Evaluation of Design Parameters for Daylighting Performance in Secondary School Classrooms Based on Field Measurements and Physical Simulations: A Case Study of Secondary School Classrooms in Guangzhou

Abstract

The quality of natural lighting within secondary school classrooms can significantly affect the physical and mental well-being of both teachers and students. While numerous studies have explored various aspects of daylighting performance and its related factors, there is no universal standard for predicting and optimizing daylighting performance from a design perspective. In this study, a method was developed that combines measurements and simulations to enhance the design parameters associated with daylighting performance. This approach facilitates the determination of precise ranges for multiple design parameters and allows for the efficient attainment of optimal daylighting performance. Daylight glare probability (DGP), point-in-time illuminance (PIT), daylight factor (DF), and lighting energy consumption were simulated based on existing control parameters of operational classrooms. The simulation results were then validated using field measurements. Genetic algorithms (GAs) were employed to optimize the control parameters, yielding a set of optimal solutions for improving daylight performance. The differences between daylighting performance indicators corresponding to the optimal solution set and those of the basic model were compared to test the performance of the optimized parameters. The proposed method is a robust process for optimizing daylight design parameters based on GAs, which not only enhances daylighting performance but also offers scientifically grounded guidelines for the design phase. It is a valuable framework for creating healthier and more productive educational environments within secondary school classrooms.

Full text

Citation: Luo, J.; Yan, G.; Zhao, L.;
Zhong, X.; Su, X. Evaluation of Design
Parameters for Daylighting
Performance in Secondary School
Classrooms Based on Field
Measurements and Physical
Simulations: A Case Study of
Secondary School Classrooms in
Guangzhou. Buildings 2024,14, 637.
https://doi.org/10.3390/
buildings14030637
Academic Editor: Eusébio
Z.E. Conceição
Received: 13 January 2024
Revised: 14 February 2024
Accepted: 19 February 2024
Published: 28 February 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
buildings
Article
Evaluation of Design Parameters for Daylighting Performance in
Secondary School Classrooms Based on Field Measurements and
Physical Simulations: A Case Study of Secondary School
Classrooms in Guangzhou
Jianhe Luo 1,2, Gaoliang Yan 1,* , Lihua Zhao 1, Xue Zhong 1and Xinyu Su 1
1School of Architecture, State Key Laboratory of Subtropical Building and Urban Science,
South China University of Technology, Guangzhou 510641, China; 201920104230@mail.scut.edu.cn (X.Z.);
202221005577@mail.scut.edu.cn (X.S.)
2Architectural Design and Research Institute of SCUT, South China University of Technology,
Guangzhou 510641, China
*Correspondence: arleo@mail.scut.edu.cn
Abstract: The quality of natural lighting within secondary school classrooms can significantly affect
the physical and mental well-being of both teachers and students. While numerous studies have
explored various aspects of daylighting performance and its related factors, there is no universal
standard for predicting and optimizing daylighting performance from a design perspective. In this
study, a method was developed that combines measurements and simulations to enhance the design
parameters associated with daylighting performance. This approach facilitates the determination
of precise ranges for multiple design parameters and allows for the efficient attainment of optimal
daylighting performance. Daylight glare probability (DGP), point-in-time illuminance (PIT), daylight
factor (DF), and lighting energy consumption were simulated based on existing control parameters
of operational classrooms. The simulation results were then validated using field measurements.
Genetic algorithms (GAs) were employed to optimize the control parameters, yielding a set of optimal
solutions for improving daylight performance. The differences between daylighting performance
indicators corresponding to the optimal solution set and those of the basic model were compared
to test the performance of the optimized parameters. The proposed method is a robust process
for optimizing daylight design parameters based on GAs, which not only enhances daylighting
performance but also offers scientifically grounded guidelines for the design phase. It is a valuable
framework for creating healthier and more productive educational environments within secondary
school classrooms.
Keywords: daylighting performance indicators; secondary school classroom; design parameters;
physical simulation; field measurements
1. Introduction
The secondary-school teaching building serves as the central hub for daily teaching
activities within a secondary school. A well-designed indoor daylight environment in such
buildings can enhance learning efficiency, promote the physical and mental wellness of
both students and educators, and reduce the prevalence of myopia [
1
–
5
]. Natural light
provides the necessary illumination for teachers and students to do their work, while also
introducing thermal radiation into the indoor space. Inappropriate thermal radiation levels
can result in indoor temperatures and air quality exceeding human comfort thresholds, as
well as cause eye and upper respiratory tract irritation, headaches, fatigue, drowsiness, and
even breathing difficulties or asthma. Additionally, the quality of the indoor environment
can affect students’ attention and memory, thereby indirectly impacting their learning
Buildings 2024,14, 637. https://doi.org/10.3390/buildings14030637 https://www.mdpi.com/journal/buildings

Buildings 2024,14, 637 2 of 23
efficiency [
6
–
10
]. Adverse health issues stemming from subpar indoor environmental
conditions can diminish performance and learning efficiency.
Today, the myopia rate among Chinese adolescents exceeds 50% [
11
]. Contemporary
medical research indicates that short-wavelength blue light found in natural daylight can
promote healthy eye development, with the quantity of natural light received during
adolescence affecting the likelihood of myopia [
12
]. Natural light provides a high-quality
light source for vision, which enhances color perception and visual performance, helping
students observe details more effectively. Therefore, a favorable natural light environment
is an essential factor in preventing myopia among teenagers. Considering the substantial
impact of natural lighting on the health and learning efficiency of teachers and students,
research into the daylighting environment has considerable significance.
Moreover, properly managing the relationship between buildings and the environment
can reduce energy consumption and carbon emissions [
13
,
14
]. By the end of 2021, China
had a total of 52,900 junior high schools with 50.1844 million students, occupying a building
area of 75.5937 million square meters. There were 14,600 regular senior high schools with
26.0503 million students, occupying a building area of 64.3621 million square meters. These
figures exclude special education, vocational education, and private education [
15
]. As the
country’s educational infrastructure has improved, the continual expansion of building
footprints has increased the energy consumption and carbon emissions of buildings. From
2006 to 2030, the energy demand for educational buildings is expected to grow at an
annual rate of 1.2%. Currently, primary and secondary school buildings account for 7% of
China’s total public building energy consumption. The energy consumption per unit area
in secondary schools is two to three times that of typical urban residential buildings [16].
The depth of typical Chinese secondary school classrooms is considerable, despite the
presence of exterior corridors. Though windows line both sides of the classroom, the central
area is not sufficiently illuminated and daylighting across the room is uneven. Due to a lack
of experience and guidance in classroom daylighting design, certain educational buildings
are designed without thorough consideration of the building’s orientation, leading to ex-
cessive window area. This oversupply of windows results in overheating and glare during
summer months. The resulting reliance on artificial lighting to compensate for insufficient
natural light not only fails to conserve energy but also creates unfavorable conditions for
adolescents’ visual health. There are currently approximately 600 million myopia cases in
China, with the rates of visual impairment among primary school students (ages 7–12),
middle school students (ages 13–15), and high school students (ages 16–18) reaching 45.71%,
74.36%, and 83.28%, respectively [
11
]. The prevalence of visual impairment among middle
school students is particularly high, warranting close attention.
Recent medical research has highlighted the role of short-wavelength blue light in
natural light promoting eyeball development [
17
]. Adolescents who are exposed to more
natural light during their growth are less likely to develop myopia [
18
]. Proposed classroom
lighting standards recommend an average indoor illuminance of at least 300 lux, with a
natural light design illuminance of at least 450 lux, and an average daylight factor of 3%
at minimum [
19
]. While a window-to-floor area ratio of no less than 20% is suggested in
design guidelines, other directives affecting indoor natural lighting seem to be lacking.
Researching the indoor lighting environment of secondary school classrooms can
address the high rate of myopia among students while also filling gaps in lighting design
guidelines and advancing energy-saving and emission reduction objectives. The design
optimization can enhance indoor lighting comfort and decrease energy consumption; ef-
fective natural daylighting conserves energy while also mitigating solar radiation effects,
consequently lowering the emissions from air conditioning. Therefore, successful daylight-
ing design should not only introduce ample natural light but also address concerns related
to glare and overheating.
Architectural design shapes the quality of indoor daylighting, with various design
control parameters influencing the distribution and intensity of daylight. In today’s ar-
chitectural design process, the optimization of building daylighting performance mainly

Buildings 2024,14, 637 3 of 23
revolves around optimizing the parameters of enclosure components such as the window-
to-wall ratio (WWR), window materials, and shading materials during the construction
drawing phase. By altering WWR, the amount of daylight entering the interior can be
adjusted, thus affecting indoor illuminance [
20
]. Increasing facade shading can reduce the
amount of daylight entering the interior, thereby lowering the DGP value [
21
]. Ensuring
sufficient indoor light when adjusting both windows and blinds as necessary can prevent
overheating while conserving energy [
22
]. Some studies have also focused on existing
buildings and lighting systems, conducting physical environment data tests to summarize
energy consumption patterns [23–26].
Previous researchers have generally conducted performance predictions after a design
scheme has already been determined, or focused on the outcomes of building performance
predictions, often overlooking the scientific exploration of acquiring measurement data
and the selection and assessment of relevant parameters.
Performance optimization design should begin during the architectural design phase,
considering the overall design for daylight in secondary school classrooms. This process
should center not only on building components but also integrate various factors, such
as building orientation, facade elements, and the optical properties of interior materials,
in accordance with regulations. It is important to emphasize that the design of window
openings and walls between windows is subject to regulatory constraints; the latter cannot
serve as a substitute for casement requirements. Enhancing performance should be a design
objective in the early stages of the design process [
27
] and isolating the control parameters
that affect daylighting performance is crucial. The number and type of control parameters
can influence the accuracy and complexity of such models.
There are many control parameters that can influence natural daylighting in buildings,
including building orientation, WWR, shading form, room dimensions, the reflectance
coefficients of interior surfaces, and the position, size, and visible light transmittance of
windows. These control parameters can affect various daylighting performance indicators,
which serve as predicted target parameters. These indicators encompass elements like
the magnitude and distribution of indoor illuminance, DF, luminance, glare, and light-
ing energy consumption. The building’s control parameters should be oriented towards
building performance, health, and comfort throughout the entire design phase, resulting in
architectural expressions that harmoniously integrate regional characteristics adapted to
the local climate and environment. To this effect, design control parameters significantly
impact the daylighting environment and lighting energy consumption.
In simulations of daylight performance, commonly used evaluation indexes are day-
light autonomy (DA) or spatial daylight autonomy (sDA), as well as useful daylight
illuminance (UDI) as recommended for climate-based daylight modeling (CBDM). These
metrics aid in assessing how well an indoor space is lit by natural light. Reinhart [
28
] sum-
marized the limitations of traditional, static daylight performance metrics and introduced
the concept of dynamic daylight performance metrics, which can more effectively reflect
the interactions between a building, its occupants, and the climate. This information assists
in making sound design-related decisions regarding daylight illuminance. CBDM metrics
can be employed to assess indoor daylight levels [
29
,
30
], primarily reflecting the average
daylight conditions on the horizontal plane. For non-horizontal surfaces, other metrics
can be utilized to evaluate vertical illuminance and real-time illuminance as per the actual
circumstances at hand.
Currently, there are two methods primarily used to predict building performance using
design control parameters: physical modeling and data-driven approaches [
31
]. Physical
modeling, or “white-box” modeling [
32
], relies on thermodynamic principles and the use of
simulation software to make performance predictions [
33
,
34
]. It operates on the principle
of predicting building behavior based on the physical properties of simulated objects,
with mathematical equations of building performance operations as a simulation engine.
These physical properties are usually derived from design plans, product specifications, or
field measurements. In contrast, data-driven methods, or “black-box” modeling, utilizes

