A Study on the Effect of Dynamic Photovoltaic Shading Devices on Energy Consumption and Daylighting of an Office Building

Abstract

Photovoltaic shading devices (PVSDs) have the dual function of providing shade and generating electricity, which can reduce building energy consumption and improve indoor daylighting levels. This study adopts a parametric performance design method and establishes a one-click simulation process by using the Grasshopper platform and Ladybugtools. The research focuses on the effect of dynamic PVSDs on daylighting and energy consumption in an office building in Qingdao. The optimal configuration of PVSDs for each month under three dynamic strategies (rotation, sliding, and hybrid) is determined here. Additionally, different control strategies and fixed PVSDs are compared to clarify the impact of various control strategies on daylighting and energy consumption. The findings reveal that, compared to no shading, dynamic PVSDs in the rotation strategy, sliding strategy, and hybrid strategy can achieve energy savings of 32.13%, 47.22%, and 50.38%, respectively. They can also increase the annual average UDI by 1.39%, 2.8%, and 3.1%, respectively. Dynamic PVSDs can significantly reduce the energy consumption of office buildings in Qingdao while improving indoor daylighting levels. A flexible control strategy that adapts to climate change can significantly improve building performance. This research can provide theoretical, methodological, and data support for the application of the PVSD in cold-climate regions in China.

Full text

Citation: Jiang, Y.; Qi, Z.; Ran, S.; Ma,
Q. A Study on the Effect of Dynamic
Photovoltaic Shading Devices on
Energy Consumption and Daylighting
of an Office Building. Buildings 2024,
14, 596. https://doi.org/10.3390/
buildings14030596
Academic Editor: Constantinos
A. Balaras
Received: 25 January 2024
Revised: 19 February 2024
Accepted: 20 February 2024
Published: 23 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
A Study on the Effect of Dynamic Photovoltaic Shading Devices
on Energy Consumption and Daylighting of an Office Building
Yan Jiang 1,2, Zongxin Qi 1, Shenglin Ran 1and Qingsong Ma 1,*
1Innovation Institute for Sustainable Maritime Architecture Research and Technology, Qingdao University of
Technology, Qingdao 266033, China; coastdesign@163.com (Y.J.); 17864271803@163.com (Z.Q.);
ran.shenglin@outlook.com (S.R.)
2Faculty of Environmental Engineering, The University of Kitakyushu, Kitakyushu 808-0135, Japan
*Correspondence: maqingsong@qut.edu.cn
Abstract: Photovoltaic shading devices (PVSDs) have the dual function of providing shade and
generating electricity, which can reduce building energy consumption and improve indoor daylight-
ing levels. This study adopts a parametric performance design method and establishes a one-click
simulation process by using the Grasshopper platform and Ladybugtools. The research focuses on
the effect of dynamic PVSDs on daylighting and energy consumption in an office building in Qingdao.
The optimal configuration of PVSDs for each month under three dynamic strategies (rotation, sliding,
and hybrid) is determined here. Additionally, different control strategies and fixed PVSDs are com-
pared to clarify the impact of various control strategies on daylighting and energy consumption. The
findings reveal that, compared to no shading, dynamic PVSDs in the rotation strategy, sliding strategy,
and hybrid strategy can achieve energy savings of 32.13%, 47.22%, and 50.38%, respectively. They can
also increase the annual average UDI by 1.39%, 2.8%, and 3.1%, respectively. Dynamic PVSDs can
significantly reduce the energy consumption of office buildings in Qingdao while improving indoor
daylighting levels. A flexible control strategy that adapts to climate change can significantly improve
building performance. This research can provide theoretical, methodological, and data support for
the application of the PVSD in cold-climate regions in China.
Keywords: photovoltaic shading devices (PVSDs); dynamic; fixed; energy consumption; daylighting
1. Introduction
The global issues of energy consumption and environmental pollution have become
significant challenges that hinder human development. In 2020, China made a commitment
to peak carbon emissions by 2030 and achieve carbon neutrality by 2060 [
1
]. Subsequently,
in 2021, China released the “Action Plan to Peak Carbon Before 2030”, which outlined
the goal that non-fossil energy consumption will reach 20% and 25% by 2025 and 2030,
respectively [
2
]. Faced with rising energy demand and environmental degradation, re-
newable energy may efficiently cut fossil fuel use and carbon emissions while also having
significant application value. In 2020, China’s energy consumption in the entire building
process accounted for 45.5% of the country’s energy consumption, energy consumption in
the building operation stage accounted for 21.3% of the country’s energy consumption, and
carbon emissions in the building operation stage accounted for 21.7% of the total carbon
emissions [
3
]. Therefore, using clean energy to reduce energy consumption in the building
operation phase is of great significance to achieve the goal of carbon neutrality.
