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Simulation of received Solar Radiation for Energy Consumption and Thermal
Comfort in Flexible and Environmental Housing with optimal courtyard in a
Csa Climate
Mahsa Norouzi
Iran University of Science a and Technology
Mitra Ghafourian
Iran University of Science a and Technology
Zahra Barzegar
Tehran Urban Research and Planning Centre(TURPC)
Article
Keywords: Flexible housing, Environmental design, Courtyard, Solar radiation, Iran
Posted Date: July 18th, 2024
DOI: https://doi.org/10.21203/rs.3.rs-4602787/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
Additional Declarations: No competing interests reported.
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Abstract
The adaptability of housing to the resident's needs over time is crucial, relying on the exibility of the structure, with expanding indoor space to outdoor areas
being one of the most suitable methods. On the other hand, outdoor spaces can contribute to providing environmentally compatible housing by reducing
energy consumption while ensuring thermal comfort. In exible and environmental housing, outdoor space (courtyard) plays a crucial role. This research
focused on investigating the role of the courtyard in apartment housing from two perspectives: exibility and environmental compatibility. In exible and
environmental housing, outdoor space (courtyard) plays a crucial role. This research focused on investigating the role of courtyards in apartment housing
from two perspectives: exibility and environmental compatibility. In this regard, the exibility approach involved expanding housing areas into the courtyard,
while the environmental approach entailed determining the optimal direction and position for the courtyard. Environmental parameters such as energy
consumption, thermal comfort, and solar radiation were simulated in three selected time intervals using EnergyPlus software. The validation process
involved comparing the measurement data with the TES-132 data logger and simulation data. The optimal unit was identied using variance analysis and
post hoc testing. Subsequently, the exibility technique was applied to the optimal unit, and the energy consumption and thermal comfort parameters were
compared before and after the implementation. The case study involved three exible housing units with courtyards in the corners (A), the north and south
(B), and the east and west (C) of buildings in a cold climate region in Hamedan. The energy consumption and thermal comfort results in the NW, NE, and SW
directions showed similarities across all units. Therefore, the optimal unit for these directions was determined through variance analysis of solar radiation.
The solar radiation results on the main walls and courtyards indicated that the courtyard acted as a climatic modier, compensating for excess and
deciency of solar radiation. The post hoc T-test analysis on solar radiation for the courtyards demonstrated that the optimal unit was assumed to be BNW,
CNE, and BSW, while in the SE direction, with all three environmental parameters matching, unit ASE was identied as the optimal one. After implementing
exibility in the optimal unit, the comparison results before and after expansion showed a reduction of 11.7% in energy consumption per capita and 6% in
thermal comfort. Flexibility, accompanied by environmental eciency, ensured that the courtyard continued to serve as a climate regulator and remained
environmentally after the expansion of units.
1 Introduction
According to Rapoport, creating an environment that aligns with human lifestyle and needs is one of the goals of housing [1]. In the design of modern
apartments, paying attention to both xed and variable human needs is of utmost importance. One of the proposed solutions is to increase exibility in the
design of apartments, which results in less displacement of households within the city. This means that the spaces should be adaptable to the variable
needs and patterns of users, including social, technological, and environmental aspects [2]. These changing needs may be personal (say an expanding
family), practical (the onset of old age) or technological (the updating of old services). The changing patterns might be demographic (say the rise of the
single person household), economic (the rise of the rental market) or environmental (the need to update housing to respond to climate change) [3]. What is
essential for creating a exible building structure is the physical characteristics of space, such as multifunctionality, adaptability, and space division [4].
Expansibility, as one of the strategies for exible housing, can be achieved by providing more space without the need for relocation, by increasing the internal
area of the residential unit [5, 6].
With the increase in environmental pollution, attention to the principles of sustainable design and environmental design is essential for modern human
societies. Environmental design is the process of addressing surrounding environmental parameters when devising plans, programs, policies, buildings, or
products. It seeks to create spaces that will enhance the natural, social, cultural, and physical environment of particular areas [7]. Early environmental design
focused on parameters such as energy consumption, solar radiation, and buildings orienting and designing to maximize the benet of solar access. The
architecture of energy-ecient and effective residential buildings is proposed as a response to minimizing the negative impact of buildings on the urban
environment. The limit of reducing energy consumption in buildings is compliance with the level of thermal comfort [8]. Thermal comfort is dened as a state
of mind which expresses the individual’s satisfaction with the thermal environment [9]. Parameters such as temperature, radiant temperature, relative
humidity, wind speed, metabolic rate, and clothing have an impact on thermal comfort [10]. Thermal comfort is also inuenced by solar radiation on the
building envelope which may in turn, cause notable thermal discomfort and unacceptable environmental conditions in certain portions of the indoor space
[11]. Designing an environmental building can be an effective solution to increase energy eciency [12], thermal comfort, and solar radiation control. Some of
its strategies include maximizing heat absorption and natural light usage [12], optimal orientation of the building [13], building shape [14, 15], and appropriate
building height [16]. One effective solution is to use open spaces as buffer areas that can act as a climate moderator [17] without requiring heating and
cooling systems [18]. Open spaces, like courtyards, are effective in all climates, and their design features, such as geometry, can vary depending on the
climate [17].
Inadequate attention to addressing both xed needs (such as reducing energy consumption) and variable needs of residents (such as expanding families)
over time has led to increased environmental problems and uncontrolled displacement of households in the city. This is a result of neglecting environmental
design principles and exibility in buildings. In this regard, the use of shared solutions is practical, one of which is utilizing a courtyard with the capability of
expanding the building and serving as a climate moderator. A courtyard as a structural component achieves the goal of creating exible and environmental
housing. Therefore, paying attention to the appropriate combination of open and enclosed spaces, considering climate-related issues such as the suitable
location and orientation of courtyards in residential complexes, is essential.
The current research aimed to achieve Flexible Environmental Housing with the help of using the courtyard. In this regard, perspective was realized by
developing housing areas into the courtyard and environment with the optimal direction and position of the courtyard (Fig.1). To achieve the research goal,
research methods like modeling, simulation using EnergyPlus software, measurement, and analysis of variance were utilized. After modeling three exible
buildings, A, B, and C, with various courtyard positions and orientations, simulations were conducted for energy consumption parameters, thermal comfort,
and building solar radiation levels. Data validation was conrmed by comparing the measurement and simulation results. Subsequently, the optimal units
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were determined based on the analysis of three parameters using Duncan and t-tests. Finally, the exibility technique was applied to the optimal units, and
the energy consumption and thermal comfort parameters before and after expansion were compared.
2 Literature Review
To achieve exible environmental housing, research literature was categorized into two sections: Flexible Design and Environmental Design. Although various
studies have individually explored exible design and environmental design, there is a noticeable gap in research that focuses on the simultaneous
integration of these two design aspects. The primary emphasis of the research has been on concurrently integrating these two facets in housing design.
Flexible housing
The concept of Flexibility in architecture was initially introduced by Priemus and Schroder, focusing on physical, spatial, and structural characteristics [4, 19].
Flexibility is dened as suitability for “different physical arrangements”, which is valid not only for the interior but also, for the exterior adjustments of the unit
itself. [20]. Lang refers to spaces that can respond to many activities without changing and reorganizing [21]. In general, previous research has approached
the subject of exibility with specic perspectives and approaches. There are more studies in the review studies and fewer in the applied research. Previous
studies have discussed exibility with two approaches: Theories and Strategies.
In research with a theoretical approach, Habraken introduced the " Support and Inll" theory [22], which ultimately led to the global approach of "Open
Building" [2, 23]. The term "Grow Home" was also introduced as an incomplete house that owners adapt based on their needs [24]. The theories are detailed
in Table1.