Buildings 2024,14, 637 4 of 23
historical empirical data to predict building performance [
35
]. The accuracy of black-
box models depends on extended training periods and comprehensive datasets. Both
performance-prediction methods are significantly influenced by the selection of input
parameters, the quality of recorded measured data, and the specific algorithms utilized [
36
].
Both methods also require the collection of physical measurements; however, the objectives
of these measurements differ.
Physical modeling requires measured data for comparison and validation against
simulation results, a crucial step in assessing the accuracy of predictions [
37
]. Data-driven
approaches necessitate measuring data prior to making predictions to create a dataset for
training the prediction model. For physical modeling, the conditions in which measure-
ments are taken (e.g., weather, room shape, material selections) should closely match the
simulated objects. Data-driven methods, conversely, may be based on measurements from
similar types of buildings, gathered with less emphasis on specific building parameters
while prioritizing variables such as temperature, humidity, solar radiation, illuminance,
luminance, and energy consumption over a specific time period. The measurement data
from the two prediction methods all reflect the selection of parameters relevant to building
performance under a given condition; both also involve determining the accuracy of the
performance predictions. Considering the respective principles and characteristics of these
two performance prediction methods, physical modeling was better suited to the purposes
of this study.
In accordance with the above considerations, this study began with an investigation on
middle school classrooms with comparable control parameters. Measurements, simulations,
and optimization processes for daylighting performance were conducted. The primary
aim was to identify important parameters related to daylighting performance and to use
GAs to optimize these parameters. Design strategies were formulated accordingly to
improve daylighting performance in middle school buildings. The results of this work may
provide valuable insights for researchers and designers to better understand the parameters
governing daylighting performance in schools, and for architectural designs that seamlessly
integrate performance predictions.
2. Methodology
2.1. Basic Information
The case-study classroom is located in the junior high school section of Guangya
Middle School in Huadu District, Guangzhou, China. Guangzhou is situated in southern
China (112
◦
57
′
~114
◦
3
′
E, 22
◦
26
′
~23
◦
56
′
N), downstream of the Pearl River and near the
South China Sea. The Tropic of Cancer crosses its northern region. Guangzhou has
a maritime subtropical monsoon climate characterized by warm and rainy conditions,
abundant sunshine, long summers, and brief frost periods. It falls under category IV of
China’s solar climate classification [
38
]. The location of this case corresponds to the solar
climate classification zone shown in Figure 1.
The monthly average solar climate conditions in Guangzhou are described in
Table 1[39]
.
Table 1. Monthly daylight climate averages, Guangzhou.
Month
January
February
March April May June July August
September
October
November December
Sunshine duration (h) 119 71.6 62.4 65.1 104 140 202 174 170 181 173 166
Solar radiation
(kWh/m2)85 67.5 74.4 83.6 108 116 141 136 123 122 105 93.1
Ghangzhou receives a total of 1628 annual sunshine hours, with solar radiation ranging
from 1213.1 to 1277 kWh/m
2
. The distribution of solar radiation is higher in the south
and lower in the north. The most intense solar radiation occurs in June and the weakest
is in February. The city has an average annual temperature of 22.8
◦
C and a relative
humidity of 73%. Middle school teaching typically takes place from February to July and
from September to the following January. High temperature, high humidity, and intense

Buildings 2024,14, 637 5 of 23
radiation in the environment can reduce both comfort and learning efficiency for teachers
and students. At times when the solar altitude angle is low, radiation from the east and
west directions can also cause indoor glare problems.
Buildings2024,14,xFORPEERREVIEW4of24
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objects,withmathematicalequationsofbuildingperformanceoperationsasasimulation
engine.Thesephysicalpropertiesareusuallyderivedfromdesignplans,productspecifi-
cations,orfieldmeasurements.Incontrast,data-drivenmethods,or“black-box”model-
ing,utilizeshistoricalempiricaldatatopredictbuildingperformance[35].Theaccuracy
ofblack-boxmodelsdependsonextendedtrainingperiodsandcomprehensivedatasets.
Bothperformance-predictionmethodsaresignificantlyinfluencedbytheselectionofin-
putparameters,thequalityofrecordedmeasureddata,andthespecificalgorithmsuti-
lized[36].Bothmethodsalsorequirethecollectionofphysicalmeasurements;however,
theobjectivesofthesemeasurementsdiffer.
Physicalmodelingrequiresmeasureddataforcomparisonandvalidationagainst
simulationresults,acrucialstepinassessingtheaccuracyofpredictions[37].Data-driven
approachesnecessitatemeasuringdatapriortomakingpredictionstocreateadatasetfor
trainingthepredictionmodel.Forphysicalmodeling,theconditionsinwhichmeasure-
mentsaretaken(e.g.,weather,roomshape,materialselections)shouldcloselymatchthe
simulatedobjects.Data-drivenmethods,conversely,maybebasedonmeasurementsfrom
similartypesofbuildings,gatheredwithlessemphasisonspecificbuildingparameters
whileprioritizingvariablessuchastemperature,humidity,solarradiation,illuminance,
luminance,andenergyconsumptionoveraspecifictimeperiod.Themeasurementdata
fromthetwopredictionmethodsallreflecttheselectionofparametersrelevanttobuilding
performanceunderagivencondition;bothalsoinvolvedeterminingtheaccuracyofthe
performancepredictions.Consideringtherespectiveprinciplesandcharacteristicsof
thesetwoperformancepredictionmethods,physicalmodelingwasbeersuitedtothe
purposesofthisstudy.
Inaccordancewiththeaboveconsiderations,thisstudybeganwithaninvestigation
onmiddleschoolclassroomswithcomparablecontrolparameters.Measurements,simu-
lations,andoptimizationprocessesfordaylightingperformancewereconducted.Thepri-
maryaimwastoidentifyimportantparametersrelatedtodaylightingperformanceand
touseGAstooptimizetheseparameters.Designstrategieswereformulatedaccordingly
toimprovedaylightingperformanceinmiddleschoolbuildings.Theresultsofthiswork
mayprovidevaluableinsightsforresearchersanddesignerstobeerunderstandthepa-
rametersgoverningdaylightingperformanceinschools,andforarchitecturaldesignsthat
seamlesslyintegrateperformancepredictions.
2.Methodology
2.1.BasicInformation
Thecase-studyclassroomislocatedinthejuniorhighschoolsectionofGuangyaMid-
dleSchoolinHuaduDistrict,Guangzhou,China.Guangzhouissituatedinsouthern
China(112°57′~114°3′ E,22°26′~23°56′N),downstreamofthePearlRiverandnearthe
SouthChinaSea.TheTropicofCancercrossesitsnorthernregion.Guangzhouhasamar-
itimesubtropicalmonsoonclimatecharacterizedbywarmandrainyconditions,abundant
sunshine,longsummers,andbrieffrostperiods.ItfallsundercategoryIVofChina’ssolar
climateclassification[38].Thelocationofthiscasecorrespondstothesolarclimateclassi-
ficationzoneshowninFigure1.

Figure 1. China daylight climate zones and location of study area. The first image depicts the
location of the research cases in China and China’s daylight climate zones. Different colors represent
different daylight climate zones, with the research cases located in Zone IV. The two image illustrates
the location of the research building within the campus and its surrounding environment, with
yellow blocks representing the research building. The final image shows the facade conditions of the
research building.
The construction of the junior high school section of Guangya Middle School in Huadu
District, Guangzhou, comprises a five-story reinforced concrete structure with a compact
layout, facing 3◦18′east of south. The building has a length of 124.5 m (408.46 ft), a width
of 86.1 m (282.48 ft), and a height of 21.9 m (71.85 ft), covering a total area of 27,336 m
2
(294,268 sq. ft). The building is divided into two parts: the east side is a teaching area and
the focus of this research, while the west side is a laboratory that falls outside of the scope
of this study (Figure 2a). The external configuration of the teaching building resembles
the letter “E” in shape, with classrooms connected in a linear pattern. The ground floor is
elevated, serving as a flexible space for various activities, professional communications,
socializing, and relaxation.
Buildings2024,14,xFORPEERREVIEW6of24
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Figure2.Functionalzoningofjuniorhighschoolsection,GuangyaMiddleSchool;locationsofthree
classroomsunderanalysis.Image(a)illustratesthetwosectionsoftheschool(classrooms,labora-
tory).Image(b)indicatesthethreerepresentativeclassroomschosenforanalysis.
ThegeometricaributesofthethreeclassroomsareillustratedinFigure3.External
sidewindowsareeach0.9minheightwhilesidewindowsfacingthecorridorare1.5m
inheight.InClassroomA,roofeavesprovideshading;theyhavethesameheightasthe
roofslabandproject2.0moutward.InClassroomsBandC,horizontalshadingdevices
madeofbrownmetalarepositionedat2/3ofthewindowheight.Theshadingdevices
havethesamewidthasthewindows,withathicknessof0.2mandaprojectionof1.1m.
Thetopoftheshadingdevicesenhancesthediffusionoflightandimprovesindoorday-
lightinguniformitybyreflectinglightontotheinteriorceiling.Theboomoftheshading
devicesweakensdirectsunlight,preventingexcessivelystrongilluminanceintheareas
closetothewindows.
Inallthreeclassrooms,thewindowsarefurnishedwithdouble-layerinsulatedtrans-
parentglass(6+12air+6)andaredesignedtoslideopen.Theinteriorceilingiscoated
withwhiteemulsionpaintandsurfacesbelow1.5marecoveredwithwhitemaetiles.
Theupperportionsofwalls,beyond1.5m,arealsocoatedwithwhiteemulsionpaint.The
flooringconsistsofdarkgreymaetiles.Theclassroomsareequippedwithamulti-split
centralairconditioningsystem.Otherenergy-consumingequipmentincludeslighting,
fans,multimediadevices,andchargingcabinetsforstudenttablets.Learninghoursinthe
teachingbuildingarescheduledonweekdaysfrom8:00–12:20,14:05–17:40,and19:00–
22:10.