Photovoltaic technology can generate electricity for buildings, reduce the reliance
on traditional urban power networks, and lower urban carbon emissions [
4
]. With the
advancement of photovoltaic technologies, building-integrated photovoltaics (BIPVs) have
been applied to building facades and roofs, as well as integrated into the architectural
design to become a functional component of the building [
5
]. The first BIPV system was
Buildings 2024,14, 596. https://doi.org/10.3390/buildings14030596 https://www.mdpi.com/journal/buildings

Buildings 2024,14, 596 2 of 22
introduced in the 1980s, but its high cost initially limited its application market [
6
]. It was
not until the advancement of photovoltaic technology and the demand for low-carbon
buildings that BIPVs became popular [
7
]. Photovoltaic shading devices (PVSDs) not only
provide shade, but also collect solar energy, converting the sunlight with restricted access to
the building into electricity [
8
]. However, as part of the building envelope, PVSDs will also
strongly affect the indoor daylighting environment and energy consumption. Therefore,
when designing PVSDs, it is necessary to find the right balance between indoor daylighting
and energy consumption as much as possible. Excessive solar radiation can cause indoor
overheating and glare problems. However, if too much solar radiation is blocked, the
energy demands for heating and artificial lighting will increase [9].
A PVSD combines shading and photovoltaic functions to improve the internal daylight
and thermal environment [
10
], lower the building cooling load [
11
], improve indoor visual
comfort [
12
], and fulfill a portion of the building’s energy demands [
13
]. Many studies have
demonstrated the energy-saving potential of PVSDs [
14
,
15
]. Compared with traditional
shading devices and unshaded windows, PVSDs perform better in terms of energy use and
daylighting [
16
]. For instance, Mostafa et al. [
17
] used an educational facility at the GUC
University in Cairo to investigate the daylighting and energy-saving benefits of installing
PVSDs in the south and east. Sadatifar et al. [
18
] optimized the design of PVSDs for five
different climate zones from the perspective of energy use and daylighting. Ellika et al. [
8
]
proposed a design method for fixed PVSDs based on multi-objective optimization (MOO).
The inclination angle and the number of PVSDs louvers on the south side of an office
building in Northern Europe were optimized with the goals of net energy consumption,
power generation, and daylighting. Chen et al. [
19
] investigated the daylighting and power
generation performance of PVSDs in China’s hot summers and cold winters. The results
found that the proportion of indoor space illumination between 450 lx and 2000 lx, and
exceeding 50% of the time, was about 85%, and the power generation amount ranged from
9.18 kWh to 22.46 kWh.
However, most of these studies seek to achieve a balance between power generation
and shading, as well as to optimize the design of fixed PVSDs with one or more of the
following objectives: daylighting, energy consumption, visual comfort, and electricity
generation. The sun’s altitude and azimuth angles continuously change throughout the
year. A dynamic PVSD can greatly enhance the potential of power generation and shading
and improve indoor daylighting and the thermal environment. However, there are only a
few studies on dynamic PVSDs. Ayca et al. [
20
] classified dynamic PVSD control strategies
into the following three categories: soft control, hard control, and other technologies (hybrid
strategies), based on the implementation method of the control strategy and the tools used.
Svetozarevic et al. [
21
] presented a dynamic PVSD that can increase power generation by
50% compared to a fixed PVSD and can meet 115% of office net energy requirements in
temperate and arid climate conditions. Meysam et al. [
22
] compared the difference in power
generation and building heating load between south-facing dynamic PVSDs and static
PVSDs in an apartment in Tehran. The study found that changing the angle and position of
the PVSD twice a year can significantly improve energy efficiency, and more changes have
little impact on energy consumption. Krarti et al. [
23
] investigated the impact of a dynamic
PVSD on building energy consumption in four U.S. cities. The study found that, while
hourly control strategies had better energy-saving potential, daily and monthly control
strategies gained most of the advantages of dynamic PVSDs. Compared with having no
PVSD, controlling a PVSD on a monthly basis can reduce greenhouse gas emissions by 87%.
From these studies, it is evident that the optimal shading system is closely related
to building characteristics [
24
] (building function, building orientation, building form
coefficient, and envelope heat transfer coefficient), geographical location [
25
] (longitude,
latitude, altitude, and climate), type of shading device [
16
], and control strategies, etc. [
26
].
Due to the complexity of influencing factors, it is difficult for architects to judge the
performance of PVSDs through experimental methods and rules of thumb. Simulation
can help designers to make decisions quickly and accurately. The simulation software

Buildings 2024,14, 596 3 of 22
commonly used in the past (EnergyPlus, DoE-2, and TRNsys) not only required users to
master a large amount of relevant professional knowledge, but also required programming
abilities. Moreover, multiple simulation software options are independent of each other,
which undoubtedly increases the difficulty for designers to evaluate PVSD performance
through software simulation. Ladybugtools in the Grasshopper platform integrate a variety
of commonly used simulation engines, including daylighting and energy consumption
simulations, which can be used to evaluate building performance in many aspects and form
a simple and flexible parametric performance evaluation process. Architects and users can
simply adjust the corresponding parameters to evaluate a PVSD under different conditions
and further export visual graphics for analysis [
27
], thereby expanding the boundaries of
the design [28].