In studies with a strategic approach, exibility is divided into adaptability and Variability. Variability is achieved by designing xed elements (structure and
services) and variable elements that allow for physical changes. It includes strategies for Expansibility, Separability, Flexible furniture and elements, and
Portable Architecture. Adaptability achieves spatial exibility by changing the functionality of spaces without altering their physical structure. This includes
strategies for the multifunctionality of spaces [3, 6, 20, 25–27]. In this study, the strategy of expansibility was chosen because, from a design perspective, the
potential for implementing this approach in the design should be considered from the beginning, and measures for its implementation should be
predetermined in terms of structural, spatial (layout of spaces), and access (circulation) dimensions. The strategy of expansibility was presented in two
forms: "Add-on," which means adding external space (such as open and semi-open spaces) to the internal space and extend a building as requirements
dictated, and "Add-in," which means the gain of useable oor space without actually increasing the ground area occupied by the house [5, 6]. Blakstad
considered space expansibility as the horizontal or vertical expansion of space, which can be achieved through the availability of space and the existing
structural capacity [28]. Živković et al., in the Principle of Spatial Reserves, stated that the oor area is a crucial criterion for the conceptualization of a exible
plan. Accordingly, the space should be oversized to a certain extent to allow for the deployment of various spatial congurations before and after internal
expansibility. Some studies have referred to the strategy of balcony adaptability through opening walls or doors by residents and the balcony's compatibility
with the home space by increasing or decreasing the balcony area for integration into the interior and exterior spaces [29]. Radogna and Kalhoefer,
emphasized the design of expansible units in response to environmental changes, focusing on combining multi-functional and the modiability of interior
space approaches [30]. In Iran, numerous theoretical studies have been conducted on the approach of exibility [31, 32], and the most inuential variable in
exible design, the expansibility of space, has been introduced [33, 34]. Table1 provides a summary of research conducted globally and in Iran regarding
expansibility techniques in construction.
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Table 1
Literature review of Flexible Housing Design
Research Category Description Referense
Theories of
Flexibility Support and
Inll “support” or base building should be clearly separated from “inll” or interior t-out in residential
structure. [22]
Uncertainty “Uncertainty” places the spontaneous and improvised circumstances in the center of the task of
designing and so, reclaims the capacity of space to act as a catalyst for future changes. [35]
Polyvalence refers to a characteristic of a static form that can be put to different uses without having to undergo
changes itself [35]
Open Building is used to dene the numerous concepts that consider the architecture and the surrounding living
environment as "a number of different levels of intervention and processes under general precondition of
constant transformation and change of built environment”.
[2, 23]
Grow Home A house which has not been completed yet and the owners will adapt it based on their needs. [24]
Shearing
Layers Building layers and Longevity include Site, Structure, Skin, Services, Space Plan, Stuff. [36–38]
Permanent
and
Initial
Flexibility
PF: The building should offer the possibility of choosing different design layouts prior to occupancy.
IF: the ability to adjust one’s housing over time.
[2, 39, 40]
Strategy of
Flexibility Expansibility Add in: the gain of useable oor space without actually increasing the ground area occupied by the
house.
Add On: an expedient approach to making more space available without having to move.
[5, 6]
Expansibility Horizontal or vertical expansion of the space with the available space and structural capacity of the
building. [28]
Expansibility The ability to change the size of the rooms [41, 42]
Expansibility Integration of two interior spaces- Expansion of the rooms to the courtyard [26]
Expansibility Floating space between private and public spaces, Expansion of private and public spaces to courtyard
and each other. [31]
Expansibility Sucient oor area for the ability to establish various spatial arrangements before and after the
Expansion. [43]
Expansibility Integrating an open space (such as a terrace) with a closed space (such as a part of the interior) in
response to Environmental changes. [30]
Expansibility Balconies can be designed to provide an expansion to the dining area. [29]
Environmental Design
Another part of the research background is the design of environmental residential buildings, which has attracted the attention of many researchers.
Considering the research objective, such designs were examined from the perspective of measuring three parameters: energy consumption levels, thermal
comfort limits, and body solar radiation levels. By reviewing the studies, it becomes evident that research examining all three parameters simultaneously is
very limited. Therefore, the previous studies were referred to in the investigation of each of the parameters independently. In recent years, understanding
energy consumption trends and elucidating methods for optimizing them through measurement, simulation, and numerical calculations have garnered
signicant attention from researchers in the residential design sector (cooling, heating, lighting, and equipment) (table2). Some articles have addressed both
cooling and heating categories [15, 44], while others have focused only on one of the two categories, either cooling or heating [45, 46]. Energy consumption in
existing buildings was measured using a data logger device [45], and the prediction of this consumption for a building under design was provided in previous
research through simulation methods using various software tools (such as DesignBuilder, EnergyPlus, etc.) [13, 46], or by numerical calculation using energy
formulas [47].
On the other hand, controlling the energy consumption of a building is contingent upon ensuring desirable thermal comfort [48]. Studies conducted in the
eld of thermal comfort are divided into two models: the heat-balance model [49] and the adaptive model. The best well-known heat-balance models are the
predicted mean vote (PMV-PPD) [10] and the standard effective temperature [50]. The adaptive moldel is based on eld surveys, A review of the thermal
comfort of people's response to the environment, using statistical analysi [51]. In different countries, there are different standards for building design based
on thermal comfort in specic. But most of them originate from the three well known and widely used international standards: ISO Standard 7730 [52],
ANSI/ASHRAE Standard 55 [53] and standard EN 15251 [54]. At present, only the last two standards include adaptive comfort components.
The research background on this topic has been presented in Table3, categorized by models. Additionally, solar radiation parameters have a signicant
impact on building envelopes in terms of thermal comfort [11] and energy consumption within the building, which can be quantied through computational
methods, measurements, and simulations. Various computational models have been proposed for accurately estimating the direct and diffuse solar radiation
on a horizontal and vertical surface based on factors such as the maximum temperature and relative humidity [55], solar radiation angle, relative humidity
and temperature [56], sunshine hours and mean temperature [57]. Some studies have used data loggers to measure radiation levels [15, 58], while others
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have utilized simulation software such as energyelus, grasshopper, ecotect, and others to estimate radiation on surfaces [59, 60]. The research background
on this topic has been presented in Table2, and categorized accordingly.
The innovation of this research lies in combining the two concepts of environmental design and exible design. So far, numerous studies have been
conducted separately on environmental design (examining energy consumption parameters, thermal comfort, and radiation) and exibility in housing
(Tables1 and 2). In the current research, this innovative combination has been achieved by allowing residents to expand their living space in the courtyard
according to their changing needs while ensuring environmental sustainability in terms of the specied parameters. Another innovation is the use of
numerous and valid global research methods to obtain parameters of energy consumption, thermal comfort, and radiation in different time frames. In
previous studies, there has been less focus on expansibility in the Special construction area. Two innovations in this research include expansion to the
private courtyard and expansion to the courtyard.
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Table 2
Literature review of Residential Environmental Design.
Approach Reference Location, Climate Method Environmental
strategy Major ndings
EC Heating
&
Cooling
[61] Shiraz, Iran
Semi-arid and hot climate
Measurement:(data
logger) - orientation - - The best orientation: South East
(SE)
- - The average annual energy
consumption per capita
[12] Jakarta: tropical climate,
Marseille: Mediterranean
climate, Poitiers: Oceanic
climate
Numerical Simulations:
(TRNSYS and CONTAM)
- - building
geometry
- - orientation
- Compact shape and appropriate
orientation reduce the energy
consumption
[13] BandarAbbas, Dezful,
Yazd, Tehran and Tabriz,
Iran.