Figure3.Geometricdimensionsofthreeclassrooms.
Themainparametersaffectingnaturaldaylightinginthethreeclassroomswerecom-
pared,asdescribedinTabl e2.
Tab l e 2.Comparisonofgeometricparametersandopticalpropertiesofthreeclassrooms.
OrientationHeight(m)RoomSize(m)Windows
No.
Wallbetween
Windows(m)
ShadeOverhang
Width(m)
Windows
Transmittance
ClassroomAS13.6(45ft)12.3×8.25×4(40
×27×13ft)40.5Eave2.00.7
Figure 2. Functional zoning of junior high school section, Guangya Middle School; locations of
three classrooms under analysis. Image (a) illustrates the two sections of the school (classrooms,
laboratory). Image (b) indicates the three representative classrooms chosen for analysis.
Three classrooms with distinct parameters from the teaching area were selected from
the junior high school section for field testing and performance simulation. These class-
rooms have varying orientations and heights. As shown in Figure 2b, Classroom A is
located in the middle of the fourth floor on the south side of the building, where south-
facing daylight conditions are favorable. Classroom B is situated in the middle of the
third floor on the north side of the building, which mainly receives north-facing daylight.

Buildings 2024,14, 637 6 of 23
Classroom C is on the second floor in the central part of the building, where it receives
daylight from both the north and south courtyards. All three classrooms are aligned along
the same longitudinal axis and have a height difference of 4 m.
The geometric attributes of the three classrooms are illustrated in Figure 3. External
side windows are each 0.9 m in height while side windows facing the corridor are 1.5 m in
height. In Classroom A, roof eaves provide shading; they have the same height as the roof
slab and project 2.0 m outward. In Classrooms B and C, horizontal shading devices made
of brown metal are positioned at 2/3 of the window height. The shading devices have the
same width as the windows, with a thickness of 0.2 m and a projection of 1.1 m. The top
of the shading devices enhances the diffusion of light and improves indoor daylighting
uniformity by reflecting light onto the interior ceiling. The bottom of the shading devices
weakens direct sunlight, preventing excessively strong illuminance in the areas close to
the windows.
Buildings2024,14,xFORPEERREVIEW6of24
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
Figure2.Functionalzoningofjuniorhighschoolsection,GuangyaMiddleSchool;locationsofthree
classroomsunderanalysis.Image(a)illustratesthetwosectionsoftheschool(classrooms,labora-
tory).Image(b)indicatesthethreerepresentativeclassroomschosenforanalysis.
ThegeometricaributesofthethreeclassroomsareillustratedinFigure3.External
sidewindowsareeach0.9minheightwhilesidewindowsfacingthecorridorare1.5m
inheight.InClassroomA,roofeavesprovideshading;theyhavethesameheightasthe
roofslabandproject2.0moutward.InClassroomsBandC,horizontalshadingdevices
madeofbrownmetalarepositionedat2/3ofthewindowheight.Theshadingdevices
havethesamewidthasthewindows,withathicknessof0.2mandaprojectionof1.1m.
Thetopoftheshadingdevicesenhancesthediffusionoflightandimprovesindoorday-
lightinguniformitybyreflectinglightontotheinteriorceiling.Theboomoftheshading
devicesweakensdirectsunlight,preventingexcessivelystrongilluminanceintheareas
closetothewindows.
Inallthreeclassrooms,thewindowsarefurnishedwithdouble-layerinsulatedtrans-
parentglass(6+12air+6)andaredesignedtoslideopen.Theinteriorceilingiscoated
withwhiteemulsionpaintandsurfacesbelow1.5marecoveredwithwhitemaetiles.
Theupperportionsofwalls,beyond1.5m,arealsocoatedwithwhiteemulsionpaint.The
flooringconsistsofdarkgreymaetiles.Theclassroomsareequippedwithamulti-split
centralairconditioningsystem.Otherenergy-consumingequipmentincludeslighting,
fans,multimediadevices,andchargingcabinetsforstudenttablets.Learninghoursinthe
teachingbuildingarescheduledonweekdaysfrom8:00–12:20,14:05–17:40,and19:00–
22:10.

Figure3.Geometricdimensionsofthreeclassrooms.
Themainparametersaffectingnaturaldaylightinginthethreeclassroomswerecom-
pared,asdescribedinTabl e2.
Tab l e 2.Comparisonofgeometricparametersandopticalpropertiesofthreeclassrooms.
OrientationHeight(m)RoomSize(m)Windows
No.
Wallbetween
Windows(m)
ShadeOverhang
Width(m)
Windows
Transmittance
ClassroomAS13.6(45ft)12.3×8.25×4(40
×27×13ft)40.5Eave2.00.7
Figure 3. Geometric dimensions of three classrooms.
In all three classrooms, the windows are furnished with double-layer insulated trans-
parent glass (6 + 12 air + 6) and are designed to slide open. The interior ceiling is coated
with white emulsion paint and surfaces below 1.5 m are covered with white matte tiles.
The upper portions of walls, beyond 1.5 m, are also coated with white emulsion paint. The
flooring consists of dark grey matte tiles. The classrooms are equipped with a multi-split
central air conditioning system. Other energy-consuming equipment includes lighting,
fans, multimedia devices, and charging cabinets for student tablets. Learning hours in the
teaching building are scheduled on weekdays from 8:00–12:20, 14:05–17:40, and 19:00–22:10.
The main parameters affecting natural daylighting in the three classrooms were com-
pared, as described in Table 2.
Table 2. Comparison of geometric parameters and optical properties of three classrooms.
Orientation Height (m) Room Size (m) Windows No. Wall between
Windows (m)
Shade Overhang
Width (m)
Windows
Transmittance
Classroom A S 13.6 (45 ft) 12.3 ×8.25 ×4
(40 ×27 ×13 ft) 4 0.5 Eave2.0 0.7
Classroom B N 9.6 (31.5 ft) 11.6 ×8.55 ×4
(38 ×28 ×13 ft) 2 1.0 1.1 0.7
Classroom C S 5.6 (18 ft) 11.6 ×8.55 ×4
(38 ×28 ×13 ft) 2 1.0 1.1 0.7
2.2. Workflow
A framework for the evaluation and optimization of building daylighting performance
was developed in this study based on field testing and physical modeling. The specific
workflow (Figure 4) has four main steps.

Buildings 2024,14, 637 7 of 23
Buildings2024,14,xFORPEERREVIEW7of24
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ClassroomBN9.6(31.5ft)11.6×8.55×4(38
×28×13ft)21.01.10.7
ClassroomCS5.6(18ft)11.6×8.55×4(38
×28×13ft)21.01.10.7
2.2.Workflow
Aframeworkfortheevaluationandoptimizationofbuildingdaylightingperfor-
mancewasdevelopedinthisstudybasedonfieldtestingandphysicalmodeling.The
specificworkflow(Figure4)hasfourmainsteps.
Step1:Usingaparametricplatformforphysicalmodeling,establishclassroommod-
elswithdifferentparametersandcomparablecharacteristics.
Step2:Utilizetheperformancesimulationprogram“LadybugTools”tooperatethe
simulationmodels,obtainingdaylightingperformancesimulationresults.
Step3:Conductfieldmeasurementsonthesimulatedclassroomsandcomparethe
simulationresultswiththeactualtestdatatoanalyzetheircommonfeaturesanddiffer-
ences.
Step4:Accordingtothesecomparisons,identifysignificantcontrolparametersand
theirreasonablerangesthataffectclassroomdaylighting.Thesecontrolparameterscan
thenbeusedasoptimizationcontrolfactors.Daylightingperformanceindicatorsserveas
objectiveparametersforanoptimizationcalculation.ByemployingaGA,wecanobtain
theoptimalsolutionsetandsummarizethedesignstrategiesforoptimizingthebuilding’s
daylighting.
Rhino model
DesignBuilder
mode l
Building ph ysica l
mode l
Weathe r fi le
Location
Lad ybu g tool s
Radiance
Reflectance factor
Radiance
parameters
CIE Sky model
Simulation
Validation
Simulated
illuminance
Measured
illminance
Multi-objective
optimization
Decisio n-
maki n g

Figure4.Overviewofresearchmethodology.
2.2.1.Modeling
Inthisstudy,Rhino7.26/Grasshopper1.0softwarewasusedformodeling.Adigital
analysismodelwascreatedbasedondesigndrawingsofthemiddleschoolteaching
buildinganddatacollectedfromfieldsurveys.Externaldecorativeelementsthathadno
impactondaylightingweresimplified.Theorientationandgeometricdimensionsofthe
classrooms,performanceparametersofthebuildingenvelope,opticalcharacteristicsof
variousmaterialinterfaces,andtheoperatingscheduleofbuildingequipmentwereallset
accordingtotheactualon-siteconditions.
2.2.2.DaylightingPerformanceSimulation
Theavailabilityofskyconditionsisaprerequisiteforconductingdaylightingsimu-
lations.TheEPW-formatmeteorologicaldataforGuangzhouprovidedbytheEnergyPlus
officialwebsite,whichprovidesaskymodelunderspecificconditions,wasusedforthe
purposesofthisstudy.Indoordaylightingevaluationstandardsaremostlybasedon
Figure 4. Overview of research methodology.
Step 1: Using a parametric platform for physical modeling, establish classroom models
with different parameters and comparable characteristics.
Step 2: Utilize the performance simulation program “Ladybug Tools” to operate the
simulation models, obtaining daylighting performance simulation results.
Step 3: Conduct field measurements on the simulated classrooms and compare the sim-
ulation results with the actual test data to analyze their common features and differences.
Step 4: According to these comparisons, identify significant control parameters and
their reasonable ranges that affect classroom daylighting. These control parameters can
then be used as optimization control factors. Daylighting performance indicators serve
as objective parameters for an optimization calculation. By employing a GA, we can
obtain the optimal solution set and summarize the design strategies for optimizing the
building’s daylighting.
2.2.1. Modeling
In this study, Rhino 7.26/Grasshopper 1.0 software was used for modeling. A digital
analysis model was created based on design drawings of the middle school teaching
building and data collected from field surveys. External decorative elements that had no
impact on daylighting were simplified. The orientation and geometric dimensions of the
classrooms, performance parameters of the building envelope, optical characteristics of
various material interfaces, and the operating schedule of building equipment were all set
according to the actual on-site conditions.
2.2.2. Daylighting Performance Simulation
The availability of sky conditions is a prerequisite for conducting daylighting sim-
ulations. The EPW-format meteorological data for Guangzhou provided by the Energy
Plus official website, which provides a sky model under specific conditions, was used for
the purposes of this study. Indoor daylighting evaluation standards are mostly based on
illuminance and can be divided by whether they are static or dynamic criteria. There are
various dynamic daylighting evaluation standards available. Dynamic metrics that can be
compared with real-time measurements include the PIT and DGP, which accurately reflect
illuminance and glare conditions at specific time points.
CBDM simulations typically rely on two indicators, DA (or sDA) and UDI, to evaluate
lighting performance. However, these indicators do not reflect real-time illumination
and cannot be compared with experimental data. The most widely used static daylight
evaluation standard, the DF, was utilized in this study. DF measurements can be used
to assess the building’s spatial form, the geometric dimensions of openings, shading
strategies, and the optical properties of interior and exterior surfaces. Additionally, during