In this study, the following three control strategies for PVSDs were considered: rotation,
sliding, and hybrid (rotation + sliding). By rotating the PVSD, the power generation, indoor
daylighting, and visual comfort can be adjusted. Sliding the PVSD up and down can
effectively adjust whether sunlight can enter the room. Of course, it can also control the
sunlight entering the room from the upper or lower part of the window. Presently, there
are few studies on dynamic PVSDs in China, especially for office buildings in cold-climate
areas. This study has established a flexible and simple parametric performance simulation
process for PVSDs based on Ladybugtools and the Grasshopper platform. The design
and control strategies of PVSD are evaluated using daylighting and energy consumption
as optimization objectives. The main purposes of this paper are twofold, as follows:
(1) To study the impact of three control strategies of PVSDs on building daylighting and
energy consumption throughout the year. (2) To explore the energy-saving and daylighting
application value of three control strategies of PVSDs in office buildings in cold areas. It is
hoped that this study can provide theoretical, methodological, and data support for the
promotion and application of dynamic PVSDs in cold-climate areas.
2. Methodology
2.1. Case Study Description
This case study is located in Qingdao, China. The research room on the sixth floor of
the office building covers an area of 71 m2. It is usually used for multi-person offices. The
length, width, and height of the room are 8 m, 9 m, and 3.4 m, respectively. This room has
three casement windows facing south. Each window has a height of 1.8 m and a width
of 1.45 m. The windowsill height is 0.9 m. Three PVSDs are installed on the outside of
the three windows. Each PVSD panel is independent. There are no other surrounding
buildings or landscaping blocking sunlight from entering the room. The north wall of
the room adjacent to the internal corridor made of glass. There is also the same type of
multi-person office on the east and west sides of the room. Figure 1shows an overview of
the case room. Among them, a represents the corridor perspective, b represents the indoor
perspective of the case room, and c represents the case room seen from the outside.
Buildings 2024, 14, x FOR PEER REVIEW 4 of 23
Figure 1. An overview of the case room.
Qingdao is located in the southern part of the Shandong Peninsula, located at
119°30′–121°00′ east longitude and 35°35′–37°09′ north latitude. Qingdao faces the sea on
three sides. Affected by the southeast monsoon, ocean currents, and water masses, Qing-
dao has significant maritime climate characteristics. Generally speaking, Qingdao has four
distinct seasons throughout the year, with hot, humid, and rainy summers and is windy
with low temperatures in the winter. The meteorological data used in this study are Qing-
dao meteorological data from 2007 to 2021 (hps://climate.onebuilding.org/, accessed on
25 January 2024). Figure 2 shows the changes in dry bulb temperature and global horizon-
tal radiation in Qingdao.
Figure 2. Dry bulb temperature and global horizontal radiation.
2.2. Baseline Model Seings
Based on the Grasshopper platform, a parametric model of the room and its sur-
rounding elements was constructed according to the actual situation. Parametric models
of energy consumption and daylight were established through Ladybugtools. The estab-
lished model was verified in Section 3. According to the actual structure of the building
and the requirements for school buildings in the “General code for energy efficiency and
renewable energy application in buildings” [29], the thermal parameters of the walls, win-
dows, and roofs, as well as the internal loads of the building, are set. The thermal proper-
ties of the building envelope are detailed in Table 1. The optical properties are outlined in
Table 2. The air conditioning temperature control schedule is shown in Figure 3. In the
daylighting model, indoor illumination test points are distributed in a grid (1 m × 1 m)
Figure 1. An overview of the case room.

Buildings 2024,14, 596 4 of 22
Qingdao is located in the southern part of the Shandong Peninsula, located at 119
◦
30
′
–
121
◦
00
′
east longitude and 35
◦
35
′
–37
◦
09
′
north latitude. Qingdao faces the sea on three
sides. Affected by the southeast monsoon, ocean currents, and water masses, Qingdao has
significant maritime climate characteristics. Generally speaking, Qingdao has four distinct
seasons throughout the year, with hot, humid, and rainy summers and is windy with
low temperatures in the winter. The meteorological data used in this study are Qingdao
meteorological data from 2007 to 2021 (https://climate.onebuilding.org/, accessed on
25 January 2024
). Figure 2shows the changes in dry bulb temperature and global horizontal
radiation in Qingdao.
Buildings 2024, 14, x FOR PEER REVIEW 4 of 23
Figure 1. An overview of the case room.
Qingdao is located in the southern part of the Shandong Peninsula, located at
119°30′–121°00′ east longitude and 35°35′–37°09′ north latitude. Qingdao faces the sea on
three sides. Affected by the southeast monsoon, ocean currents, and water masses, Qing-
dao has significant maritime climate characteristics. Generally speaking, Qingdao has four
distinct seasons throughout the year, with hot, humid, and rainy summers and is windy
with low temperatures in the winter. The meteorological data used in this study are Qing-
dao meteorological data from 2007 to 2021 (hps://climate.onebuilding.org/, accessed on
25 January 2024). Figure 2 shows the changes in dry bulb temperature and global horizon-
tal radiation in Qingdao.
Figure 2. Dry bulb temperature and global horizontal radiation.
2.2. Baseline Model Seings
Based on the Grasshopper platform, a parametric model of the room and its sur-
rounding elements was constructed according to the actual situation. Parametric models
of energy consumption and daylight were established through Ladybugtools. The estab-
lished model was verified in Section 3. According to the actual structure of the building
and the requirements for school buildings in the “General code for energy efficiency and
renewable energy application in buildings” [29], the thermal parameters of the walls, win-
dows, and roofs, as well as the internal loads of the building, are set. The thermal proper-
ties of the building envelope are detailed in Table 1. The optical properties are outlined in
Table 2. The air conditioning temperature control schedule is shown in Figure 3. In the
daylighting model, indoor illumination test points are distributed in a grid (1 m × 1 m)
Figure 2. Dry bulb temperature and global horizontal radiation.