Simulation:(Design
Builder) - orientation - - best orientation: main façade
south.
[44] Different climatic zones meta-analysis and
synthesis) - Courtyard
geometry
- Courtyard
orientation
- - in hot climates: courtyards with
north-south orientation and high
aspect ratios (greater than 2).
- - In temperate climate zones, square
or round courtyards with an aspect
ratio of around 1.
- - in cold climate zones: square
courtyards with north-south
orientation and high openness
[15] Qinghai–Tibet Plateau
severe cold climate
Measurement: (data
logger)
Simulation: (EnergyPlus)
- Building
Orientation
-skylight area
ratio
- - optimal orientation: 30° south by
east and optimal orientation range
spanning.
- - Heating load is negatively
correlated with the skylight area ratio.
SR [59] Tlemcen, Algeria
Hot-summer
Mediterranean
climate
Simulation: (Ecotect and
Comsol Multiphysics)
real interventions
- - building’s
orientation
- exposure to
daylight
- - orientation and architecture and
insulation control the energy
consumption.
- - removing the north and northwest
openings which include a door and
two windows.
[62] Hamedan,Iran
Cold Climate
Simulation: (climate
consultant), Mahoney’s
Table, Evan’s index, Pen
warden graph
- - geometric
conformation -compare strategies with typology of
15 houses located in the Three old
his torical areas:
South eas tern orientation, medium-
sized windows, compact urban
texture and planning,
one-sided yards turn into central
yards, which help to have too much
air movement that is not required for
this climate.
[58] Lhasa rural houses in
China, Cold Climate Field survey,
Measurement,
simulation: (EnergyPlus)
- form design add-on direct solar gain system on
width direction of north rooms,
sunroom on roof, north sealed
balcony, window-wall ratio of south
façade, layout shape
[63] Ardabil, Tabriz, Sanandaj,
and Hamedan.
cold climate
Law of Cosines
computational method - - form design
- - aspect ratio
- - orientation
- the appropriate form: rectangle with
an east-west orientation and suitable
aspect ratio for it is 1:1.2.
- The appropriate orientation for the
determined aspect ratio is 165°
Southeast.
[60] Jianhu in China Simulation:
(Grasshopper) -Urban form
factors
- orientation
- block layout : low south side and
high north side.
orientation: 15° south by westor due
south.
- Shape: Avoid large shapes
[14] Cebu city in Malaysia
tropical monsoon climate
Simulation:
(Grasshopper) - - Building
orientation - the optimal orientation: 290° from
the center of the building, with a
recorded value of 731,356 kWh m − 2.
EC: Energy Consumption, SR: Solar Radiation
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Table 3
Literature review of Residential Environmental Design.
Approach Author Location, Climate Method Environmental
strategy Major ndings
TC Heat
balance [64] Every climate Numerical - psychrometric
table thermal comfort range based on
the formula:
To =0.31Tout + 17.8
[65] Nepal:
cold, temperate, and subtropical regions
Field investigation,
Measurement: data
loggers
- thermal
insulation - Comfort temperature: 17.2 ◦C,
20.9 ◦C, and 21.7 ◦C in the cold,
temperate, and subtropical regions.
- Comfort temperature range in
winter: 17.2–21.7 (◦C)
[66] Tabriz, Iran
cold climate
Olgyay, Givoni
bioclimatic charts,
Mahoney tables,
ASHRAE 55
standard
- passive
strategies Orientating, Passive Heating,
Openings: (Windows for gaining
solar radiation), Compact Layout,
Semi-open Transition Spaces,
Natural Ventilation, Sun shadings.
[67] Shiraz, Iran
semi-arid climate
Measurement: data
logger
Numerical:
PMV/PPD model
-wind catcher - wind catcher and openings are
close and open: the thermal
comfort was directly related to the
amount of wind coming from the
wind tower.
- average ambient temperature in
groundand rst oor decreased.
[68] Shiraz, Iran
semi-arid climate
Simulation: Design
Builder
Numerical:
PMV/PPD model
- passive
strategies - PMV in May, June, September and
October were in the range of − 1 to
1 .
[69] Shiraz, Iran
semi-arid climate
Measurement: data
logger
Numerical:
PMV/PPD model
-Open and
close window - the ve-door of the traditional
house are effective on thermal
comfort.
- the design of the types of
openings, the dimensions and sizes
of the openings are effective on the
internal thermal comfort.
[70] Dalian City in China, a cold region Simulation: ENVI-
met
Calculation:
equivalent
temperature (PET)
- Spatial
morphology - building orientation, sunshine
spacing coecient, and building
layout will affect the thermal
environment.
Adaptive [71] Different climatic zones systematic Review,
Field Survey
- - comfort temperature in the
condition of FR, CL, HT and mixed =
26.1 ◦
C, 22.5 ◦
C, 18.6
◦C and 22.7
◦C
- - in a temperate climate = 22.9 ◦
C
- - for tropical, sub-tropical,
continental and polar climate = 24.5
◦C, 23.4
◦C, 21.7
◦C and 10.7 ◦
C
[18] Turpan, China
dry–hot and dry–cold areas
eld measurement,
questionnaire - passive solar
heating - - summer: passive solar heating
- - Winter: passive solar heating
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Approach Author Location, Climate Method Environmental
strategy Major ndings
-Semi-outdoor
spaces
-Courtyards
-surface area
- - Semi-outdoor spaces covered by
awnings
- - courtyards: arranged as buffer
spaces.
- - winter: surface area of the
exterior wall: small
EC
&
TC
Cooling [46] Hong Kong:
hot and humid
Simulation: (Design
Builder).
Givoni Building Bio-
Climatic Chart
- - solar
protection
- - window
opening area
- Shading
devices
- solar protection is sensitive
strategy
- combination of sensitive passive
design parameters can reduce the
annual and peak cooling load.
EC
&
TC
Heating
&
Cooling,
Heat
balance
[17] climate zone:
Melbourne(3A),Copenhagen(5A),
Singapore (0A), Tehran (3B), Algiers(3B)
brute-force
Simulation: Design
Builder
- - courtyard
geometry
- geographical
location
- square-shaped courtyards:
improving both summer and winter
of extremely hot-humid locations
and only summer of a warm-dry
climate.
- rectangular-shaped courtyards
perform better in the winter of
warm-dry climates and summer of
warm-humid climates.
TC: Thermal Comfort
3 Material and Method
To achieve exible and environmentall apartment housing, energy consumption, thermal comfort parameters, and solar radiation in exible units were
simulated, and the optimal orientation and situation of courtyards for units were determined. In this regard, the research method of this article was divided
into two sections: materials and methods.
A. Material
The research was conducted in Iran, Hamadan, which based on the Köppen-Geiger climate classication, has a temperate climate (Csa) with dry summers
(S) and very warm temperatures (A) [72]. The case study of this research involved designing a residential complex located in the southwest of this city
(Fig.2-A). The time frame was determined based on the thermal comfort temperature of the Sanandaj station (the closest station with a similar climate),
which ranges from 21 to 27 degrees Celsius [34]. The months in which the average monthly temperature was lower than the annual average temperature (12
degrees Celsius) were considered cold months. However, those with a medium temperature ranging from the minimum to the maximum thermal comfort
temperature were categorized as warm months, and the ones with an average between the minimum thermal comfort temperature and the annual average
temperature were considered as moderate months (Fig.2-B). Previous research has shown that the winter in this city can last up to 6 months, which is the
main concern [73]. By checking the annual temperature of all days of 2022 with Meteonorm and EnergyPlus, the time range of the research was the 15th day
of every month, the coldest day of the year (January 11), and the hottest day of the year (July 29), from 00:00 to 23:00. Determining the degree of measured
parameters in extreme climatic conditions along with checking the mean temperature on the 15th day of each month guarantee the accuracy and validity of
the research.