Buildings 2024,14, 637 8 of 23
each illumination energy simulation, artificial lighting was activated when the natural
daylight illuminance on the desktop fell below 450 lux [
40
]. Ladybug Tools were used
to simulate the DF, PIT, DGP, and lighting energy consumption of the three case-study
classrooms. The simulation time for each performance indicator was synchronized with
the field measurement time.
2.2.3. Testing Tools and Methods
Data collection in this study involved illuminance measurements, luminance measure-
ment, and lighting electrical energy monitoring. A TES1330A (by TES Electrical Electronic
Corp. The company is based in Taipei, Taiwan) portable illuminance meter was used for
illuminance measurements (Figure 5a), a Konica Minolta LS-110 (Konica Minolta Sensing
Americas Inc., Ramsey, NJ, USA) was used for luminance measurements (Figure 5b), and a
Pilot SPM91 electric meter (Zhuhai Pilot Technology Co., Ltd., Zhuhai, China) was used to
sub-meter the lighting electrical energy in the classrooms (Figure 5c). The key parameters
of these three testing tools are listed in Table 3.
Buildings 2024, 14, x FOR PEER REVIEW 8 of 24
illuminance and can be divided by whether they are static or dynamic criteria. There are
various dynamic daylighting evaluation standards available. Dynamic metrics that can be
compared with real-time measurements include the PIT and DGP, which accurately re-
flect illuminance and glare conditions at specific time points.
CBDM simulations typically rely on two indicators, DA (or sDA) and UDI, to evalu-
ate lighting performance. However, these indicators do not reflect real-time illumination
and cannot be compared with experimental data. The most widely used static daylight
evaluation standard, the DF, was utilized in this study. DF measurements can be used to
assess the building’s spatial form, the geometric dimensions of openings, shading strate-
gies, and the optical properties of interior and exterior surfaces. Additionally, during each
illumination energy simulation, artificial lighting was activated when the natural daylight
illuminance on the desktop fell below 450 lux [40]. Ladybug Tools were used to simulate
the DF, PIT, DGP, and lighting energy consumption of the three case-study classrooms.
The simulation time for each performance indicator was synchronized with the field meas-
urement time.
2.2.3. Testing Tools and Methods
Data collection in this study involved illuminance measurements, luminance meas-
urement, and lighting electrical energy monitoring. A TES1330A (by TES Electrical Elec-
tronic Corp. The company is based in Taipei, Taiwan) portable illuminance meter was
used for illuminance measurements (Figure 5a), a Konica Minolta LS-110 was used for
luminance measurements (Figure 5b), and a Pilot SPM91 electric meter was used to sub-
meter the lighting electrical energy in the classrooms (Figure 5c). The key parameters of
these three testing tools are listed in Table 3.
Figure 5. Instruments used in this study. Image (a) displays a TES1330A illuminance meter, (b)
shows a Konica Minolta LS-110 luminance meter, and (c) shows a Pilot SPM91 electric energy meter.
Table 3. Main parameters of three test tools.
TES1330A Konica Minolta LS-110 Pilot SPM91
Measuring range 0.01–20,000 Lux Fast: 0.01–999,900 cd/m2, Slow: 0.01–499,900 cd/m2 999,999.9 kWh
Accuracy ±3%rdg ± 0.5%f.s 0.01–9.99 cd/m2 ± 2% ± 2 value, >10.00 cd/m2 ± 2% ± 1 value kWh Class 1.0
Illuminance testing was conducted following the Indoor Illuminance Testing Guide
by the Illuminating Engineering Society of North America (IESNA) [41]. The test was per-
formed on 18 May 2023, from 12:00 to 13:00 under partly cloudy sky conditions. The in-
door scenario involved drawing open the curtains and ensuring that artificial lighting re-
mained switched off throughout the testing process. The test area was divided into a grid
of 1 m × 1 m squares and measurements were taken at 0.8 m above the ground. The ar-
rangement of measurement points is illustrated in Figure 6.
Figure 5. Instruments used in this study. Image (a) displays a TES1330A illuminance meter, (b) shows
a Konica Minolta LS-110 luminance meter, and (c) shows a Pilot SPM91 electric energy meter.
Table 3. Main parameters of three test tools.
TES1330A Konica Minolta LS-110 Pilot SPM91
Measuring range 0.01–20,000 Lux Fast: 0.01–999,900 cd/m2, Slow:
0.01–499,900 cd/m2999,999.9 kWh
Accuracy ±3%rdg ±0.5%f.s 0.01–9.99 cd/m2±2% ±2 value,
>10.00 cd/m2±2% ±1 value kWh Class 1.0
Illuminance testing was conducted following the Indoor Illuminance Testing Guide
by the Illuminating Engineering Society of North America (IESNA) [
41
]. The test was
performed on 18 May 2023, from 12:00 to 13:00 under partly cloudy sky conditions. The
indoor scenario involved drawing open the curtains and ensuring that artificial lighting
remained switched off throughout the testing process. The test area was divided into a
grid of 1 m
×
1 m squares and measurements were taken at 0.8 m above the ground. The
arrangement of measurement points is illustrated in Figure 6.
The average indoor illuminance was calculated by Equation (1). DF, which represents
the illuminance level at a specific point indoors and is used to evaluate the daylighting
level in different areas of a building, was calculated by Equation (2).

Buildings 2024,14, 637 9 of 23
Buildings2024,14,xFORPEERREVIEW9of24

TheaverageindoorilluminancewascalculatedbyEquation(1).DF,whichrepresents
theilluminancelevelataspecificpointindoorsandisusedtoevaluatethedaylighting
levelindifferentareasofabuilding,wascalculatedbyEquation(2).
Averageilluminance(Eavg)iscalculatedasfollows:
E 󰇛EE⋯E󰇜85
⁄(1)
whereErepresentstheilluminancemeasuredateachpoint;thereare85measurement
pointsineachclassroomunderanalysishere.
DFiscalculatedasfollows:
DF 󰇛EE
⁄󰇜100%(2)
whereEnrepresentstheilluminanceofaspecificpointonagivenindoorplaneunder
diffuseskyillumination,andEwrepresentstheilluminanceoftheunobstructedhorizontal
planeoutdoorsatthesametimeandlocationastheindoorpointunderdiffuseskyillu-
mination.

Figure6.Illuminancemeasurementpointarrangements.Themeasuringpointsarespacedatahor-
izontaldistanceof1m,withatotalof85measuringpointsineachofthethreeclassrooms.
Thereflectanceandtransmiancecoefficientsofvariousmaterialsurfacesinthe
testedclassroomsweredeterminedusingEquations(3)and(4),respectively[42].Thevis-
iblelighttransmiancecoefficientoftheclassroomwindowsinthisstudyis0.70(witha
maximumvalueof1).Themeasuredreflectancecoefficientsfordifferentinteriorsurfaces
andtheindustry-recognizedstandardreflectancecoefficientsarelistedinTable4[19].The
reflectancecoefficientsofinteriorsurfaceshaveacertainimpactonindoordaylighting
andareimportantparametersfordaylightingsimulations.Themeasuredmaterialreflec-
tanceandtransmiancecoefficientswereusedthroughoutthesimulationprocess,with
anoverallenvironmentalreflectancecoefficientof0.2.
Thereflectancecoefficientρcanbecalculatedasfollows:
ρ  E
E
⁄ (3)
andthetransmiancecoefficientτas:τEE
⁄(4)
whereErepresentstheincidentilluminance,Eρisthereflectedilluminance,andEτisthe
transmiedilluminance.

Figure 6. Illuminance measurement point arrangements. The measuring points are spaced at a
horizontal distance of 1 m, with a total of 85 measuring points in each of the three classrooms.
Average illuminance (Eavg ) is calculated as follows:
Eavg =(E1+E2+. . . +E85)/85 (1)
where E represents the illuminance measured at each point; there are 85 measurement
points in each classroom under analysis here.
DF is calculated as follows:
DF =(En/Ew)×100% (2)
where E
n
represents the illuminance of a specific point on a given indoor plane under diffuse
sky illumination, and E
w
represents the illuminance of the unobstructed horizontal plane
outdoors at the same time and location as the indoor point under diffuse sky illumination.
The reflectance and transmittance coefficients of various material surfaces in the tested
classrooms were determined using Equations (3) and (4), respectively [
42
]. The visible
light transmittance coefficient of the classroom windows in this study is 0.70 (with a
maximum value of 1). The measured reflectance coefficients for different interior surfaces
and the industry-recognized standard reflectance coefficients are listed in Table 4[
19
]. The
reflectance coefficients of interior surfaces have a certain impact on indoor daylighting and
are important parameters for daylighting simulations. The measured material reflectance
and transmittance coefficients were used throughout the simulation process, with an overall
environmental reflectance coefficient of 0.2.
Table 4. Reflectance factors.
Surface
Ceiling Wall
(Whitewash)
Wall (Tile) Floor Window Door Shade
Reflectance
factor
Standard 0.7–0.8 0.5–0.6 0.5–0.6 0.2–0.4 0.2 — —
Measured 0.79 0.75 0.68 0.4 0.2 0.49 0.71
Roughness 0.05 0.05 0.05 0.05 1.5 0.05 0.1
The reflectance coefficient ρcan be calculated as follows:
ρ=Eρ/E (3)
and the transmittance coefficient τas:
τ=Eτ/E (4)