2.2. Baseline Model Settings
Based on the Grasshopper platform, a parametric model of the room and its surround-
ing elements was constructed according to the actual situation. Parametric models of
energy consumption and daylight were established through Ladybugtools. The established
model was verified in Section 3. According to the actual structure of the building and the
requirements for school buildings in the “General code for energy efficiency and renewable
energy application in buildings” [
29
], the thermal parameters of the walls, windows, and
roofs, as well as the internal loads of the building, are set. The thermal properties of the
building envelope are detailed in Table 1. The optical properties are outlined in Table 2.
The air conditioning temperature control schedule is shown in Figure 3. In the daylighting
model, indoor illumination test points are distributed in a grid (1 m
×
1 m) with a height
of 0.75 m. The minimum illumination of the indoor working surfaces is controlled at 300
lux. When the working surface does not reach 300 lux, artificial lighting is turned on.
Table 1. Thermal properties of the building.
Component Value Unit
External wall U-value 0.36 W/(m2K)
Roof U-value 0.47 W/(m2K)
Window U-value 2.58 W/(m2K)
Airtightness 0.0003 m3/s-m2
Lighting load 8 W/m2
Equipment load 15 W/m2
Ventilation per person 0.0084 m3/s-ppl

Buildings 2024,14, 596 5 of 22
Table 2. Optical properties of the surfaces.
Building Elements RGB Reflectance Roughness Specularity Transmissivity
Opaque wall 0.85, 0.85, 0.85 0.05 0.0013 -
Ceiling 0.16, 0.17, 0.17 0.005 0.008 -
Floor 0.4, 0.45, 0.41 0.002 0.05 -
Window - - - 0.65
Glass wall - - - 0.65
Buildings 2024, 14, x FOR PEER REVIEW 5 of 23
with a height of 0.75 m. The minimum illumination of the indoor working surfaces is con-
trolled at 300 lux. When the working surface does not reach 300 lux, artificial lighting is
turned on.
(a) (b)
Figure 3. Air conditioning schedule: (a) Heating setpoint schedule; (b) Cooling setpoint schedule.
Table 1. Thermal properties of the building.
Component Value Unit
External wall U-value 0.36 W/(m2K)
Roof U-value 0.47 W/(m2K)
Window U-value 2.58 W/(m2K)
Airtightness 0.0003 m3/s-m2
Lighting load 8 W/m2
Equipment load 15 W/m2
Ventilation per person 0.0084 m3/s-ppl
Table 2. Optical properties of the surfaces.
Building Elements RGB Reflectance Roughness Specularity Transmissivity
Opaque wall 0.85, 0.85, 0.85 0.05 0.0013 -
Ceiling 0.16, 0.17, 0.17 0.005 0.008 -
Floor 0.4, 0.45, 0.41 0.002 0.05 -
Window - - - 0.65
Glass wall - - - 0.65
2.3. PVSD Control Strategies
Figure 4 shows the dynamic photovoltaic shading devices (PVSDs) installed on the
south wall. Among them, the red arrow represents the solar altitude angle at the summer
solstice, and the blue arrow represents the solar altitude angle at the winter solstice. There
are three design variables for PVSDs, namely the width, tilt angle, and sliding height. The
range and interval control of the variables are shown in Table 3. This study considers the
impact of three dynamic strategies of PVSDs (rotation, sliding up and down, and hybrid)
on indoor daylighting and energy consumption. In the case of the rotation strategy, the
width and installation height of the PVSD are fixed and can be rotated at intervals of 5°,
between 0° and 70°. In the case of the sliding strategy, the width and tilt angle of the PVSD
are fixed and can slide up and down along the guide rails. The highest point can slide to
1 m above the upper eaves of the window, and the lowest point can slide to the lower
eaves of the window. The interval between each slide is 0.2 m (the upper eave of the win-
dow is 0 m, and the lower eave of the window is −1.8 m). A hybrid strategy is a combina-
tion of a rotation strategy and a sliding strategy. The PVSD has a fixed width and can
rotate at the same time that it slides. These three dynamic strategies are controlled by a set
schedule and are activated monthly. The rotation and sliding of the PVSD panels are re-
alized through gears and slide rails. The PVSD panel has three layers, the glass on the
outside, the silicon wafer in the middle, and the base plate (plastic) at the back. The three
Figure 3. Air conditioning schedule: (a) Heating setpoint schedule; (b) Cooling setpoint schedule.