The building form designed to combat the cold is similar to compact volumes such as cubes (with a low ratio of external surface area to external volume),
and service spaces and access areas are located at the center of the volume (Table4).
The solution proposed in the article involves the use of a courtyard, which can be implemented in two different forms: internal (within the central area of the
residential volumes) or external (positioned on the outer edges). The internal courtyard design leads to an increase in the oor area of the lower level and the
overall dimensions of the building, resulting in a larger surface area exposed to open air. However, in the specic climate conditions chosen for this study, the
internal courtyard does not fulll its intended environmental function; hence, external courtyards were opted. The primary focus of this research is to
integrate exible design with environmental considerations. This requires providing sucient lighting, ventilation [74], and thermal comfort. The L-shaped unit
plan can provide the possibility of placing a courtyard, expanding spaces into it, having multiple building bodies adjacent to the courtyard, appropriate
daylighting depth (9–13 meters), and desirable ventilation. A Flexible Environmental residential design was achieved by determining the optimal location and
orientation of the courtyard. The position of private courtyard for each unit (only on the top oor) was divided into three types: courtyard in the four corners of
the building (A), courtyard in the north and south direction of the building (B), and courtyard in the east and west direction of the building (C) (Table4). In
each of the three buildings A, B, and C, four L-shaped units were placed in the SW, SE, NW, and NE directions, For example, the ASW unit is building unit A in
the SW direction. The plans of the units were designed based on the Neufert standard drawn by AutoCAD software and presented in Table4 [75]. To achieve
exible housing, a part of the courtyard could be added to the oor area, reducing the courtyard area from 42 (before expansibility) to 18 (after expansibility)
square meters. The U-values were determined based on Part 19 of the National Building Regulations of Iran. The U-values were determined based on Part 19
of the National Building Regulations of Iran. According to Iran's national building regulations, Hamedan is classied as a city with high energy consumption,
falling under Group A (residential, administrative buildings, etc.), and is considered part of Group 1 in terms of energy eciency [76] (Table4).
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B. Method
In line with the research objectives, buildings A, B, and C were modeled using SketchUp 2015 software, with six thermal zones specied (entire rst to third
oors, units SW, SE, NW, NE, and common areas). Weather data was obtained through Meteonorm software and aligned with the data from the Iranian
Meteorological Organization. Subsequently, the output from SketchUp with the OS: Space5 extension and the output from Meteonorm with the EPW
extension were imported into EnergyPlus 8.4 software. Additionally, information such as the material of building elements, ceilings, oors, walls, windows,
number of occupants, permeability levels, thermal resistance of building elements, upper and lower limits of thermal comfort, etc., were determined in
EnergyPlus. Internal and external boundary conditions of surfaces were divided into contact with external air, contact with soil, exposure to sunlight, and
exposure to wind. The number of calculation repetitions was set to 6 times in each zone and at each moment to increase the accuracy of the calculations.
To determine the optimal orientation of the building on the site, the building was rotated from an angle of -30 to + 30 degrees in 10-degree intervals and
assessed in terms of the simulation of two parameters: energy consumption and thermal comfort. In this step, the amount of energy consumption, thermal
comfort, and received radiation on the south, east, and west walls of buildings A, B, and C were simulated in the selected time range. To accurately compare
the solar radiation on different surfaces, precise criteria are required. The solar radiation towards the north direction is considered indirect and its inuence
needs to be excluded for this purpose. The solar radiation received from the east in all months with a higher amount is desirable and optimal. Western solar
radiation received in all months with a lower amount is climatic. Also, the greater amount of southern solar radiation is suitable in cold months and
unsuitable in hot months. In mild months, a moderate amount of solar radiation is optimal [61]. Subsequently, the energy consumption, thermal comfort, and
solar radiation of the three buildings were comparatively analyzed. However, a comparative comparison alone was not sucient, and statistical analysis was
required. Therefore, the Co-directional units were analyzed and optimized with the help of a one-way analysis of variance (ANOVA), Duncan's Post Hoc test,
and T-test.
The research was validated by comparing measured and simulated data. Subsequently, the optimal units were identied based on the three parameters. The
exibility technique was implemented in the selected optimal unit. Finally, energy consumption and thermal comfort parameters before and after
expansibility were compared by re-simulating using EnergyPlus software under identical conditions and the role of the courtyard was examined in terms of
exibility and environmental sustainability. The research method process was showed in Fig.3.
For the validation of the data in the article, in the rst step, solar radiation on the horizontal surface was simulated using EnergyPlus software, which is one of
the most powerful energy simulation software in the world. It can simulate energy consumption for heating, cooling, ventilation, lighting, and water use in
buildings, enabling prediction of the building's thermal behavior before design based on its inputs and outputs [77]. The software has a high level of accuracy
and its average deviation for annual energy consumption is 3.2% and for indoor air temperature is 0.8°C [78]. In the next step, a pyranometer of type
datalogger TES-132 was placed at a height of 80 centimeters above the ground oor, and the amount of solar radiation received on the horizontal surface on
the coldest day of the year at 10 AM, 11 AM, and 12 PM was measured (Table5). Finally, measurement results were compared with the simulation. Given the
very slight difference between the simulated and measured values of solar radiation on the horizontal surface, validation of the simulation data was
conrmed with an error coecient of less than one percent, demonstrating the reliability and validity of the study.
Page 10/25
4 Result
The current research examined the role of the courtyard from both exibility and environmental perspectives and determined its optimal position and
orientation. As stated in the research method, the parameters of energy consumption and thermal comfort in the overall building volume were calculated
with EnergyPlus software by rotating from an angle of -30 to + 30 degrees at ten-degree intervals, and the optimal orientation of the building on the site was
determined. Subsequently, the energy consumption, thermal comfort, and radiation on the vertical surfaces of three buildings (A, B, and C) before applying
the exibility technique, were simulated using EnergyPlus software.
A. Environmental Comparison Before Expansibility
Building Orientation
The building's energy consumption and thermal comfort were simulated and compared for different orientations ranging from − 30 degrees (SouthWest) to +
30 degrees (SouthEast) at 10-degree intervals, covering seven different angular positions. The study found that the optimal orientation for the building, in
terms of energy consumption and thermal comfort, was at an angle of zero degrees (north-south orientation). The highest energy consumption and thermal
discomfort were observed at angles of -30 degrees and + 30 degrees (Table6).
Energy Consumption (EC)
Page 11/25
The energy consumption rates for the 15th of each month as well as the coldest and hottest days of the year for buildings A, B, and C, were calculated using
EnergyPlus software (Table7). The best energy consumption was observed at 4 PM on the coldest day of the year, with the worst at 7 AM. Additionally,
during the hottest day of the year, the period from 1 AM to 9 AM showed the best energy consumption, while 4 PM showed the worst (Fig.4). With the help of
comparative analysis, the lowest consumption apartment unit based on the annual amount belongs to ASW, the lowest minimum consumption on the coldest
day of the year belongs to BSE and the highest maximum consumption on the hottest day of the year belongs to ANE.
Also, by comparing the annual energy consumption of unidirectional units, the best amount in NW and NE direction belongs to building C, in SW and SE
directions belongs to building A, on the coldest day of the year belongs to buildings C, C, A and B respectively and on the hottest Day of the year respectively
belonged to building A.