Buildings 2024,14, 637 10 of 23
where E represents the incident illuminance, E
ρ
is the reflected illuminance, and E
τ
is the
transmitted illuminance.
The luminance test was conducted on selected classrooms [
43
] to evaluate the DGP
inside the windows. The test was carried out on 16 May 2023, at 13:00, under clear sky
conditions. DGP [
44
,
45
] was evaluated based on the vertical illuminance, luminance, size,
and position of the windows relative to the eye by Equation (5). Appendix Adescribes the
impact of different DGP ranges on human visual perception.
DGP can be calculated as follows:
DGP =5.87 ×10−5×Eυ+9.18 ×10−2log10 1+∑
i
L2
s,iωs,i
E1.87
υP2
i!+0.16 (5)
where E
υ
represents the vertical illuminance of the eye, L
s
is the luminance of the glare
source, ωdenotes the solid angle of the glare source, and P is the position index.
For the three selected test classrooms, monitoring electricity meters were installed
in lighting distribution boxes to record the usage time and energy consumed by indoor
lights on weekdays. Each classroom is equipped with 18 linear lamps arranged vertically,
each with a power of 22 W. There are also three specialized asymmetrically distributed
light fixtures to illuminate the blackboard, each with a power of 36 W. The layout of the
classroom lighting fixtures is shown in Figure 7.
Buildings 2024, 14, x FOR PEER REVIEW 10 of 24
Table 4. Reflectance factors.
Surface
Ceiling Wall
(Whitewash) Wall (Tile) Floor Window Door Shade
Reflectance
factor
Standard 0.7–0.8 0.5–0.6 0.5–0.6 0.2–0.4 0.2 — —
Measured 0.79 0.75 0.68 0.4 0.2 0.49 0.71
Roughness 0.05 0.05 0.05 0.05 1.5 0.05 0.1
The luminance test was conducted on selected classrooms [43] to evaluate the DGP
inside the windows. The test was carried out on 16 May 2023, at 13:00, under clear sky
conditions. DGP [44,45] was evaluated based on the vertical illuminance, luminance, size,
and position of the windows relative to the eye by Equation (5). Appendix A describes the
impact of different DGP ranges on human visual perception.
DGP can be calculated as follows:
DGP=5.87×10 ×E+9.18×10log 1+L,
ω,
E
.P
+0.16 (5)
where Eυ represents the vertical illuminance of the eye, Ls is the luminance of the glare
source, ω denotes the solid angle of the glare source, and P is the position index.
For the three selected test classrooms, monitoring electricity meters were installed in
lighting distribution boxes to record the usage time and energy consumed by indoor lights
on weekdays. Each classroom is equipped with 18 linear lamps arranged vertically, each
with a power of 22 W. There are also three specialized asymmetrically distributed light
fixtures to illuminate the blackboard, each with a power of 36 W. The layout of the class-
room lighting fixtures is shown in Figure 7.
Figure 7. Classroom lighting arrangement. The three classroom lighting systems are all arranged in
the same manner, with three strip lights evenly spaced in the classroom and blackboard lights in
parallel at the front.
In real-time monitoring of the lighting energy consumption, artificial lighting was
activated for supplementary illumination when the indoor natural light intensity cannot
reach 450 lux. This activation can be adjusted according to outdoor weather conditions
and daily schedules. During lighting energy consumption simulations, referring to the
school schedule as shown in Table 5, the 450 lux threshold served as the boundary. Artifi-
cial lighting was activated when the illumination fell below this value, while it remained
fully active during nighime study periods.
Table 5. Guangya middle school schedule.
Morning Noon Afternoon Dusk Night
Time 8:00–12:20 12:20–14:25 14:25–17:40 17:40–19:00 19:00–22:10
2.2.4. Multi-Objective Optimization Design
The control parameters affecting the daylighting performance of the classrooms were
discerned based on simulation and field measurement results. They include building
Figure 7. Classroom lighting arrangement. The three classroom lighting systems are all arranged
in the same manner, with three strip lights evenly spaced in the classroom and blackboard lights in
parallel at the front.
In real-time monitoring of the lighting energy consumption, artificial lighting was
activated for supplementary illumination when the indoor natural light intensity cannot
reach 450 lux. This activation can be adjusted according to outdoor weather conditions and
daily schedules. During lighting energy consumption simulations, referring to the school
schedule as shown in Table 5, the 450 lux threshold served as the boundary. Artificial
lighting was activated when the illumination fell below this value, while it remained fully
active during nighttime study periods.
Table 5. Guangya middle school schedule.
Morning Noon Afternoon Dusk Night
Time 8:00–12:20 12:20–14:25 14:25–17:40 17:40–19:00 19:00–22:10
2.2.4. Multi-Objective Optimization Design
The control parameters affecting the daylighting performance of the classrooms were
discerned based on simulation and field measurement results. They include building
orientation, window size, shading form, and the optical properties of material surfaces
(e.g., reflection
and transmittance coefficients). The dimensions and heights of the class-
rooms, which are typically constrained by design specifications and objective conditions,
were not considered as control parameters in this study.
The DF, PIT, DGP, and lighting energy consumption values were set as objective
parameters for optimization. To achieve the best possible daylighting performance, DF and

Buildings 2024,14, 637 11 of 23
PIT should be maximized, DGP should be kept below 0.4, and lighting energy consumption
should be minimized. The multi-objective optimization program “Octopus”, which utilizes
a GA, was employed for the optimization process. A total of 50 iterations (max generations)
were operated with a population size of 50 individuals in each iteration.
3. Results
3.1. Simulated Daylighting Performance
DF, PIT, DGP, and daylighting energy consumption were simulated for the three class-
rooms to evaluate the impact of different design parameters on daylighting performance.
3.1.1. Simulated DF Distribution
The DF distribution simulation results for the three classroom desktops are shown in
Figure 8.
Buildings2024,14,xFORPEERREVIEW11of24

orientation,windowsize,shadingform,andtheopticalpropertiesofmaterialsurfaces
(e.g.,reflectionandtransmiancecoefficients).Thedimensionsandheightsoftheclass-
rooms,whicharetypicallyconstrainedbydesignspecificationsandobjectiveconditions,
werenotconsideredascontrolparametersinthisstudy.
TheDF,PIT,DGP,andlightingenergyconsumptionvaluesweresetasobjectivepa-
rametersforoptimization.Toachievethebestpossibledaylightingperformance,DFand
PITshouldbemaximized,DGPshouldbekeptbelow0.4,andlightingenergyconsump-
tionshouldbeminimized.Themulti-objectiveoptimizationprogram“Octopus”,which
utilizesaGA,wasemployedfortheoptimizationprocess.Atotalof50iterations(max
generations)wereoperatedwithapopulationsizeof50individualsineachiteration.
3.Results
3.1.SimulatedDaylightingPerformance
DF,PIT,DGP,anddaylightingenergyconsumptionweresimulatedforthethree
classroomstoevaluatetheimpactofdifferentdesignparametersondaylightingperfor-
mance.
3.1.1.SimulatedDFDistribution
TheDFdistributionsimulationresultsforthethreeclassroomdesktopsareshownin
Figure8.

Figure8.DFsimulationresults.DifferentcolorsrepresentdifferentDFvalues,withdarkercolors
indicatinglowerilluminancelevelsandbrightercolorsindicatinghigherilluminancelevels.
Duetodifferencesintheorientation,windowsize,windowquantity,shadingform,
andexternaldaylightingconditions,theDFsimulationresultsshowsignificantvariations
amongthethreeclassrooms.ClassroomAexhibitedthebestdaylightingperformance,
withaDFrangeof11.81–1.20%.Thisisaributedtoitspredominantsouth-facingorien-
tation,unobstructedsurroundings,andadvantageouspositiononthefourthfloor.The
southernwindowareainsidethisclassroomreceivesthemaximumilluminance,gradu-
allydecreasingtowardsthecentralarea.Thenorthernpartoftheclassroomreceivesday-
lightthroughthecorridor,leadingtoanincreaseinilluminance,whiletheeastandwest
endsoftheclassroomhavethepoorestdaylightingconditions.
ClassroomBshowsthesecond-bestdaylightingperformance,withaDFrangefrom
10.99–0.81%.Itissituatedonthethirdfloorandmainlyreceivesnorth-facingdaylight,
whichisunobstructed,whiledaylightfromthenorthisdominatedbydiffuselightfrom
thesky.Exceptforareasnearthewindows,thewholeclassroomreceivesrelativelyuni-
formdaylighting,withhigherilluminanceclosetothenorthwindowsthatdecreases
graduallytowardsthecentralarea.Thepresenceofthecourtyardprovidesadditional
daylightnearthesouthwindows,resultinginaslightincreaseinilluminance.Theillumi-
nanceislowestattheeastandwestwalls.
ClassroomCexhibitsthepoorestdaylightingperformance,withaDFrangefrom
9.25–0.31%.Beinglocatedonthelowest(second)floor,itreliesondaylightfromthecourt-
yardforbothnorth-andsouth-facingorientations.Thesouthernwalldirectlyfacingthe
Figure 8. DF simulation results. Different colors represent different DF values, with darker colors
indicating lower illuminance levels and brighter colors indicating higher illuminance levels.
Due to differences in the orientation, window size, window quantity, shading form,
and external daylighting conditions, the DF simulation results show significant variations
among the three classrooms. Classroom A exhibited the best daylighting performance, with
a DF range of 11.81–1.20%. This is attributed to its predominant south-facing orientation,
unobstructed surroundings, and advantageous position on the fourth floor. The southern
window area inside this classroom receives the maximum illuminance, gradually decreasing
towards the central area. The northern part of the classroom receives daylight through
the corridor, leading to an increase in illuminance, while the east and west ends of the
classroom have the poorest daylighting conditions.
Classroom B shows the second-best daylighting performance, with a DF range from
10.99–0.81%. It is situated on the third floor and mainly receives north-facing daylight,
which is unobstructed, while daylight from the north is dominated by diffuse light from the
sky. Except for areas near the windows, the whole classroom receives relatively uniform
daylighting, with higher illuminance close to the north windows that decreases gradually
towards the central area. The presence of the courtyard provides additional daylight near
the south windows, resulting in a slight increase in illuminance. The illuminance is lowest
at the east and west walls.
Classroom C exhibits the poorest daylighting performance, with a DF range from
9.25–0.31%.
Being located on the lowest (second) floor, it relies on daylight from the court-
yard for both north- and south-facing orientations. The southern wall directly facing the
courtyard receives the highest illuminance, which gradually decreases towards the northern
part of the classroom and reaches its lowest value near the northern wall.
3.1.2. Simulated PIT
The PIT simulation results for the three classroom desktops are shown in Figure 9.

Buildings 2024,14, 637 12 of 23
Buildings2024,14,xFORPEERREVIEW12of24

courtyardreceivesthehighestilluminance,whichgraduallydecreasestowardsthenorth-
ernpartoftheclassroomandreachesitslowestvaluenearthenorthernwall.
3.1.2.SimulatedPIT
ThePITsimulationresultsforthethreeclassroomdesktopsareshowninFigure9.

Figure9.PITsimulationresults.
ThePITsimulationresultsshowthatareasnearlightsourcesreceivehigherillumi-
nance.InClassroomA,thesizeofthewallbetweenthewindowsonthelightsideis0.5
m,whichdoesnotsignificantlyaffecttheilluminancedistributionintheareasclosetothe
windows.However,inClassroomsBandC,thewindow-walldimensionsare1.0m,re-
sultinginnoticeablylowerilluminanceinthoseareas.ClassroomCismostaffected,un-
derscoringthesignificantimpactofwindow-walldimensionsonindoorilluminance
whendaylightconditionsaresuboptimal.
ThoughbothClassroomAandClassroomCfacesouth,ClassroomAhasbeerday-
lightconditionsduetotheabsenceofexternalobstructions.Further,whilebothClass-
roomAandClassroomBlackexternalobstructions,theyhavedifferentorientations;
ClassroomAfacessouthandClassroomBfacesnorth.Asaresult,theindoorilluminance
inClassroomAishigherthaninClassroomB.
3.1.3.SimulatedDGP
TheDGPsimulationresultsfordifferentobservationpointsinthethreeclassrooms
areshowninFigure10.
Figure 9. PIT simulation results.
The PIT simulation results show that areas near light sources receive higher illumi-
nance. In Classroom A, the size of the wall between the windows on the light side is
0.5 m, which does not significantly affect the illuminance distribution in the areas close to
the windows. However, in Classrooms B and C, the window-wall dimensions are
1.0 m
,
resulting in noticeably lower illuminance in those areas. Classroom C is most affected,
underscoring the significant impact of window-wall dimensions on indoor illuminance
when daylight conditions are suboptimal.
Though both Classroom A and Classroom C face south, Classroom A has better day-
light conditions due to the absence of external obstructions. Further, while both Classroom
A and Classroom B lack external obstructions, they have different orientations; Classroom
A faces south and Classroom B faces north. As a result, the indoor illuminance in Classroom
A is higher than in Classroom B.
3.1.3. Simulated DGP
The DGP simulation results for different observation points in the three classrooms
are shown in Figure 10.
DGP simulations were conducted for all three classrooms. Firstly, the midline position
of each room was selected at a distance of 4.1 m from the daylighting wall. The DGP
simulation results for all three classrooms were below 0.35, with the highest DGP value
observed at the mid-point of Classroom A and the lowest DGP value at the mid-point of
Classroom C.
Next, the observation point distance was adjusted to identify the distance between the
point and wall at which DGP reached 0.35. The observation point was 3.6 m away from
the wall for Classroom A, 3.0 m away for Classroom B, and 1.0 m away for Classroom C.
Beyond this area, glare would not be perceived by teachers and students. Further adjusting
the observation point distance was performed to identify the point at which DGP reached
0.40. This distance was 2.1 m for Classroom A, 2.0 m for Classroom B, and 0.1 m for
Classroom C. In this area, teachers and students would experience discomfort due to glare.
Finally, when the observation point was moved closer to the wall at 0.1 m, Class-
room A’s DGP reached 0.696 and Classroom B’s DGP reached 0.697, indicating significant
glare. In the summer season in the Guangzhou area, both south-facing and north-facing
orientations would cause strong glare. When comparing Classroom B to Classroom A,
differences in orientation and shading dimensions caused a rapid decrease in Classroom
B’s DGP value. Classroom C experienced the least glare due to the shading from its
south-facing orientation.

