verify if similar characteristics are present during the cooling months, a comparative analysis of solar radiation and energy consumption during the heating months was conducted. Based on the analysis of heating energy consumption and solar radiation during the heating months in the side-by-side arrangement (PRL1) (Table 4, Figure 2), the heating energy consumption ranks as follows: P06 = P11 > P07 = P10 > P14 > P15 > P02 = P03 > P12 > P05 > P13 > P04 > P08 = P09 > P01 = P16. The order of solar radiation received by the buildings is P01 > P16 > P04 > P08 > P09 > P13 > P05 > P12 > P02 > P03 > P15 > P14 > P07 > P06 > P10 > P11. The comparative analysis reveals that the four buildings in the central area (P06, P11, P07, P10) exhibit the highest energy consumption during the heating months, followed by the four buildings situated directly to the east and west. Conversely, the four buildings on the south side display the lowest building energy consumption. Furthermore, the buildings in the central area receive the lowest solar radiation. Similar characteristics are observed in the parallel staggered layout. The comparative analysis in Table 3 demonstrates that buildings with staggered layouts receive more solar radiation in peripheral areas and less solar radiation in central areas. Additionally, building energy intensity exhibits a pattern of lower intensity in peripheral areas and higher intensity in central areas. Overall, the solar radiation distribution follows a pattern of higher values in the periphery and lower values in the center, while energy consumption demonstrates an opposite trend, with lower consumption in peripheral buildings and higher consumption in the center.
In the parallel-row layout, Building P01 stands out as the structure receiving the most significant solar radiation during the heating months, boasting an impressive 1080.919 kWh/m2-a. Conversely, Building P11 finds itself at the opposite end of the spectrum, with the lowest solar radiation recorded at 965.522 kWh/m2-a. This results in a maximum deviation in total solar radiation of 11.95%. When it comes to heating energy consumption, Buildings P06 and P11 take the lead, with a consumption rate of 32.05 kWh/m2-a. On the other hand, Buildings P01 and P16 have the lowest heating energy consumption at 30.88 kWh/m2-a, exhibiting a maximum difference of 3.79%. In the staggered layout, Building S01 emerges as the standout, basking in the highest solar radiation during the heating months, registering an impressive 1088.531 kWh/m2-a. In contrast, Building S07 experiences the least solar radiation at 972.705 kWh/m2-a, resulting in a notable maximum deviation of 11.91% in total solar radiation. The highest heating energy consumption in this layout is recorded in Building S07, standing at 31.99 kWh/m2-a. On the other hand, Building S01 exhibits the lowest heating energy consumption, measuring just 30.75 kWh/m2-a, with a maximum difference of 4.03%.
Furthermore, the staggered arrangement exhibits a more significant effect on solar radiation’s impact on building energy consumption compared to the side-by-side arrangement. This improvement can be attributed to the effectiveness of the staggered layout in mitigating the effects of winter alley winds [35,36].

3.3. Analysis of the Relationship between the Influence of Layout Form on Solar Radiation in the Cooling Months (CSR) and Cooling Energy Consumption (CEC) of a Single Building

Based on the analysis of cooling energy consumption and solar radiation during the cooling months in the parallel ranks layout (PRL1) (Figure 2, Table 4), the cooling energy consumption can be ranked as follows: P04 > P01 > P13 > P16 > P05 = P12 > P08 > P09 > P02 = P03 > P15 = P14 > P07 = P06 > P10 = P11. The comparison results presented in Table 3 indicate that the distribution of cooling energy consumption aligns with the solar radiation distribution among the four regions, ranging from high to low. This suggests that during the cooling months, particularly in summer, the regions situated in the northwest, northeast, southeast, and southwest receive the most solar radiation and, consequently, exhibit the highest energy consumption. In contrast, the middle region receives the weakest solar radiation and demonstrates the lowest energy consumption. Thus, a positive correlation between solar radiation and building energy consumption can be observed, with both variables exhibiting higher values around the periphery and lower values in the middle region. Furthermore, a similar pattern is observed in the comparative analysis of SRL2 data.
In the parallel-row layout, Building P01 stands out as the structure with the highest solar radiation during the cooling months, registering an impressive value of 2703.875 kWh/m2-a. Conversely, Building P11 records the lowest solar radiation during the cooling months, with a value of 2480.318 kWh/m2-a. This results in a maximum deviation of 9.01% in total solar radiation. The highest cooling energy consumption is observed in Building P04, with a consumption rate of 48.72 kWh/m2-a. On the other hand, Buildings P10 and P11 exhibit the lowest cooling energy consumption at 46.67 kWh/m2-a, demonstrating a maximum difference of 4.39%. Within the staggered layout, Building S01 takes the lead, basking in the highest solar radiation during the cooling months, with a notable value of 2721.887 kWh/m2-a. In contrast, Building S07 experiences the least solar radiation during the cooling months, measuring 2514.363 kWh/m2-a, resulting in a noteworthy maximum deviation of 8.25% in total solar radiation. The highest cooling energy consumption is observed in Building S01, with a value of 49.00 kWh/m2-a. On the other hand, Building S11 exhibits the lowest cooling energy consumption, measuring a mere 47.21 kWh/m2-a, with a maximum difference of 3.79%.