Table 7
Energy consumption on the 15th of every month (Kwh)
A B C
Month NW NE SW SE NW NE SW SE NW NE SW SE
Jan 8569.9 8567.1 8026.2 8027.4 8686.1 8674.4 8132.6 8158.1 8571.4 8584.9 8292.4 10285.2
Feb 5945.5 5931.2 5510.4 5502.4 6002.5 5988.8 5559.0 5604.8 5922.5 5925.4 5692.4 7091.1
Mar 3686.1 3683.1 3412.3 3413.0 3685.1 3681.8 3413.6 3577.9 3667.7 3675.5 3510.0 4417.0
Apr 1870.8 1849.6 1783.3 1764.0 1838.3 1827.6 1752.7 2067.0 1878.7 1860.2 1828.4 2323.2
May 544.6 499.4 533.3 490.1 509.5 486.9 506.0 957.8 549.4 510.1 549.5 696.7
Jun 235.2 190.0 239.4 195.5 278.2 251.6 283.3 847.5 256.9 212.0 258.5 323.2
Jul 517.9 506.1 545.9 535.7 611.8 606.0 638.1 1121.3 569.3 555.3 579.5 793.8
Aug 393.5 338.0 477.7 426.7 460.5 429.6 550.7 1039.8 450.8 398.4 502.0 631.7
Sep 305.5 268.8 278.2 236.6 296.6 272.5 281.5 949.5 304.8 269.0 291.6 391.7
Oct 1550.6 1531.1 1340.7 1324.9 1534.3 1519.4 1321.5 1754.0 1504.2 1494.4 1385.8 1789.0
Nov 4211.1 4189.5 3829.1 3802.4 4242.8 4216.1 3848.9 3947.9 4153.4 4138.5 3952.4 4968.5
Dec 6717.1 6725.6 6243.9 6262.0 6795.3 6802.6 6324.7 6357.7 6689.4 6720.4 6456.7 8014.8
Annual 6717.1 6725.6 6243.9 6262.0 6795.3 6802.6 6324.7 6357.7 6689.4 6720.4 6456.7 8014.8
Thermal Comfort (TC)
The Thermal Comfort on the 15th of each month, the coldest and hottest days of the year for buildings A, B, and C, were calculated using EnergyPlus
software (Table8). The best PMV was observed at 4 PM on the coldest day of the year, with the worst at 7 AM. Additionally, at 5 AM, there was the best PMV,
whereas 4 AM had the worst (Fig.5). With the help of comparative analysis, the optimal apartment unit based on annual thermal comfort belongs to BSE, its
lowest minimum on the coldest day of the year belongs to BSE and its lowest maximum on the hottest day of the year belongs to ASE.
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Table 8
PMV in hottest day of the year
A B C
Time (hour) NW NE SW SE NW NE SW SE NW NE SW SE
1:00 0.6299 0.6315 0.6336 0.6305 0.6335 0.6346 0.6323 1.0262 0.6343 0.6311 0.6337 0.6354
2:00 0.6047 0.6063 0.6082 0.6053 0.6081 0.6092 0.6070 0.9851 0.6089 0.6058 0.6083 0.6100
3:00 0.5859 0.5875 0.5894 0.5865 0.5892 0.5903 0.5882 0.9546 0.5900 0.5870 0.5894 0.5911
4:00 0.5757 0.5772 0.5791 0.5763 0.5790 0.5800 0.5779 0.9379 0.5797 0.5768 0.5791 0.5807
5:00 0.5726 0.5741 0.5760 0.5732 0.5759 0.5769 0.5749 0.9329 0.5766 0.5737 0.5761 0.5776
6:00 0.5863 0.5878 0.5897 0.5869 0.5896 0.5907 0.5885 0.9552 0.5904 0.5873 0.5898 0.5914
7:00 0.6302 0.6319 0.6339 0.6309 0.6338 0.6350 0.6327 1.0268 0.6346 0.6314 0.6340 0.6358
8:00 0.6943 0.6961 0.6984 0.695 0.6983 0.6995 0.6970 1.1312 0.6992 0.6956 0.6985 0.7004
9:00 0.7659 0.7679 0.7704 0.7667 0.7702 0.7716 0.7689 1.2478 0.7712 0.7673 0.7705 0.7726
10:00 0.8361 0.8383 0.8410 0.837 0.8409 0.8424 0.8394 1.3622 0.8419 0.8376 0.8411 0.8434
11:00 0.8992 0.9015 0.9045 0.9001 0.9043 0.9059 0.9027 1.4649 0.9054 0.9008 0.9046 0.9070
12:00 0.9523 0.9548 0.9580 0.9533 0.9577 0.9595 0.9560 1.5516 0.9590 0.9541 0.9580 0.9607
13:00 0.9929 0.9955 0.9988 0.9939 0.9985 1.0003 0.9968 1.6177 0.9998 0.9947 0.9988 1.0016
14:00 1.0198 1.0225 1.0258 1.0209 1.0256 1.0275 1.0238 1.6615 1.0269 1.0217 1.0259 1.0288
15:00 1.0331 1.0358 1.0392 1.0342 1.0390 1.0409 1.0371 1.6832 1.0403 1.0350 1.0393 1.0422
16:00 1.0345 1.0372 1.0406 1.0356 1.0404 1.0422 1.0385 1.6854 1.0417 1.0364 1.0407 1.0436
17:00 1.0198 1.0225 1.0258 1.0209 1.0256 1.0275 1.0238 1.6615 1.0269 1.0217 1.0259 1.0288
18:00 0.9878 0.9904 0.9936 0.9888 0.9934 0.9952 0.9916 1.6093 0.9947 0.9896 0.9937 0.9964
19:00 0.9428 0.9453 0.9484 0.9438 0.9481 0.9499 0.9465 1.5360 0.9494 0.9445 0.9484 0.9511
20:00 0.8886 0.8909 0.8938 0.8895 0.8936 0.8953 0.8920 1.4477 0.8948 0.8902 0.8939 0.8964
21:00 0.8330 0.8352 0.8379 0.8339 0.8378 0.8393 0.8363 1.3572 0.8389 0.8346 0.8380 0.8403
22:00 0.7768 0.7788 0.7814 0.7776 0.7812 0.7826 0.7798 1.2656 0.7822 0.7782 0.7815 0.7836
23:00 0.7212 0.7231 0.7255 0.722 0.7253 0.7266 0.7240 1.1751 0.7263 0.7226 0.7256 0.7276
0:00 0.6650 0.6667 0.6689 0.6657 0.6688 0.6700 0.6676 1.0834 0.6696 0.6662 0.6690 0.6708
MAX 1.0345 1.0372 1.0406 0.5732 1.0404 1.0422 1.0385 1.6854 1.0417 1.0364 1.0407 1.0436
MIN 0.5726 0.5741 0.5760 1.0356 0.5759 0.5769 0.5749 0.9329 0.5766 0.5737 0.5761 0.5776
Also, by comparing the annual energy consumption of unidirectional units, the best amount in NW and NE directions belongs to building C, in SW and SE
directions belongs to building A, on the coldest day of the year belongs to buildings C, C, A and B respectively and on the hottest Day of the year respectively
belonged to building A.
Moreover, by comparing the annual thermal comfort for the units of the same direction, it was realized that the best amount of NW direction belonged to C;
whereas, the optimal amount for NE direction belongs to C, for SW direction belongs to B, and for SE direction belongs to B (Fig.5). On the coldest day of the
year the best rate was recorded for C, C, B, and B and, on the hottest day of the year it became A, C, B and B respectively.
Solar Radiation (SR)
The received solar radiation on the surfaces for the fteenth of each month in buildings A, B, and C, were calculated using the EnergyPlus software. The
vertical surfaces of the buildings include eight walls (four main walls and four courtyard walls) in the north, south, east, and west directions.