Buildings 2024,14, 637 13 of 23
Figure 10. DGP simulation results. DGP simulations were conducted for the center and window
positions of the three classrooms. The positions of observation points were simulated for DGP values
of 0.35 and 0.4.

Buildings 2024,14, 637 14 of 23
3.1.4. Simulated Lighting Energy Consumption
The lighting energy consumption simulation results of the three classrooms are shown
in Figure 11.
Buildings2024,14,xFORPEERREVIEW14of24

DGPsimulationswereconductedforallthreeclassrooms.Firstly,themidlineposi-
tionofeachroomwasselectedatadistanceof4.1mfromthedaylightingwall.TheDGP
simulationresultsforallthreeclassroomswerebelow0.35,withthehighestDGPvalue
observedatthemid-pointofClassroomAandthelowestDGPvalueatthemid-pointof
ClassroomC.
Next,theobservationpointdistancewasadjustedtoidentifythedistancebetween
thepointandwallatwhichDGPreached0.35.Theobservationpointwas3.6mawayfrom
thewallforClassroomA,3.0mawayforClassroomB,and1.0mawayforClassroomC.
Beyondthisarea,glarewouldnotbeperceivedbyteachersandstudents.Furtheradjust-
ingtheobservationpointdistancewasperformedtoidentifythepointatwhichDGP
reached0.40.Thisdistancewas2.1mforClassroomA,2.0mforClassroomB,and0.1m
forClassroomC.Inthisarea,teachersandstudentswouldexperiencediscomfortdueto
glare.
Finally,whentheobservationpointwasmovedclosertothewallat0.1m,Classroom
A’sDGPreached0.696andClassroomB’sDGPreached0.697,indicatingsignificantglare.
InthesummerseasonintheGuangzhouarea,bothsouth-facingandnorth-facingorien-
tationswouldcausestrongglare.WhencomparingClassroomBtoClassroomA,differ-
encesinorientationandshadingdimensionscausedarapiddecreaseinClassroomB’s
DGPvalue.ClassroomCexperiencedtheleastglareduetotheshadingfromitssouth-
facingorientation.
3.1.4.SimulatedLightingEnergyConsumption
Thelightingenergyconsumptionsimulationresultsofthethreeclassroomsare
showninFigure11.

Figure11.Simulationresultsforenergyconsumptionoflighting.Lightingenergyconsumptionwas
simulatedforthreeclassroomsduringthetransitionalseasonofMay,withvariouscolorsindicating
differentlevelsofenergyconsumption.Whennoonewaspresent,energyconsumptionwasnil.
TheMaylightingenergysimulationforClassroomAindicatedaconsumptionof
76.71kWh,forClassroomBitwas85.44kWh,andforClassroomCwas86.75kWh.After
excludingthenoonbreakperiod,theenergyconsumptionintheafternoonwaslowerthan
thatinthemorning,mainlyduetohigherindoorilluminanceintheeveningcomparedto
themorning.ClassroomAwassuperiortoBandCinitsconditionsoforientation,total
windowwidth,andwindow-wallwidth,resultinginashorterdurationandlowerenergy
consumptionforartificiallighting.ClassroomsBandChadsimilarlightingenergy
Figure 11. Simulation results for energy consumption of lighting. Lighting energy consumption was
simulated for three classrooms during the transitional season of May, with various colors indicating
different levels of energy consumption. When no one was present, energy consumption was nil.
The May lighting energy simulation for Classroom A indicated a consumption of
76.71 kWh, for Classroom B it was 85.44 kWh, and for Classroom C was 86.75 kWh.
After excluding the noon break period, the energy consumption in the afternoon was
lower than that in the morning, mainly due to higher indoor illuminance in the evening
compared to the morning. Classroom A was superior to B and C in its conditions of
orientation, total window width, and window-wall width, resulting in a shorter duration
and lower energy consumption for artificial lighting. Classrooms B and C had similar
lighting energy consumption results. As per the distribution of DF values, the main
illuminance in Classrooms B and C was concentrated near the windows, while the central
area had poorer daylight conditions, requiring additional artificial lighting.
3.2. Classroom Daylighting Performance Measurement
Field tests were conducted to validate the daylighting performance simulation results,
including real-time illuminance, luminance, and lighting energy consumption.
3.2.1. Classroom Illuminance Field Measurement
Real-time illuminance test results from the three classrooms were processed visually
to obtain the illuminance distributions shown in Figure 12.
The overall distribution of real-time illuminance from field testing closely mirrors the
PIT distribution, characterized by generally low illuminance levels. The highest illuminance
within Classroom A was 468 lux, the lowest was 34 lux, the average illuminance was
170.95 lux
, the daylight uniformity was 0.20, and the percentage of the DF greater than
or equal to 3% was 8%. In Classroom B, the highest illuminance was 421 lux, the lowest
was 49 lux, the average illuminance was 147.4 lux, the daylight uniformity was 0.33, and
the percentage of the DF greater than or equal to 3% was 9%. In Classroom C, the highest
illuminance was 154 lux, the lowest was 17.8 lux, the average illuminance was 61.9 lux,
the daylight uniformity was 0.29, and the percentage of the DF greater than or equal to
3% was 0%. Classroom B, which utilizes north-facing daylight, primarily receives diffuse

Buildings 2024,14, 637 15 of 23
sky light, resulting in relatively uniform daylight coverage. Classroom A, which receives
south-facing daylight, has the largest total window width among the three classrooms, and
is externally unobstructed, receiving the highest illuminance distribution.
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consumptionresults.AsperthedistributionofDFvalues,themainilluminanceinClass-
roomsBandCwasconcentratednearthewindows,whilethecentralareahadpoorer
daylightconditions,requiringadditionalartificiallighting.
3.2.ClassroomDaylightingPerformanceMeasurement
Fieldtestswereconductedtovalidatethedaylightingperformancesimulationre-
sults,includingreal-timeilluminance,luminance,andlightingenergyconsumption.
3.2.1.ClassroomIlluminanceFieldMeasurement
Real-timeilluminancetestresultsfromthethreeclassroomswereprocessedvisually
toobtaintheilluminancedistributionsshowninFigure12.

Figure12.Visualizationofindoorilluminationdistribution.Underidenticalconditions,thereliabil-
ityofthesimulationwasvalidatedthroughfieldmeasurementswiththesameexpressionasthe
illuminancesimulationresults.
Theoveralldistributionofreal-timeilluminancefromfieldtestingcloselymirrorsthe
PITdistribution,characterizedbygenerallylowilluminancelevels.Thehighestillumi-
nancewithinClassroomAwas468lux,thelowestwas34lux,theaverageilluminance
was170.95lux,thedaylightuniformitywas0.20,andthepercentageoftheDFgreater
thanorequalto3%was8%.InClassroomB,thehighestilluminancewas421lux,the
lowestwas49lux,theaverageilluminancewas147.4lux,thedaylightuniformitywas
0.33,andthepercentageoftheDFgreaterthanorequalto3%was9%.InClassroomC,
thehighestilluminancewas154lux,thelowestwas17.8lux,theaverageilluminancewas
61.9lux,thedaylightuniformitywas0.29,andthepercentageoftheDFgreaterthanor
equalto3%was0%.ClassroomB,whichutilizesnorth-facingdaylight,primarilyreceives
diffuseskylight,resultinginrelativelyuniformdaylightcoverage.ClassroomA,which
receivessouth-facingdaylight,hasthelargesttotalwindowwidthamongthethreeclass-
rooms,andisexternallyunobstructed,receivingthehighestilluminancedistribution.
Withinthesameroom,regardlessofnorthorsouthorientation,windowsdirectly
facingopenoutdoorareaswerefoundtoreceivemoredaylightcomparedtowindows
facingthecorridor.Whendirectlyfacingopenoutdoorareas,south-facingdaylight
(ClassroomA)createdhigherindoorilluminancethannorth-facingdaylight(Classroom
B),whilenorth-facingdaylightshowedbeeruniformity.Additionally,increasingthe
windowwidthwasbeneficialforsidelightdaylighting.

Figure 12. Visualization of indoor illumination distribution. Under identical conditions, the relia-
bility of the simulation was validated through field measurements with the same expression as the
illuminance simulation results.
Within the same room, regardless of north or south orientation, windows directly
facing open outdoor areas were found to receive more daylight compared to windows
facing the corridor. When directly facing open outdoor areas, south-facing daylight (Class-
room A) created higher indoor illuminance than north-facing daylight (Classroom B), while
north-facing daylight showed better uniformity. Additionally, increasing the window width
was beneficial for sidelight daylighting.
3.2.2. Luminance Measurement
Luminance and illuminance tests were conducted on the windows of the three class-
rooms, with measurements taken perpendicular to the eyes. The testing method is shown
in Figure 13. The DGP for each classroom was calculated using Equation (5).
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3.2.2.LuminanceMeasurement
Luminanceandilluminancetestswereconductedonthewindowsofthethreeclass-
rooms,withmeasurementstakenperpendiculartotheeyes.Thetestingmethodisshown
inFigure13.TheDGPforeachclassroomwascalculatedusingEquation(5).