3.4. Analysis of the Relationship between the Influence of Building Spacing on Building Energy Consumption in Residential Cluster Layout

In order to conduct a linear analysis on energy consumption and solar radiation for the two types of layout forms, it is essential to conduct a correlation analysis on the relevant data. The findings of this analysis are presented in Table 6. As the six sets of data met the criteria for correlation analysis, individual regression analyses were conducted to quantitatively examine the association between energy consumption and solar radiation.
The linear analysis of the average operating energy consumption versus total solar radiation in the PRL1 layout revealed an R2 value of 0.815 and Sig value less than 0.01. These findings suggest that in the parallel determinant layout, total solar radiation has a positive correlation with the average operating energy consumption of the buildings, accounting for 81.5% of the total variation and exhibiting a good goodness of fit. As a result, the linear equation for this relationship is derived as:
Y1 = 0.01X1 + 114.791.
Y1. PRL1 average operating energy consumption.
X1. PRL1 total solar radiation.
The outcomes of the fitted regression analysis are presented in Table 6. The findings suggest that the linear analysis of the average operational energy consumption of SRL2 and total solar radiation shows an R2 value of 0.512, which is indicative of a reasonably good fit. Furthermore, the other parameters fulfill the conditions for linear analysis, signifying that total solar radiation has a positive correlation with the building energy consumption in the staggered layout, accounting for only about 51.2% of the total variation. The remaining parameters are consistent with the linear equation. As a result, the linear equation for the average operational energy consumption of SRL2, with respect to total solar radiation, is derived as:
Y2 = 0.03X2 + 117.909.
Y2. SRL2 average operating energy consumption.
X2. SRL2 total solar radiation.
The linear equation of PRL1 average heating energy consumption versus heating month solar radiation is as follows:
Y3 = −0.011X3 + 42.381.
Y3. PRL1 average heating energy consumption.
X3. PRL1 heating month solar radiation.
The linear equation of SRL2 average heating energy consumption versus heating month solar radiation is as follows:
Y4 = −0.011X4 + 42.431.
Y4. SRL2 average heating energy consumption.
X4. SRL2 heating month solar radiation.
The linear equation of PRL1 average cooling energy consumption versus cooling month solar radiation is as follows:
Y5 = 0.01X5 + 22.07.
Y5. PRL1 average cooling energy consumption.
X5. PRL1 cooling month solar radiation.
The linear equation of SRL2 average cooling energy consumption versus cooling month solar radiation is as follows:
Y6 = 0.09X6 + 24.664.
Y6. SRL2 average cooling energy consumption.
X6. SRL2 cooling month solar radiation.