In building A, the southern main walls of units ASW and ASE had the same amount of received solar radiation throughout the year (Table9) because the areas
and shading type on them were the same for both units. However, the southern walls of the courtyard of these units had different received solar radiation
(2074.99 and 2064.29 watts in February, respectively), as despite having the same area, their shading types differed. Furthermore, in July, the south walls had
the minimum received radiation while the east and west walls had the maximum amount of received solar radiation.
Page 13/25
Table 9
received Solar Radiation of the main walls and the courtyard walls of building A ,on the 15th of every month.
ANW ANE ASW ASE
Month N N_yard W W_yard N N_yard E E_yard S S_yard W W_yard S S_yard
Jan 746.6 344.7 1151.2 704.3 746.6 344.7 1115.5 670.4 4460.8 1956.3 1151.2 1005.3 4460.8 1943.7
Feb 996.5 460.1 1444.4 962.7 996.5 460.1 1418.2 933.2 4667.3 2075.0 1444.4 1257.9 4667.3 2064.3
Mar 1267.8 586.0 1665.2 1230.5 1267.8 586.2 1585.5 1182.1 3949.7 1805.0 1665.2 1444.4 3949.7 1781.0
Apr 1483.6 686.7 2027.6 1670.8 1483.6 676.4 1976.6 1616.3 3268.8 1517.2 2027.6 1746.8 3268.8 1523.0
May 1965.6 851.2 2272.5 1948.5 1965.6 886.3 2492.2 2147.2 2670.7 1261.3 2272.5 1918.5 2670.7 1261.3
Jun 2309.8 985.5 2461.9 2136.0 2309.8 1031.5 2691.7 2342.6 2483.7 1178.4 2461.9 2051.9 2483.7 1179.6
Jul 2196.7 937.5 2266.3 1943.6 2196.7 990.4 2683.3 2316.3 2651.1 1247.1 2266.3 1896.7 2651.1 1252.2
Aug 1673.0 761.9 2259.7 1899.5 1673.0 756.5 2302.1 1929.2 3237.1 1512.7 2259.7 1934.4 3237.1 1519.6
Sep 1306.4 608.5 2109.4 1632.7 1306.4 606.9 2139.0 1652.7 4468.8 2044.6 2109.4 1848.5 4468.8 2048.3
Oct 1060.6 492.3 1654.7 1162.3 1060.6 492.3 1580.6 1108.4 4487.1 2021.5 1654.7 1448.5 4487.1 1999.5
Nov 889.8 410.2 1127.4 735.9 889.8 410.2 1164.8 740.1 4215.1 1858.6 1127.4 974.9 4215.1 1875.7
Dec 728.3 335.2 1036.6 614.1 728.3 335.2 953.4 567.1 4007.1 1763.7 1036.6 901.0 4007.1 1723.7
Annual 1385.4 621.6 1789.7 1386.7 1385.4 631.4 1841.9 1433.8 3713.9 1686.8 1789.7 1535.7 3713.9 1681.0
By comparing the received radiation on the southern main walls, units CSW and CSE had the highest amount, which was desirable for the cold month (Fig.6).
Units BSW and BSE had the least amount, which was optimal for the hot month, and units ASW and ASE had a middle amount, which was desirable for the
moderate month. In the eastern main walls, units BNE and BSE had the highest amount of received solar radiation, which was desirable in all months (Fig.6).
However, in the western main walls, units ANE, ASE, BNE, BSE, CNE, and CSE did not receive this type of radiation, which was desirable in all months.
By comparing the received radiation on the southern walls of the courtyard, units BSW and BSE had the highest amount, which was desirable for the cold
month, while units ASW and ASE had the least amount, which was optimal for the hot month, and units CSW and CSE had a middle amount, which was optimal
for the moderate month (Fig.7). In the eastern walls of the courtyard, unit ASE had the highest amount of solar radiation, which was desirable in all months
(Fig.7). However, in the western walls of the courtyard, the ANE, ASE, BNW, BSW, CNE, and CSE units became optimal in all months due to not receiving this type
of solar radiation.
5 Disscusion
To analyze the results of simulation parameters such as energy consumption, thermal comfort, and solar radiation on buildings A, B, and C in SW, SE, NW, and
NE directions, the analysis of variance (ANOVA) method was used with the software SPSS. The data was described in the Descriptives table. The null
hypothesis (H0) assumed that the means of the data in all directions are the same. The probability of the H0 occurrence was assessed by examining the
signicance level (sig) of the data mean differences in the ANOVA and Independent Samples Test tables. If the sig value is greater than the alpha coecient
(0.05), it conrms the null hypothesis, indicating that the means of the data in the three groups are similar. Otherwise, differences exist between the groups.
The Duncan post hoc test was used to evaluate statistical differences among at least three different samples. The T-test post-hoc test was employed to
determine the presence of statistical differences between a maximum of two different samples.
A. Environmental Comparison Before Expansibility
Energy Consumption (EC)
The average energy consumption data for three groups on the 15th of each month, the coldest and hottest days of the year in the NW, NE, SW, and SE
directions were determined, along with the upper and lower limits of the mean in each direction. Then, using analysis of variance, the probability of the null
hypothesis occurrence was investigated, and for evaluating the statistical difference between three different samples, the Duncan post-hoc test was utilized.
The results were elaborated on in detail within three-time intervals.
On the 15th of each month, the sig values in the NW, NE, SW, and SE directions were 0.999, 0.999, 0.997, and 0.792, respectively, which, as the value was
greater than 0.05, proved the H0 and indicated that the means of the data in these three buildings are similar.
On the hottest day, the sig values in the NW, NE, and SW directions were 0.947, 0.941, 0.961, and 0.539, respectively. Since the values were greater than
0.05, the H0 was approved, conrming that the means of the data in these three buildings were similar.
On the coldest day, the sig values in the NW, NE, and SW directions were 0.903, 0.917, and 0.811, respectively, which, as the values were greater than
0.05, proved the H0 and showed that the means of the data in these three groups are similar. However, the sig value in the SE direction was 0.000,
implying that since the value was less than 0.05, the H0 was rejected, indicating that there is a difference in the means of the data among groups in this
Page 14/25
direction (Table10). The results of the Duncan test to determine different groups showed that the mean energy consumption of Building C differed by
0.04 from Buildings A and B, making Buildings A and B the best in this direction with lower energy consumption (Table11).
The summary of optimal units in terms of energy consumption for each period of NW, NE, SW, and SE degrees is presented in Table15.
Table 10
Energy consumption Anova Test In Coldest day of the year
Direction Sum of Squares df Mean Square F Sig.
NW Between Groups 1.288 2 .644 .102 .903
Within Groups 435.362 69 6.310
Total 436.650 71
NE Between Groups .968 2 .484 .086 .917
Within Groups 386.882 69 5.607
Total 387.850 71
SW Between Groups 3.821 2 1.911 .211 .811
Within Groups 626.210 69 9.076
Total 630.031 71
SE Between Groups 360.503 2 180.252 15.379 .000
Within Groups 808.715 69 11.721
Total 1169.219 71
Table 11
Energy consumption Duncan Test For SE Direction
N group Subset for alpha = 0.05
1 2
1 (A) 24 16.870833
2 (B) 24 17.212500
3 (C) 24 21.779167
Sig. .731 1.000
Means for groups in homogeneous subsets are displayed.
a. Uses Harmonic Mean Sample Size = 24.000.
Thermal Comfort (TC)
The average thermal comfort data for three buildings on the fteenth of each month, the coldest and hottest days of the year in the NW, NE, SW, and SE
directions were determined, along with the upper and lower limits of the mean in each direction. Then, using analysis of variance, the probability of the H0
occurrence was investigated, and for evaluating the statistical difference between three different samples, the Duncan post-hoc test was utilized. The results
were elaborated on in detail within three-time intervals.