Figure13.Luminancefieldmeasurement.Classroomluminancewasmeasuredunderconditions
identicaltothesimulation.TheluminancevaluewasutilizedtocalculateDGP,whichwasthen
comparedwiththesimulatedDGP.
Themeasurementpointswerelocatedat1/4,1/2,and3/4oftheclassroomdepth
alongthecentralaxisoftheexteriorwindow.TheDGPvaluesat1/4,1/2,and3/4perpen-
diculartothedirectionoftheexteriorwindowofthethreeclassroomswerecalculated
basedontheluminanceoftheglaresource(Ls),theverticalilluminanceofthelightsource
(Eυ),thesolidangleoftheglaresource(ω),andthepositionindex(P)ateachmeasurement
point.ThetestresultsandcalculatedDGPvaluesarelistedinTable6.
Tab l e 6.IlluminanceluminancemeasurementsandDGPforthreeclassrooms.
Ls1/4
(cd/m2)
Ls1/2
(cd/m2)
Ls3/4
(cd/m2)
Eυ1/4
(lux)
Eυ1/2
(lux)
Eυ3/4
(lux)DGP1/4DGP1/2DGP3/4
ClassroomA9036.935415.314737.51198913209830.3900.3180.287
ClassroomB6536.133190.291427.1714029847570.3770.3010.236
ClassroomC2036.541309.27892.894213112500.3160.2740.239
Ls1/4,Ls1/2,andLs3/4representluminancemeasurementvaluesat1/4,1/2,and3/4positionsvertically
alongthedepthoftheclassroomfromtheexteriorwindow,respectively.Eυ1/4,Eυ1/2,andEυ3/4repre-
sentilluminancemeasurementvaluesat1/4,1/2,and3/4positionsverticallyalongthedepthofthe
classroomfromtheexteriorwindow,respectively.DGP1/4,DGP1/2,andDGP3/4representcalculated
DGPvaluesat1/4,1/2,and3/4positionsverticallyalongthedepthoftheclassroomfromtheexterior
window,respectively.
AcomparisonbetweentheDGPvaluesobtainedfromtheactualmeasurementsof
glaresourceluminance(Ls)andverticalilluminanceofthelightsource(Eυ)againstthe
simulatedDGPvaluesindicatedsmallermeasuredvalues,withdeviationsrangingfrom
2.7%to5.9%.Giventheinfluenceoftheactualmeasurementenvironment,thisrangeof
errorisacceptable.Thebuildingorientation,shading,andpresenceofdaylightobstruc-
tionsoutdoorscanstillbeusedasparameterstodeterminewhetherthereisexcessive
glarecausedbydaylight.Optimizingtheseparameterscanhelptoresolveglare-related
issues.

Figure 13. Luminance field measurement. Classroom luminance was measured under conditions
identical to the simulation. The luminance value was utilized to calculate DGP, which was then
compared with the simulated DGP.

Buildings 2024,14, 637 16 of 23
The measurement points were located at 1/4, 1/2, and 3/4 of the classroom depth
along the central axis of the exterior window. The DGP values at 1/4, 1/2, and 3/4 per-
pendicular to the direction of the exterior window of the three classrooms were calculated
based on the luminance of the glare source (L
s
), the vertical illuminance of the light source
(E
υ
), the solid angle of the glare source (
ω
), and the position index (P) at each measurement
point. The test results and calculated DGP values are listed in Table 6.
Table 6. Illuminance luminance measurements and DGP for three classrooms.
Ls1/4
(cd/m2)
Ls1/2
(cd/m2)
Ls3/4
(cd/m2)
Eυ1/4
(lux)
Eυ1/2
(lux)
Eυ3/4
(lux) DGP1/4 DGP1/2 DGP3/4
Classroom A
9036.93 5415.31 4737.51 1989 1320 983 0.390 0.318 0.287
Classroom B 6536.13 3190.29 1427.17 1402 984 757 0.377 0.301 0.236
Classroom C 2036.54 1309.27 892.89 421 311 250 0.316 0.274 0.239
L
s1/4
, L
s1/2
, and L
s3/4
represent luminance measurement values at 1/4, 1/2, and 3/4 positions vertically along
the depth of the classroom from the exterior window, respectively. E
υ1/4
, E
υ1/2
, and E
υ3/4
represent illumi-
nance measurement values at 1/4, 1/2, and 3/4 positions vertically along the depth of the classroom from the
exterior window, respectively. DGP
1/4
, DGP
1/2
, and DGP
3/4
represent calculated DGP values at 1/4, 1/2, and
3/4 positions vertically along the depth of the classroom from the exterior window, respectively.
A comparison between the DGP values obtained from the actual measurements of
glare source luminance (L
s
) and vertical illuminance of the light source (E
υ
) against the
simulated DGP values indicated smaller measured values, with deviations ranging from
2.7% to 5.9%. Given the influence of the actual measurement environment, this range of
error is acceptable. The building orientation, shading, and presence of daylight obstructions
outdoors can still be used as parameters to determine whether there is excessive glare
caused by daylight. Optimizing these parameters can help to resolve glare-related issues.
3.2.3. Lighting Energy Consumption Monitoring
Monitoring electricity meters were installed on the lighting circuits of the three class-
rooms, including the energy consumption of the blackboard lighting and general classroom
lighting on the same circuit. The meter readings for May are given in Table 7.
Table 7. Lighting energy consumption for three classrooms.
May Classroom A (kWh) Classroom B (kWh) Classroom C (kWh)
Lighting energy
consumption 83.96 97.33 118.86
The monitored electricity meter data were significantly higher than the actual mea-
sured data, with deviations ranging from 9.45% to 37%. Among them, Classroom C shows
the highest deviation. The actual weather conditions in Guangzhou in May included
18 days
of rainy or overcast weather, 9 days of cloudy weather, and only 4 days of clear
weather. The classrooms needed more artificial lighting on non-clear days. Classroom C, in
particular, had poor daylighting in certain areas, necessitating that artificial lighting was
switched on during all study hours.
3.3. Multi-Objective Optimization Design
The “genome” of the GA used in this study represents the control parameters. The
objective parameters are the daylighting performance indicators. Among them, control
parameters provide a range of values based on the design experience, classroom dimensions,
structural layout, and regulatory requirements: Classroom orientation ranges from 45
◦
east
of south to 45
◦
west of south, total main daylighting surface window width between
7.8 m
and 8.8 m, window height between 1.6 m and 2.4 m, shading overhang width between
0 and 2.0 m
, and window transmittance coefficient between 0.6 and 0.9. The multi-objective
optimization results are shown in Figure 14.

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Figure14.Paretofrontdistribution.TheParetocurveidentifiestheoptimalsetofbuildingschemes
withsuperioropticalperformance.Thedifferentcoloredblocksinthefigurerepresentvarious
schemesforvariationsincontrolparameters,withblueareaindicatingtheParetofrontset.
Inthelatestagesofiteration,therewasanoticeableexpansioninthePareto-optimal
frontregionandthecorrespondinggenomedensity.Whentheiterationstopped,thebest
genomeandthesetofobjectiveparametersexhibitedsimilarcharacteristics,resultingin
ahighfitnessfunction.Astateofequilibriumbecameevidentuponscrutinizingtheopti-
malgenomeandobjectiveparameters,atwhichpointallfourobjectiveparameterswere
balanced.Therangeofgenomeparametersencompassclassroomorientationinthesouth-
westdirectionrangingfrom27°30′to28°44′,totalmaindaylightingsurfacewindowwidth
between8.5mand8.8m,windowheightbetween2.32mand2.4m,shadingoverhang
widthbetween1.85mand1.94m,andwindowtransmiancecoefficientbetween0.87and
0.9.Therangesofobjectiveparameterswerealsodetermined.Theoptimalsolutionset
wascomparedwiththeobjectiveparametersobtainedfromtheperformancesimulation
oftheactualmodelclassrooms(Table8).
Tab l e 8.Comparisonofopticalperformancebetweenoptimizedsolutionandthreeclassrooms.
DGP1/4DGP1/2DGP3/4PITavg(lux)DFavg(%)LightingEnergy(kWh)
Optimizedsolution0.3610.3220.2941077.015.5768.37
ClassroomA0.40.3320.306761.373.9476.71
ClassroomB0.390.3190.289676.73.6685.44
ClassroomC0.320.2860.273549.082.7586.75
Figure 14. Pareto front distribution. The Pareto curve identifies the optimal set of building schemes
with superior optical performance. The different colored blocks in the figure represent various
schemes for variations in control parameters, with blue area indicating the Pareto front set.
In the late stages of iteration, there was a noticeable expansion in the Pareto-optimal
front region and the corresponding genome density. When the iteration stopped, the best
genome and the set of objective parameters exhibited similar characteristics, resulting
in a high fitness function. A state of equilibrium became evident upon scrutinizing the
optimal genome and objective parameters, at which point all four objective parameters were
balanced. The range of genome parameters encompass classroom orientation in the south-
west direction ranging from 27
◦
30
′
to 28
◦
44
′
, total main daylighting surface window width
between 8.5 m and 8.8 m, window height between 2.32 m and 2.4 m, shading overhang
width between 1.85 m and 1.94 m, and window transmittance coefficient between 0.87 and
0.9. The ranges of objective parameters were also determined. The optimal solution set was
compared with the objective parameters obtained from the performance simulation of the
actual model classrooms (Table 8).

Buildings 2024,14, 637 18 of 23
Table 8. Comparison of optical performance between optimized solution and three classrooms.
DGP1/4 DGP1/2 DGP3/4 PITavg (lux) DFavg (%) Lighting Energy (kWh)
Optimized solution 0.361 0.322 0.294 1077.01 5.57 68.37
Classroom A 0.4 0.332 0.306 761.37 3.94 76.71
Classroom B 0.39 0.319 0.289 676.7 3.66 85.44
Classroom C 0.32 0.286 0.273 549.08 2.75 86.75
DGP
1/4
, DGP
1/2
, and DGP
3/4
represent the measured and simulated values of DGP at positions 1/4, 1/2, and
3/4 vertically along the depth of the classroom from the exterior window. PIT
avg
denotes the average real-time
illuminance, while DFavg represents the average daylight factor in the classroom.
Using the optimal solution set allows for the DGP values to effectively be controlled
within a range below 0.4. Compared to Classroom A, DGP
1/4
decreased by 10.8%, DGP
1/2
decreased by 3.1%, and DGP
3/4
decreased by 4.1%. Compared to Classroom B, DGP
1/4
decreased by 8%, DGP
1/2
increased by 0.9%, and DGP
3/4
increased by 1.7%. Compared
to Classroom C, DGP
1/4
increased by 11.4%, DGP
1/2
increased by 11.2%, and DGP
3/4
increased by 7.1%. DGP tends to increase when there is excessive daylight, and vice versa.
Utilizing an optimal solution set ensures that the DGP remains within a reasonable range
and exhibits uniform changes. Further, when using the optimal solution set, both the PIT
and DF are effectively optimized. Compared to Classroom A, PIT
avg
increased by 41.4%
and DF
avg
increased by 41.3%. Compared to Classroom B, PIT
avg
increased by 59.1% and
DF
avg
increased by 52.2%. Compared to Classroom C, PIT
avg
increased by 96.1% and DF
avg
increased by 102.5%.
Using the optimal solution set also ensures favorable illuminance and DF values
while reducing the energy consumed by artificial lighting. Compared to Classroom A,
the lighting energy consumption was reduced by 10.9%. Compared to Classroom B, the
lighting energy consumption was reduced by 20%. Compared to Classroom C, the lighting
energy consumption was reduced by 21.2%.
In summary, the parameters of the optimal solution set represent an effective design
strategy that considers multiple indicators of daylight performance.
4. Discussion
4.1. Evaluating Design Parameters by Measured vs. Simulated Daylighting Performance Index
With a primary focus on enhancing the daylighting performance of secondary school
classrooms, a method was developed in this study to evaluate design parameters through
daylighting performance indicators derived from field measurements and physical simu-
lations. The proposed method solves the problem whereby important parameters cannot
be correctly selected to predict daylighting performance in the early phases of design. It
also provides architects with recommended design parameters and parameter value ranges
in the design scheme phase. Compared to Classroom A, which has inherently favorable
lighting conditions, the daylighting performance obtained by using the recommended pa-
rameters was found to effectively reduce the indoor DGP. Simultaneously, PIT
avg
increased
by 41.4%, DF
avg
increased by 41.3%, and lighting energy consumption decreased by 10.9%.
The value ranges of these parameters are correlated with regional climate and building
types, rendering the proposed method applicable to other similar cases.
4.2. Accuracy of Physical Simulations and Field Measurements
To improve prediction accuracy, this study adopted a combination of physical simula-
tions and field measurements. First, the reflection and transmittance coefficients based on
field measurements were input to the simulation program. It is worth noting that these
coefficients can vary for the same material under different conditions. To assess the impact
of these coefficients, a sensitivity analysis was conducted on control parameters while
adjusting their values at appropriate stages, as illustrated in Figure 15.