4. Significance and Conclusions

  • Utilizing energy simulations as a research methodology, this study employs solar radiation received by individual buildings as a metric to evaluate mutual shading effects among structures. It delves into the distribution patterns of energy intensity within each building and quantitatively analyzes their causal relationships. This approach not only offers valuable design insights but also serves as a reference for the planning and design of residential building complexes. The exploration of energy intensity distribution and its influencing factors forms the foundational framework for endeavors aimed at mitigating building energy consumption. Although this investigation focuses on the hot summer and cold winter regions of China, its findings have broader implications for similar building energy consumption studies on a global scale. This global perspective holds significant promise for addressing challenges related to worldwide energy consumption and carbon emissions. Simultaneously, the investigation into the distribution characteristics of energy intensity among individual buildings within various settlements provides valuable insights for enhancing the quality of life in adjacent communities.
  • The utilization of total solar radiation as an indicator to assess the shading dynamics of residential complexes throughout a year in a region of China characterized by hot summers and cold winters can provide valuable insights, with the potential for applicability to regions worldwide sharing climatic similarities with the Wuhan area. This metric effectively portrays the interplay of shading effects among buildings and exhibits a discernible linear correlation with building energy consumption intensity. The maximum disparity in overall solar radiation among the buildings amounted to 9.75% in the parallel-row layout, while in the staggered-row layout, it reached 9.15%. Notably, when excluding the influences of greenery, road infrastructure, and building morphology, a distinct distribution pattern emerges for both average operational energy consumption and solar radiation in parallel-row configurations. This pattern reveals higher values in surrounding areas compared to the central region, with the northern side surpassing the southern side.
  • In regions experiencing hot summers and cold winters, the solar radiation received by individual buildings remains a robust indicator of the shading dynamics between structures, particularly during the heating months in locales characterized by such climatic extremes. This parameter exhibits a significant negative correlation with building energy consumption intensity. In both layout configurations, heating energy consumption displays a distinct distribution pattern, with higher values being concentrated around the perimeter and the southern aspect registering higher values compared to the northern orientation. In contrast, solar radiation exhibits a contrasting distribution pattern, with lower values at the periphery and the northern side exceeding the southern side. In the parallel-row layout, the maximum deviation in solar radiation during the heating months reaches 11.95%, while the prospective reduction in heating energy consumption can attain as much as 3.79%. Conversely, within the staggered-row layout, the maximum deviation in solar radiation during the heating months reaches 11.91%, with the potential reduction in heating energy consumption extending to a maximum of 4.03%.
  • During the cooling months in regions characterized by hot summers and cold winters, the solar radiation received by individual buildings remains an effective indicator of the shading interactions among structures and maintains a strong correlation with building energy consumption intensity. In both layout configurations, cooling energy consumption and solar radiation exhibit a consistent distribution pattern, with higher values concentrated at the perimeter and lower values towards the center. In the parallel-row layout, the maximum variance in solar radiation during the cooling months amounts to 9.01%, with the potential for reducing energy consumption for cooling extending to a maximum of 4.39%. In the staggered-row layout, the highest discrepancy in solar radiation during the cooling months is 8.25%, and the prospective reduction in energy consumption for cooling can reach a maximum of 3.79%.