2.3. PVSD Control Strategies
Figure 4shows the dynamic photovoltaic shading devices (PVSDs) installed on the
south wall. Among them, the red arrow represents the solar altitude angle at the summer
solstice, and the blue arrow represents the solar altitude angle at the winter solstice. There
are three design variables for PVSDs, namely the width, tilt angle, and sliding height. The
range and interval control of the variables are shown in Table 3. This study considers the
impact of three dynamic strategies of PVSDs (rotation, sliding up and down, and hybrid) on
indoor daylighting and energy consumption. In the case of the rotation strategy, the width
and installation height of the PVSD are fixed and can be rotated at intervals of 5
◦
, between
0
◦
and 70
◦
. In the case of the sliding strategy, the width and tilt angle of the PVSD are fixed
and can slide up and down along the guide rails. The highest point can slide to 1 m above
the upper eaves of the window, and the lowest point can slide to the lower eaves of the
window. The interval between each slide is 0.2 m (the upper eave of the window is 0 m, and
the lower eave of the window is
−
1.8 m). A hybrid strategy is a combination of a rotation
strategy and a sliding strategy. The PVSD has a fixed width and can rotate at the same
time that it slides. These three dynamic strategies are controlled by a set schedule and are
activated monthly. The rotation and sliding of the PVSD panels are realized through gears
and slide rails. The PVSD panel has three layers, the glass on the outside, the silicon wafer
in the middle, and the base plate (plastic) at the back. The three layers are held together by
an aluminum frame and are fixed and sealed with some adhesive. In the simulation, the
power generation capacity of each square meter of photovoltaic panel is set at 160 W.
Table 3. The range and interval control of variables.
Variables Range of Values Value Interval Unit
Width 0.2~1.2 0.2 Meter
Sliding height −1.8~1 0.2 Meter
Tilt angle 0~70 5 Degree

Buildings 2024,14, 596 6 of 22
Buildings 2024, 14, x FOR PEER REVIEW 6 of 23
layers are held together by an aluminum frame and are fixed and sealed with some adhe-
sive. In the simulation, the power generation capacity of each square meter of photovoltaic
panel is set at 160 W.
Table 3. The range and interval control of variables.
Variables Range of Values Value Interval Unit
Width 0.2~1.2 0.2 Meter
Sliding height −1.8~1 0.2 Meter
Tilt angle 0~70 5 Degree
Figure 4. Schematic diagram of PVSDs.
2.4. Parametric Performance Design Method
The parametric performance design method integrates parametric models and per-
formance evaluation to provide non-professionals with a visual toolbox to optimize and
evaluate design solutions. The parametric performance design method allows designers
to modify algorithms or rules, prompting the computer to generate multiple design solu-
tions and evaluate the performance of the solutions so that the designer can choose the
best design solution. In parametric performance design thinking, the generation, modifi-
cation, and evaluation of solutions are combined into a cyclic process that can be driven
by performance. This allows designers to pay aention to the relationship between pa-
rameters and goals and the feasibility of the solution during the design process, thereby
improving the quality and efficiency of the design. The parametric performance design
process of dynamic PVSDs is shown in Figure 5. As shown in Figure 5 (left), given certain
ideas, an architect or designer can input a large number of rules and parameters and let
the computer algorithmically generate and evaluate a large number of potential designs.
Architects or designers can choose reasonable and beer design solutions from the calcu-
lated results. Figure 5 (right) is a flowchart demonstrating the design generation process
in more detail. Firstly, fixed parameters (including meteorological data, loads and sched-
ules, HVAC systems, building construction, and building geometry) and dynamic param-
eters (including PVSD geometry, rotation angle, and movement height) are set to create a
parametric simulation model. The generated PVSD solutions are then evaluated based on
the evaluation metrics (energy consumption, power generation, and UDI). Finally, archi-
tects or designers can choose reasonable and beer solutions from the output results.
Figure 4. Schematic diagram of PVSDs.
2.4. Parametric Performance Design Method
The parametric performance design method integrates parametric models and per-
formance evaluation to provide non-professionals with a visual toolbox to optimize and
evaluate design solutions. The parametric performance design method allows designers to
modify algorithms or rules, prompting the computer to generate multiple design solutions
and evaluate the performance of the solutions so that the designer can choose the best
design solution. In parametric performance design thinking, the generation, modification,
and evaluation of solutions are combined into a cyclic process that can be driven by perfor-
mance. This allows designers to pay attention to the relationship between parameters and
goals and the feasibility of the solution during the design process, thereby improving the
quality and efficiency of the design. The parametric performance design process of dynamic
PVSDs is shown in Figure 5. As shown in Figure 5(left), given certain ideas, an architect or
designer can input a large number of rules and parameters and let the computer algorith-
mically generate and evaluate a large number of potential designs. Architects or designers
can choose reasonable and better design solutions from the calculated results. Figure 5
(right) is a flowchart demonstrating the design generation process in more detail. Firstly,
fixed parameters (including meteorological data, loads and schedules, HVAC systems,
building construction, and building geometry) and dynamic parameters (including PVSD
geometry, rotation angle, and movement height) are set to create a parametric simulation
model. The generated PVSD solutions are then evaluated based on the evaluation metrics
(energy consumption, power generation, and UDI). Finally, architects or designers can
choose reasonable and better solutions from the output results.

Buildings 2024,14, 596 7 of 22
Buildings 2024, 14, x FOR PEER REVIEW 7 of 23
Figure 5. Parametric performance design flow chart.