Buildings 2024,14, 637 19 of 23
Buildings2024,14,xFORPEERREVIEW20of24

forthesamematerialandaveragedwhenmeasuringmaterialreflectanceortransmiance
coefficients,minimizingtheimpactonsimulationaccuracy.

Figure15.Effectofchangingcontrolparametersontargetparameters.
Secondly,toensureconsistencywiththefieldmeasurements,thedynamicindicator
PITwaschosenoverUDI,asUDIprimarilyfocusesonassessingtheaverageilluminance
levelinaroomandisnotsuitablefordirectcomparisonwithmeasuredilluminancelevels.
Guangzhouisacitywithindistinctseasons,characterizedbypredominantlyhotandhu-
midweatherthroughouttheyear.Winterismildandbrief.Seasonscanbecategorized
usingclimaticstatisticstodefinespring(March,April,May),summer(June,July,August),
autumn(September,October,November),andwinter(December,January,February)
[46].
Springandautumn,transitionalseasonsinGuangzhou,arethetimesofyearthat
secondaryschoolstudentspredominantlyspendatschool.ThemonthofMaywasse-
lectedasthetimingforbothmeasurementsandsimulations,asitishighlyrepresentative
ofthetypicalweatherconditionsinGuangzhouduringtheschoolyear.Thephysicalen-
vironmentoftransitionalseasonbuildingssignificantlyaffectscomfortandlearningeffi-
ciency[47,48],makingresearchontransitionalseasonscriticallyimportant.Ifmeasure-
mentsandsimulationsareconductedduringwinterandsummer,adjustmentstothesize
ofshadingdevicesareneededtoincreaseordecreaseindoordaylightinglevels.Therefore,
itisrecommendedtouseadjustableshadingsystemstomeetthelightingrequirements
duringdifferentseasons.
Fieldmeasurementswereconductedconcurrentlyinthreeclassroomsandinvolved
asingletestsession,whichmayhaveimpactedtheaccuracyofilluminancemeasure-
ments.However,fromtheperspectiveofoverallperformanceevaluationanddesignpa-
rameters,thisdidnotsignificantlyimpactthestudy’sconclusions.Futureresearchen-
deavorswillinvolvemultipletestsconductedatdifferenttimeperiodstoexpandthesam-
plesizeandmorecomprehensivelyevaluatedaylightingperformance.
Additionally,thefieldmeasurementswereconductedinanexistingteachingbuild-
ing,andtherecommendedparametersweredesignedunderconditionssimilartothose
Figure 15. Effect of changing control parameters on target parameters.
Following optimization, among the four control parameters, the window’s transmit-
tance coefficient exhibited the most significant effect on the target parameters. Increasing
the window’s transmittance coefficient strengthened the trends in DF, PIT, and DGP. Con-
versely, changes in building orientation, shading size, and window width did not show
consistent patterns or significant trends in daylighting performance indicators.
A variation of 0.1 in the window’s reflectance coefficient exerted a substantial impact
on the simulation results. Therefore, meticulous measurement of the window’s transmit-
tance coefficient is crucial. To account for this, multiple measurement points were taken
for the same material and averaged when measuring material reflectance or transmittance
coefficients, minimizing the impact on simulation accuracy.
Secondly, to ensure consistency with the field measurements, the dynamic indicator
PIT was chosen over UDI, as UDI primarily focuses on assessing the average illuminance
level in a room and is not suitable for direct comparison with measured illuminance levels.
Guangzhou is a city with indistinct seasons, characterized by predominantly hot and
humid weather throughout the year. Winter is mild and brief. Seasons can be categorized
using climatic statistics to define spring (March, April, May), summer (June, July, August),
autumn (September, October, November), and winter (December, January, February) [46].
Spring and autumn, transitional seasons in Guangzhou, are the times of year that
secondary school students predominantly spend at school. The month of May was selected
as the timing for both measurements and simulations, as it is highly representative of the
typical weather conditions in Guangzhou during the school year. The physical environment
of transitional season buildings significantly affects comfort and learning
efficiency [47,48]
,
making research on transitional seasons critically important. If measurements and sim-
ulations are conducted during winter and summer, adjustments to the size of shading
devices are needed to increase or decrease indoor daylighting levels. Therefore, it is rec-
ommended to use adjustable shading systems to meet the lighting requirements during
different seasons.
Field measurements were conducted concurrently in three classrooms and involved a
single test session, which may have impacted the accuracy of illuminance measurements.
However, from the perspective of overall performance evaluation and design parameters,
this did not significantly impact the study’s conclusions. Future research endeavors will

Buildings 2024,14, 637 20 of 23
involve multiple tests conducted at different time periods to expand the sample size and
more comprehensively evaluate daylighting performance.
Additionally, the field measurements were conducted in an existing teaching building,
and the recommended parameters were designed under conditions similar to those of
the measured samples. Considering that the dimensions of current secondary school
classrooms are already well-established and stable, the recommended parameters can be
effectively applied to classrooms of this type. The simulation model used in this study is
comprehensive and well-established. Parametric design offers a significant advantage by
allowing adaptation to diverse regions through fine-tuning geographical climate conditions,
meteorological data, and control parameter ranges. However, it is not suitable for buildings
other than educational buildings.
4.3. Advantages and Disadvantages of Genetic Algorithms
The GA proved to be an efficient optimization method in this study. Control parame-
ters were treated as genes, then randomly selected and combined to quickly calculate the
optimal solution set. This approach was particularly effective for optimizing the objective
parameter DGP, which has specific range requirements. The GA was implemented through
the Design Explorer platform to restrict parameter values within predefined ranges, fa-
cilitating the rapid identifications of optimal solutions. However, it is important to note
that the quality of the solution set was influenced by the choices made regarding the
elitism rate, mutation rate, and crossover rate when configuring the GA. The algorithm
also demonstrated a degree of sensitivity to the initial population selection, necessitating
skillful guidance in establishing the initial population parameters.
4.4. Data-Driven Performance Prediction
Data-driven approaches have significant potential for predicting building performance.
In this study, the GA provided gene sets and solution sets that can serve as valuable inputs
for data-driven applications. These datasets could be harnessed in machine learning
processes to accurately predict daylighting performance in buildings, offering promising
avenues for future research. The design parameters derived in this study also may provide
a workable foundation for exploring new design approaches and expanding the scope of
research in this field.
5. Conclusions
An effective method for evaluating design parameters related to daylighting per-
formance was established in this study based on measurements and simulations. This
method can be used to identify precise ranges for multiple design parameters, enabling
the rapid attainment of optimized daylighting performance. Focused on secondary school
classrooms in the Guangzhou region, building daylighting performance was evaluated
based on DGP, PIT, DF, and lighting energy consumption. The conclusions of this work can
be summarized as follows.
(1)
Small variations in the window transmittance coefficient exerted a significant impact
on the simulation results. A reduction of 0.1 in the transmittance coefficient resulted
in a 10.1% decrease in the average PIT and a 14.6% decrease in the average DF. A
further reduction of 0.2 in the coefficient led to a 40.4% decrease in the average PIT
and a 32.6% decrease in the average DF.
(2)
In accordance with established architectural design standards, four design parameter
ranges that impact classroom daylighting performance were delineated: orientation
within 27
◦
30
′
to 28
◦
44
′
south of west, total window width of 8.5 m to 8.8 m on the
main daylighting facade, window height of 2.32 m to 2.4 m, sunshade overhang width
of 1.85 m to 1.94 m, and window transmittance coefficient of 0.87 to 0.9.
(3)
Compared to Classroom A, which exhibited better daylighting conditions than other
classrooms included in the case study, the daylighting performance obtained by using
the recommended parameters can effectively reduce the indoor DGP, increase the

Buildings 2024,14, 637 21 of 23
average PIT by 41.4%, increase the average DF by 41.3%, and decrease the energy
consumed by artificial lighting by up to 10.9%.
In summary, the results of this study may provide architects with a method to pre-
dict daylighting performance and optimal parameter ranges during the early design
phase. This holds practical significance for the swift prediction and assessment of building
daylighting performance.
Author Contributions: Conceptualization, J.L. and L.Z.; Methodology, X.Z.; Software, G.Y.; Valida-
tion, G.Y.; Formal analysis, G.Y.; Investigation, G.Y. and X.S.; Writing—original draft, J.L. and G.Y.;
Writing—review & editing, G.Y., X.Z. and X.S.; Supervision, J.L. and L.Z.; Project administration, L.Z.;
Funding acquisition, J.L. All authors have read and agreed to the published version of
the manuscript
.
Funding: This research was funded by [National Natural Science Foundation of China] grant number
[52278107]; and [Guangdong Department of Housing and Urban-Rural Development 2022 Science
and Technology Innovation Plan] grant number [2022-K2-450970]; and [Natural Science Foundation
of Guangdong Province] The title of the project: [Multi-objective optimization design strategy for
the daylighting and thermal performance of secondary school teaching buildings in Guangdong
Province]. The APC was funded by [Natural Science Foundation of Guangdong Province].
Data Availability Statement: The data presented in this study are available on request from the
corresponding author (privacy reasons).
Conflicts of Interest: The authors declare no conflict of interest.
Nomenclature
DGP Daylight glare probability
PIT Point-in-time illuminance
DF Daylight factor
GAs Genetic algorithms
WWR Window-to-wall ratio
DA Daylight autonomy
sDA Spatial daylight autonomy
UDI Useful daylight illuminance
CBDM Climate-based daylight modeling
IESNA Illuminating Engineering Society of North America
E Illuminance
Eavg Average illuminance
EnIndoor illuminance
EwOutdoor illuminance
ρReflectance coefficient
EρReflected illuminance
τTransmittance coefficient
EτTransmitted illuminance
EυVertical illuminance
Lruanj Luminance
ωSolid angle
P Position index
Appendix A
Classification of DGP ranges on human visual perception.
DGP Classification DGP Range
Imperceptible Glare 0.35 > DGP
Perceptible Glare 0.4 > DGP ≥0.35
Disturbing Glare 0.45 > DGP ≥0.4
Intolerable Glare DGP ≥0.45

Buildings 2024,14, 637 22 of 23
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