Author Contributions

Conceptualization, J.Y. and H.Z.; methodology, J.Y. and H.Z.; software, J.Y.; validation, J.Y., H.Z., Y.L., X.H., S.J., X.J., Z.K. and H.Y.; formal analysis, J.Y.; investigation, J.Y.; resources, J.Y. and H.Z.; data curation, J.Y.; writing—original draft preparation, J.Y.; writing—review and editing, J.Y.; visualization, J.Y.; supervision, H.Z.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Humanities and Social Science Research Project of the Ministry of Education of China, grant number 22YJAZH146; the National Natural Science Foundation of China, grant number 51508169; and the Local Cooperative Project of the China Scholarship Council, grant number 202008420322.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 15307 g001
Figure 1. The schematic representation of the approach used in the study.
Figure 1. The schematic representation of the approach used in the study.
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Sustainability 15 15307 g002
Figure 2. Relationship between building energy consumption and solar radiation for PRL1 monolithic buildings.
Figure 2. Relationship between building energy consumption and solar radiation for PRL1 monolithic buildings.
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Figure 3. Relationship between building energy consumption and solar radiation for SRL2 monolithic buildings.
Figure 3. Relationship between building energy consumption and solar radiation for SRL2 monolithic buildings.
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Table 1. The layout form of residential clusters and the numbering of individual buildings are shown in the table.
Table 1. The layout form of residential clusters and the numbering of individual buildings are shown in the table.
Building NumberAxonometric DrawingsBuilding NumberAxonometric Drawings
Sustainability 15 15307 i001Sustainability 15 15307 i002Sustainability 15 15307 i003Sustainability 15 15307 i004
PRL1SRL2
Table 2. Graphical representation of single-building energy consumption and solar radiation analysis.
Table 2. Graphical representation of single-building energy consumption and solar radiation analysis.
FormTSR vs. AOECHSR vs. HECCSR vs. CEC
PRL1Sustainability 15 15307 i005Sustainability 15 15307 i006Sustainability 15 15307 i007
Sustainability 15 15307 i008Sustainability 15 15307 i009Sustainability 15 15307 i010
SRL2Sustainability 15 15307 i011Sustainability 15 15307 i012Sustainability 15 15307 i013
Sustainability 15 15307 i014Sustainability 15 15307 i015Sustainability 15 15307 i016
Color Remarks: Sustainability 15 15307 i017Highest value Sustainability 15 15307 i018Higher value Sustainability 15 15307 i019Lower value Sustainability 15 15307 i020Lowest value
Table 3. Parameter table.
Table 3. Parameter table.
Parameter CategoryLimit Value
Building size40 m × 10 m × 30 m
Shape coefficient≤0.35
Roof heat transfer coefficient (W/m2/K)≤0.50
Heat transfer coefficient of the wall (W/m2/K)≤1.20
Window thermal coefficient (W/m2/K)≤3.2
Area ratio of window to wall (south)≤0.35
Area ratio of window to wall (east, west, north)≤0.30
Table 4. Building energy consumption and solar radiation for PRL1.
Table 4. Building energy consumption and solar radiation for PRL1.
Building NumberAOEC (kWh/m2-a)HEC (kWh/m2-a)CEC (kWh/m2-a)TSR (kWh/m2-a)HSR (kWh/m2-a)CSR (kWh/m2-a)
P01126.0830.8848.703297.0781080.9192703.875
P02125.4431.6847.263131.2421015.7532578.623
P03125.4431.6847.263131.1131015.6262578.622
P04126.2431.0248.723285.8811067.0192703.584
P05126.1531.3248.333192.7601026.5342633.740
P06125.2432.0546.693008.586966.1252484.600
P07125.2332.0446.693008.907966.4452484.618
P08125.8031.0148.293223.5151060.6282641.284
P09125.7831.0148.273220.0681060.6272637.839
P10125.2132.0446.673004.605965.8422480.336
P11125.2232.0546.673004.284965.5222480.318
P12126.1631.3348.333191.9021025.9312632.902
P13126.2831.1248.663264.791054.9092687.287
P14125.4231.7847.143103.7441003.5152556.047
P15125.4131.7747.143103.9361003.7072556.064
P16125.9930.8848.613283.9501080.3092690.746
Table 5. Building energy consumption and solar radiation for SRL2.
Table 5. Building energy consumption and solar radiation for SRL2.
Building NumberAOEC (kWh/m2-a)HEC (kWh/m2-a)CEC (kWh/m2-a)TSR (kWh/m2-a)HSR (kWh/m2-a)CSR (kWh/m2-a)
S01126.2530.7549.003319.5041088.5312721.887
S02125.8831.6547.733158.4431020.1302604.647
S03125.8231.4047.923183.7521032.6282620.972
S04126.4831.1948.793279.7801060.0022703.116
S05126.4131.4648.453190.7361020.8832636.370
S06125.6631.8647.303060.608983.4062527.992
S07125.7131.9947.223041.107972.7052514.363
S08125.9330.9548.483237.6201064.4102653.196
S09125.8530.9648.393225.4111062.3712642.170
S10125.7231.9847.243044.246974.4262516.113
S11125.6131.9047.213044.609977.7302514.611
S12126.4231.4548.473196.4221022.8872640.248
S13126.5931.1448.953279.5661054.8922703.224
S14125.7831.6547.633140.2451014.3642587.606
S15125.9331.6347.803150.2501014.3942597.331
S16126.0330.8548.683285.7451080.6172692.363
Table 6. Linear regression analysis of data.
Table 6. Linear regression analysis of data.
Correlation FactorsLayout FormPearson CorrelationSig1R2Sig2Constant TermSig3CoefficientSig4
AOEC and TSRPRL10.910<0.010.827<0.01114.791<0.010.01<0.01
SRL20.7380.010.5440.01117.909<0.010.030.01
AHEC and HSRPRL1−0.985<0.010.970<0.0142.381<0.01−0.011<0.01
SRL2−0.986<0.010.973<0.0142.431<0.01−0.011<0.01
ACEC and CSRPRL10.979<0.010.959<0.0122.07<0.010.01<0.01
SRL20.979<0.010.959<0.0124.664<0.010.09<0.01
Sig1: Correlation analysis significance index. Sig2: Linear regression analysis significance index. Sig3: Constant term significance index. Sig4: Coefficient Significance index.
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Yan, J.; Zhang, H.; Li, Y.; Huang, X.; Jin, S.; Jia, X.; Ke, Z.; Yu, H. Study on the Influence of the Energy Intensity of Residential District Layout on Neighborhood Buildings. Sustainability 2023, 15, 15307. https://doi.org/10.3390/su152115307

AMA Style

Yan J, Zhang H, Li Y, Huang X, Jin S, Jia X, Ke Z, Yu H. Study on the Influence of the Energy Intensity of Residential District Layout on Neighborhood Buildings. Sustainability. 2023; 15(21):15307. https://doi.org/10.3390/su152115307

Chicago/Turabian Style

Yan, Junle, Hui Zhang, Yunjiang Li, Xiaoxi Huang, Shiyu Jin, Xueying Jia, Zikang Ke, and Haibo Yu. 2023. "Study on the Influence of the Energy Intensity of Residential District Layout on Neighborhood Buildings" Sustainability 15, no. 21: 15307. https://doi.org/10.3390/su152115307

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