2.5. Evaluation Indicators
2.5.1. Daylighting Evaluation Indicators
The evaluation of indoor daylighting commonly utilizes the following two indicators
in China: the daylight factor (DF) and the illuminance (lux). While illuminance is a local
short-term indicator, evaluating the daylighting performance of the entire space over a
long period requires a lot of additional work [30]. DF is a ratio and does not show the
absolute value of illumination [31]. It ignores the impact of changes in climate conditions,
building orientation, location, etc., on daylighting [32]. Furthermore, neither illuminance
nor DF consider the impact of glare. To address these issues, a series of dynamic daylight-
ing evaluation indicators has been proposed. The most commonly used ones are daylight
autonomy (DA) and useful daylight illuminance (UDI). DA is defined as the percentage
of occupied hours of the year during which daylight meets a minimum illuminance
threshold [33]. Based on daylight autonomy, continuous daylight autonomy (cDA) [34]
and spatial daylight autonomy [35] have also been derived to describe the proportion of
time below the minimum illumination threshold and the adequacy of ambient daylight
levels in indoor environments, respectively.
UDI is the percentage of time during a period when indoor illumination levels are
within a certain range [30]. In comparison with DA, UDI sets upper and lower limits for
indoor illumination levels, allowing for the evaluation of indoor illumination levels and
limitation of glare occurrence [36]. The given illuminance range on a typical work plane
was originally suggested to be 100–2000 lux, which was later revised to 100–3000 lux [37].
According to Mardaljevic’s research, the need for indoor artificial lighting can be largely
eliminated when the UDI is in the range of 300–3000 lux [37]. When the illumination value
of the working plane is below 100 lux, it is difficult to perform basic visual tasks. When
the illumination value of the working surface is greater than 3000 lux, glare is likely to
occur [38]. As a result, the UDI target range for this study is set to 100–3000 lux, divided
into two parts, as follows: the supplementary useful illumination range of 100–300 lux and
the autonomous useful illumination range of 300–3000 lux, recorded as UDIlow (<100 lux),
UDIsup (100–300 lux), UDIauto (300–3000 lux), and UDIup (>3000 lux). The calculation
equation for UDI is as follows:
Figure 5. Parametric performance design flow chart.
2.5. Evaluation Indicators
2.5.1. Daylighting Evaluation Indicators
The evaluation of indoor daylighting commonly utilizes the following two indicators
in China: the daylight factor (DF) and the illuminance (lux). While illuminance is a
local short-term indicator, evaluating the daylighting performance of the entire space
over a long period requires a lot of additional work [
30
]. DF is a ratio and does not
show the absolute value of illumination [
31
]. It ignores the impact of changes in climate
conditions, building orientation, location, etc., on daylighting [
32
]. Furthermore, neither
illuminance nor DF consider the impact of glare. To address these issues, a series of
dynamic daylighting evaluation indicators has been proposed. The most commonly used
ones are daylight autonomy (DA) and useful daylight illuminance (UDI). DA is defined
as the percentage of occupied hours of the year during which daylight meets a minimum
illuminance threshold [
33
]. Based on daylight autonomy, continuous daylight autonomy
(cDA) [
34
] and spatial daylight autonomy [
35
] have also been derived to describe the
proportion of time below the minimum illumination threshold and the adequacy of ambient
daylight levels in indoor environments, respectively.
UDI is the percentage of time during a period when indoor illumination levels are
within a certain range [
30
]. In comparison with DA, UDI sets upper and lower limits for
indoor illumination levels, allowing for the evaluation of indoor illumination levels and
limitation of glare occurrence [
36
]. The given illuminance range on a typical work plane
was originally suggested to be 100–2000 lux, which was later revised to 100–3000 lux [
37
].
According to Mardaljevic’s research, the need for indoor artificial lighting can be largely
eliminated when the UDI is in the range of 300–3000 lux [
37
]. When the illumination value
of the working plane is below 100 lux, it is difficult to perform basic visual tasks. When
the illumination value of the working surface is greater than 3000 lux, glare is likely to
occur [
38
]. As a result, the UDI target range for this study is set to 100–3000 lux, divided
into two parts, as follows: the supplementary useful illumination range of 100–300 lux and
the autonomous useful illumination range of 300–3000 lux, recorded as UDIlow (<100 lux),

Buildings 2024,14, 596 8 of 22
UDIsup (100–300 lux), UDIauto (300–3000 lux), and UDIup (>3000 lux). The calculation
equation for UDI is as follows:
UD I =∑if i ∗ti
∑iti ∈[0, 1]























UD I low :f i =1Ei <100
0Ei ≥100
UD I su p :f i =1 100 ≤Ei ≤300
0Ei <100, Ei >300
UD I auto :f i =1 300 ≤Ei ≤3000
0Ei <300, Ei >3000
UD I u p :f i =1Ei >3000
0Ei ≤3000
(1)
where ti represents each occupied hour in the calculation time, fiis a weighting factor, and
Ei represents the illuminance value of each hour.