On the 15th of each month, the sig values in the NW, NE, SW, and SE directions were 0.965, 0.901, 0.974, and 0.973, respectively, which, as the value was
greater than 0.05, proved the H0 and indicated that the means of the data in these three buildings are similar.
On the hottest day, the sig values in the NW, NE, and SW directions were 0.993, 0.995, and 0.999, respectively, which, as the values were greater than
0.05, proved the H0 and showed that the means of the data in these three buildings are similar. However, the sig value in the SE direction was 0.000,
which, as the value was less than 0.05, rejected the H0 and indicated that there is a difference in the means of the data among groups in this direction
(Table12). The results of the Duncan test showed that the mean data of Building B differed by 0.5 from Buildings A and C. Therefore, Buildings A and C
were optimized with lower PMV.
On the coldest day, the sig values in the NW, NE, and SW directions were 0.454, 0.489, and 0.884, respectively, which, as the values were greater than
0.05, proved the H0 and showed that the means of the data in these three buildings are similar. However, the sig value in the SE direction was 0.002,
which, as the value was less than 0.05, rejected the H0 and indicated that there is a difference in the means of the data among groups in this direction
(Table12). The results of the Duncan test showed that the mean data of Building C differed by 0.03 from Buildings A and B. Therefore, Buildings A and B
were optimized with lower PMV.
The summary of optimal units in terms of thermal comfort for each period of NW, NE, SW, and SE degrees is presented in Table15.
Page 15/25
Table 12
Thermal Comfort (PMV) ANOVA Test in Coldest Day and Hottest Day
Of The Year
Direction Sig. Coldest day Sig. Hottest Day
NW Between Groups .454 .993
NE Between Groups .489 .995
SW Between Groups .884 .999
SE Between Groups .002 .000
Solar Radiation (SR)
In the beginning, the solar radiation data for the southern, eastern, and western main walls of Buildings A, B, and C were described, followed by the
description of the courtyard body structures in the directions NW, NE, SW, and SE. Then, using analysis of variance (ANOVA) and Independent Samples Test,
the probability of the H0 occurrence was investigated. Different buildings were identied with follow-up T-Test and Duncan tests, and optimal units were
determined in each direction.
On the 15th day of each month, sig values of the south, east, and west main walls in three buildings in all directions NW, NE, SW, and SE were all 0.000
(Table13), which, as the values were less than 0.05, rejected the H0. The results of the Duncan test in each direction are as follows:
NW: The mean data of the solar radiation of the western main walls of Building B, differed by 1629 and 1303 watts from Buildings A and C, respectively.
Buildings A and C were optimized due to their lower received solar radiation.
NE: By examining the mean data of the solar radiation of the eastern main walls, Building B was the best.
SW: by examining the mean data of the solar radiation of the southern main walls, Building C was the best, and in the western main walls, Buildings A
and C were optimized.
SE: Upon examining the mean data of the solar radiation of the southern main walls, Building C was the best, and in the eastern walls, Building B was
optimized.
On the 15th day of each month, sig values of the southern, western and eastern walls of the courtyard in three buildings in all directions NW, NE, SW, and SE
were all 0.000 (Table13), which, as the values were less than 0.05, rejected the H0. The results of the Duncan test in each direction are as follows:
On the 15th of every month, the sig values of the southern, western, and eastern walls of the courtyard of three groups in NW, NE, SW, and SE directions were
presented in Table13. The results of the T-Test analysis were provided for the four directions mentioned above.
NW: The mean solar radiation data of the western walls of the courtyard in buildings A and C had a Sig value greater than 0.05, indicating that data of
these buildings are similar. B was optimized due to the lack of Western solar radiation on the courtyard walls. In the eastern walls of the courtyard, only
B, and in the southern walls of the courtyard, only C received solar radiation and was optimized.
NE: The mean solar radiation data of the courtyard’s eastern walls in A and C had a Sig value greater than 0.05, indicating that data of these buildings are
similar. In the southern walls of the courtyard, only C received solar radiation and was optimized. In the western walls of the courtyard, A and C were the
best due to not receiving solar radiation.
SW: The mean solar radiation data of the southern walls of the courtyard in A and B had a Sig value greater than 0.05, indicating that data of these
buildings are similar, and as a result, both buildings were considered optimal. The mean solar radiation data for the western walls of the courtyard,
yielded a Sig value below 0.05, signifying differences in the average solar radiation data among the buildings. C was deemed more suitable than A while
B was optimized due to not receiving Western solar radiation. In the eastern walls of the courtyard, only B received solar radiation and was optimized.
SE: The mean solar radiation data of the southern walls of the courtyard in A and B had a Sig value greater than 0.05, indicating that data of these
buildings are similar, and as a result, both buildings were considered optimal. The mean solar radiation data for the eastern walls of the courtyard,
yielded a Sig value below 0.05, signifying differences in the mean solar radiation data among the buildings and A was optimized. In the western walls of
the courtyard A and C were optimized due to the lack of western solar radiation.
Page 16/25
Table 13
Solar radiation ANOVA Test and Independent Samples Test
Solar radiation ANOVA Test Solar radiation Independent Samples Test
Sig. Sig. (2-tailed)
NW
Western Main walls
Between Groups .000 NW
Western walls of the courtyard
Equal variances assumed .361
Within Groups Equal variances not assumed .363
Total
NE
Eastern Main walls
Between Groups .000 NE
Eastern walls of the courtyard
Equal variances assumed .535
Within Groups Equal variances not assumed .527
Total
SW
Southern Main walls
Between Groups .000 SW
Western walls of the courtyard
Equal variances assumed .032
Within Groups Equal variances not assumed .032
Total
SW
Western Main walls
Between Groups .000 SW
Southern walls of the courtyard
Equal variances assumed .073
Within Groups Equal variances not assumed .074
Total
SE
Southern Main walls
Between Groups .000 SE
Eastern walls of the courtyard
Equal variances assumed .047
Within Groups Equal variances not assumed .047
Total
SE
Eastern Main walls
Between Groups .000 SE
Southern walls of the Yard
Equal variances assumed .066
Within Groups Equal variances not assumed .066
Total
The optimal unit based on the radiation parameter assessment results is presented in Table14. For instance, in the SW direction, based on statistical
analysis, the CSW unit had the most optimal amount of radiation received from the southern main walls, and the ASW and CSW units had the most optimal
amount of radiation received from the western main walls. Furthermore, in the same direction, units ASW and BSW exhibited the highest levels of solar
radiation received by the southern walls of the courtyard, while unit BSW had the highest levels of solar radiation received by the western and eastern walls of
the courtyard. Moreover, the results of comparative statistical analysis and comparative analysis in unit optimization in terms of received solar radiation
indicated a high level of consistency among the optimal units. The received solar radiation by the main walls and courtyard walls of the units complemented
each other, with the courtyard acting as a climatic modier, compensating for excess or decient solar radiation required by the units.
The EC, TC And SR Optimized Apartment
The optimal units were presented based on a simultaneous assessment of energy consumption, thermal comfort, and radiation parameters in Table15.
Considering that the best units in terms of energy consumption and thermal comfort on the fteenth day of each month, the coldest day, and the hottest day
Page 17/25
in the NW, NE, and SW directions belonged to all three apartments, none had precedence over the others, therefore determining the optimal units in these
directions was done based on the results of the analysis of solar radiation parameter variance. The post hoc T-test on the solar radiation parameter to
courtyard walls of the building showed that BNW, CNE, and BSW became the best units in their respective directions (Table15).