2.5.2. Energy Consumption Indicators
This paper uses energy use intensity (EUI) as the basic indicator to evaluate energy
consumption, intuitively quantifies the building’s energy consumption, and eliminates
the impact of room area on this indicator. EUI represents the ratio of building energy
consumption to the total building area within a certain period, in kWh/m
2
[
39
]. The
energy consumption calculated in this paper includes lighting energy consumption and
air conditioning energy consumption (cooling energy consumption and heating energy
consumption). In the following sections, EUI-a will be used to represent the annual
energy consumption intensity, and EUI-m will be used to represent the monthly energy
consumption intensity.
2.5.3. Model Validation Indicators
This paper uses the following two indicators to check the model’s accuracy: mean bias
error (MBE) and coefficient of variation of the root mean squared error (CV_RMSE). The
calculation equations for the MBE and CV_RMSE are listed in Equations (2) and (3) [
39
],
as follows:
MBE =Σn
i=1(Mi−Si)
Σn
i=1Mi
(%)(2)
CV(RMS E)=1
ysΣn
i=1(Mi−Si)2
n(%)(3)
where
Si
and
Mi
refer to the time intervals (i) of simulation and measurement, respectively,
yis the average value of measurement, and nis the total value for calculation.
3. Results
3.1. Model Validation
Figure 6shows the measured and simulated temperatures inside the building during
the test period. The MRE and CV(RMSE) values are 0.33% and 1.58%, respectively. Accord-
ing to the requirements of the ASHRAE Guideline (14-2014) [
40
], if the hourly MBE value is
within
±
10% and the hourly CV (RMSE) value is within 30%, the energy consumption sim-
ulation model verification is considered successful. Therefore, the error of the simulation
model used in this study is within the acceptable range.

Buildings 2024,14, 596 9 of 22
Buildings 2024, 14, x FOR PEER REVIEW 9 of 23
Figure 6. Comparison between measured and simulated indoor temperatures.
Table 4 shows the measured and simulated illumination distribution of the indoor
working plane at 10:30 and 12:30 on 16 December. The illumination of all of the grids in
the measured and simulated data is greater than 100 lux, and the distribution of illumina-
tion values is also reasonable. Judging by existing research, Merghani et al. [41] considered
an MBE of 20% and an RMSE of 32% to be acceptable in illumination simulation. In a
study of classroom daylight performance optimization in hot and dry areas, Khaoula [42]
found that the MBE and CV (RMSE) were −19.57% and 26.01%, respectivley. Yoon et al.
[43] studied six simulation algorithms and found that the CV (RMSE) of the daylighting
model ranged from 25.36% to 42.05%. The error can be reduced by using more optimized
modeling and measurement techniques [33]. These errors in the daylighting model are
acceptable due to the influence of measuring instruments, field tests, and various param-
eters in the modeling process (sky conditions, building materials, etc.). Therefore, these
models and related parameters will be used in the following research.
Table 4. Visualization of simulated and measured illuminance levels and their distribution at the
work plane level.
10:30 12:30
Measured
Average illumination: 3118.47 lux Average illumination: 1199.8 lux
12
15
18
21
24
27
123456789101112131415161718192021222324
Temprature (℃）
Measurement Simulation Outdoor
Figure 6. Comparison between measured and simulated indoor temperatures.
Table 4shows the measured and simulated illumination distribution of the indoor
working plane at 10:30 and 12:30 on 16 December. The illumination of all of the grids in the
measured and simulated data is greater than 100 lux, and the distribution of illumination
values is also reasonable. Judging by existing research, Merghani et al. [
41
] considered an
MBE of 20% and an RMSE of 32% to be acceptable in illumination simulation. In a study of
classroom daylight performance optimization in hot and dry areas, Khaoula [
42
] found that
the MBE and CV (RMSE) were
−
19.57% and 26.01%, respectivley. Yoon et al. [
43
] studied
six simulation algorithms and found that the CV (RMSE) of the daylighting model ranged
from 25.36% to 42.05%. The error can be reduced by using more optimized modeling
and measurement techniques [
33
]. These errors in the daylighting model are acceptable
due to the influence of measuring instruments, field tests, and various parameters in the
modeling process (sky conditions, building materials, etc.). Therefore, these models and
related parameters will be used in the following research.
Table 4. Visualization of simulated and measured illuminance levels and their distribution at the
work plane level.
10:30 12:30
Measured
Buildings 2024, 14, x FOR PEER REVIEW 9 of 23
Figure 6. Comparison between measured and simulated indoor temperatures.
Table 4 shows the measured and simulated illumination distribution of the indoor
working plane at 10:30 and 12:30 on 16 December. The illumination of all of the grids in
the measured and simulated data is greater than 100 lux, and the distribution of illumina-
tion values is also reasonable. Judging by existing research, Merghani et al. [41] considered
an MBE of 20% and an RMSE of 32% to be acceptable in illumination simulation. In a
study of classroom daylight performance optimization in hot and dry areas, Khaoula [42]
found that the MBE and CV (RMSE) were −19.57% and 26.01%, respectivley. Yoon et al.
[43] studied six simulation algorithms and found that the CV (RMSE) of the daylighting
model ranged from 25.36% to 42.05%. The error can be reduced by using more optimized
modeling and measurement techniques [33]. These errors in the daylighting model are
acceptable due to the influence of measuring instruments, field tests, and various param-
eters in the modeling process (sky conditions, building materials, etc.). Therefore, these
models and related parameters will be used in the following research.