In the SE direction, units ASE and BSE were optimized for energy consumption and thermal comfort on the coldest day, units ASE and CSE were optimized for
thermal comfort on the hottest day, and unit ASE was optimized for solar radiation on the building walls on the 15th day of each month. As a result, in the SE
direction, with the alignment of all three parameters of energy consumption, thermal comfort, and solar radiation, unit ASE became the most frequent optimal
unit (Table15).
B. Environmental Comparison After Expansibility
In the environmental approach, the optimal units were specied based on the measurement of two dependent parameters of energy consumption, thermal
comfort, and an independent parameter of solar radiation. However, after applying the exibility technique, only two dependent parameters were examined in
the optimal units of ASE and CNE. In the exibility section, measuring the performance of the optimal unit after development in the courtyard could only be
analyzed based on these two dependent parameters.
Considering that the ASE unit was the most frequently optimized in terms of measuring three parameters, the exibility technique was applied to it, and the
per capita energy consumption of the coldest and hottest days after the expansion was simulated and compared with its value before the development. The
results indicated a reduction of 11.7% and 18.4% in energy consumption per square meter on the coldest and hottest days post-expansion, respectively
(Fig.8).
Considering the undesirable solar radiation received by the northern units, the exibility technique was also applied in unit CNE, as it had an optimal courtyard
orientation. As a result, the thermal comfort per capita of this unit was compared before and after expansion. The results showed that thermal comfort per
capita of this unit decreased by 6% after expansion on the hottest and coldest days (Fig.8).
6 Conclusion
In this research, the role of the courtyard in the apartment was investigated from two perspectives: exibility (expansibility) and environment (climate
moderation). The research question focused on how the courtyard in exible housing can contribute to microclimate regulation, make the unit
environmentally, and expansibility does not disrupt the climatic function of the courtyard. In this regard, the exibility approach was achieved by expanding
housing areas into the courtyard, while the environmental approach involved determining the optimal direction and position of the courtyard. The research
methodology of this article included the following general stages: volumetric modeling using SketchUp, simulation of environmental parameters using
EnergyPlus, measurement of meteorological data with a TES-132 data logger, and statistical analysis of variance with post hoc tests. Three exible buildings,
A, B, and C, with different courtyard positions and orientations in the city of Hamedan with a cold climate, were modeled. Subsequently, energy consumption
parameters, thermal comfort, and received solar radiation were simulated using EnergyPlus software. Data validation was conducted by comparing the
measurement and simulation results. To determine the optimal unit, one-way analysis of variance (ANOVA) with post hoc Duncan and t-test were used.
Finally, the exibility technique was applied to the optimal units, and the energy consumption and thermal comfort parameters before and after development
were compared. The research results were divided into two main approaches:environmental and exible design.
a. Environmental Comparison Before Expansibility
In the environmental approach, the optimal unit was determined based on the desired courtyard position and orientation by assessing energy consumption
parameters, thermal comfort, and solar radiation.
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-Energy Consumption (EC):The results showed that on the 15th of every month and the hottest day of the year, the mean data were similar among the
buildings, and none of the buildings had a clear advantage over the others. However, on the coldest day, in the SE direction, the post hoc Duncan test
indicated thatBuildingsA and B performed better due to lower energy consumption.
-Thermal Comfort (TC): The results also revealed that on the 15th of every month, the average data were similar among the buildings, and none of the
buildings had a clear advantage over the others. In the SE direction, the post hoc Duncan test showed that on the hottest day, Buildings A and C were optimal,
and on the coldest day, Buildings A and B were optimal.
-Solar Radiation (SR):The results regarding solar radiation on the main facades showed that on the 15th of every month, in the NW direction, Buildings A and
C were optimal, in the NE direction, Building B was optimal, in the SW direction, Building C was optimal, and in the SE direction, Buildings C and B performed
the best. In terms of solar radiation on the courtyard facades, the results indicated that on the 15th of every month, in the NW direction, Building B was
optimal, in the NE direction, Building C was optimal, in the SW direction, Building B was optimal, and in the SE direction, Building A performed the best.
The results indicated that there was no preference among the units in terms of energy consumption and thermal comfort in the NW, NE, and SW directions.
Therefore, determining the optimal units in these directions was done through the analysis of solar radiation parameters.The comparison of solar radiation
on the main walls and the courtyard showed that the courtyard acted as a climatic modier, compensating for any surplus or deciency in solar radiation
needed for the units. The follow-up T-test of the solar radiation parameter on the courtyard bodies showed that BNW, CNE, and BSW were the best units for their
direction, but in the SE direction, with the matching of all three parameters of energy consumption, thermal comfort, and radiation, the ASE unit was
optimized. In all aspects, the impact of received solar radiation on building wall as an independent variable was controlled by the courtyard as a moderating
factor. In addition to being exible, these units were environmentally compatible with optimal courtyard orientation and position (Figure 9).
b. Environmental Comparison After Expansibility
Due to the preference reasons outlined in section 3-b, the exibility technique (development) was applied to the optimal units ASE and CNE.Considering the
independence of the solar radiation parameter, only the two dependent parameters of energy consumption and thermal comfort were simulated and
compared before and after development.
-Energy Consumption (EC):The energy consumption per capita on the coldest andhottestdays for the optimal unit (ASE) decreased by 11.7% and 18.4%
before and afterexpansibility, respectively, proving the environmental sustainability of the expanded unit in terms of energy consumption.
-Thermal Comfort (TC):Similarly, the thermal comfort per capita on the hottest and coldest days for the optimal unit CNE decreased by 6% after expansion,
demonstrating the environmental sustainability of the expanded unit in terms of thermal comfort.
The comparison results of environmental parameters in the optimal units ASE and CNE before and after expansion showed that the energy consumption and
thermal comfort per capita in these units had decreased. This demonstrates that expandability was accompanied by climate-environmental eciency.It can
be concluded that in addition to being expansible,the units also had an environmental approach. Therefore, the courtyard, with its suitable orientation and
position along with the implementation of the expansion technique, continued to play a microclimate role and acted as a climate moderator with control over
solar radiation.
Declarations
Author Contribution
Literature Review: Flexibility by Mitra Ghafourian, Environmental Design by Mahsa NorouziMaterial and Method by Zahra BarzegarSimulation and
measurement by Mahsa Norouzi and Zahra BarzegarResult and Discussion by Mahsa Norouziabstract and conclusion by Mitra Ghafourian and Zahra
BarzegarFigures and tables by Mahsa Norouzimanuscript text prepration by Mahsa Norouzi and Zahra Barzegar
Data Availability
Yes, I have research data to declare. The datasets used and/or analysed during the current study available and can be send when ever wanted any where
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Figures
Figure 1
Conceptual Model of Environmentally Flexible Housing.
Figure 2
A. Case study introduction
B. The average monthly and yearly temperature of Hamedan city (Ta) in 2021-2022 based on software Meteonorm 8.4 and Thermal comfort temperature
(Tc).
Page 22/25
Figure 3
Research method process, EC: Energy Consumption, TC:Termal Comfort, SR: Solar Radiation
Page 23/25
Figure 4
Energy Consumption (Kwh) in Coldest Day and Hottest Day Of The Year.
Figure 5
PMV in coldest day of the year and 15th of every month.
Page 24/25
Figure 6
Solar Radiation (W) of Southern and Eastern Main Bodies in 15th of every month.
Figure 7
Solar Radiation (W) of Southern and Eastern Bodies of the Yard in 15th of every month.
Figure 8
Per capita energy consumption for ASE and Per capita PMV for CNE in Coldest Day Of The Year.
Page 25/25
Figure 9
Conceptual model of the conclusion