Table 4. Visualization of simulated and measured illuminance levels and their distribution at the
work plane level.
10:30 12:30
Measured
Average illumination: 3118.47 lux Average illumination: 1199.8 lux
12
15
18
21
24
27
123456789101112131415161718192021222324
Temprature (℃）
Measurement Simulation Outdoor
Average illumination: 3118.47 lux
Buildings 2024, 14, x FOR PEER REVIEW 9 of 23
Figure 6. Comparison between measured and simulated indoor temperatures.
Table 4 shows the measured and simulated illumination distribution of the indoor
working plane at 10:30 and 12:30 on 16 December. The illumination of all of the grids in
the measured and simulated data is greater than 100 lux, and the distribution of illumina-
tion values is also reasonable. Judging by existing research, Merghani et al. [41] considered
an MBE of 20% and an RMSE of 32% to be acceptable in illumination simulation. In a
study of classroom daylight performance optimization in hot and dry areas, Khaoula [42]
found that the MBE and CV (RMSE) were −19.57% and 26.01%, respectivley. Yoon et al.
[43] studied six simulation algorithms and found that the CV (RMSE) of the daylighting
model ranged from 25.36% to 42.05%. The error can be reduced by using more optimized
modeling and measurement techniques [33]. These errors in the daylighting model are
acceptable due to the influence of measuring instruments, field tests, and various param-
eters in the modeling process (sky conditions, building materials, etc.). Therefore, these
models and related parameters will be used in the following research.
Table 4. Visualization of simulated and measured illuminance levels and their distribution at the
work plane level.
10:30 12:30
Measured
Average illumination: 3118.47 lux Average illumination: 1199.8 lux
12
15
18
21
24
27
123456789101112131415161718192021222324
Temprature (℃）
Measurement Simulation Outdoor
Average illumination: 1199.8 lux

Buildings 2024,14, 596 10 of 22
Table 4. Cont.
10:30 12:30
Simulated
Buildings 2024, 14, x FOR PEER REVIEW 10 of 23
Simulated
Average illumination: 3096.91 lux Average illumination: 1309.63 lux
MBE 0.69% −9.15%
CV (RMSE) 21.78 15.53%
3.2. Energy Consumption and Daylighting Performance of Fixed PVSDs
3.2.1. The Impact of Fixed PVSDs on Building Energy Consumption and Daylighting
Figure 7 shows the impact of year-round fixed PVSDs on indoor energy consump-
tion. It has been found that, as the width of the PVSDs increases, the average annual net
energy use intensity (EUI) throughout the year gradually decreases. The panel width in-
creased from 0.2 m to 1.2 m, and the average annual net EUI of the PVSDs decreased from
42.89 kWh/m2 to 30.57 kWh/m2. This also shows that, in Qingdao, PVSD power generation
capacity plays a leading role in the annual energy consumption. At the same time, it can
also be seen that, as the panel tilt angle increases from 0° to 70°, the average annual net
EUI of PVSDs first decreases and then increases. When the panel tilt angle is 20°, the av-
erage annual net EUI of all shading solutions is the lowest, at 36.1 kWh/m2. The influence
of the panel installation height on the annual net EUI shows different trends on the above
and below the reference position (0 m). As the panel installation position arises from the
boom of the window frame (−1.8 m) to the top of the window frame (0 m), the annual
average net EUI of PVSDs continues to increase. As the panel installation position arises
from the top of the window frame (0 m) to 1 m above the window, the average annual net
EUI continues to decrease. When the PVSDs are installed at the boom of the window
frame (−1.8 m), the average annual net EUI is the lowest, at 34.53 kWh/m2. When the in-
stallation position is at the top of the window frame (0 m), the maximum average annual
net EUI is 38.82 kWh/m2.
Average illumination: 3096.91 lux
Buildings 2024, 14, x FOR PEER REVIEW 10 of 23
Simulated
Average illumination: 3096.91 lux Average illumination: 1309.63 lux
MBE 0.69% −9.15%
CV (RMSE) 21.78 15.53%
3.2. Energy Consumption and Daylighting Performance of Fixed PVSDs
3.2.1. The Impact of Fixed PVSDs on Building Energy Consumption and Daylighting
Figure 7 shows the impact of year-round fixed PVSDs on indoor energy consump-
tion. It has been found that, as the width of the PVSDs increases, the average annual net
energy use intensity (EUI) throughout the year gradually decreases. The panel width in-
creased from 0.2 m to 1.2 m, and the average annual net EUI of the PVSDs decreased from
42.89 kWh/m2 to 30.57 kWh/m2. This also shows that, in Qingdao, PVSD power generation
capacity plays a leading role in the annual energy consumption. At the same time, it can
also be seen that, as the panel tilt angle increases from 0° to 70°, the average annual net
EUI of PVSDs first decreases and then increases. When the panel tilt angle is 20°, the av-
erage annual net EUI of all shading solutions is the lowest, at 36.1 kWh/m2. The influence
of the panel installation height on the annual net EUI shows different trends on the above
and below the reference position (0 m). As the panel installation position arises from the
boom of the window frame (−1.8 m) to the top of the window frame (0 m), the annual
average net EUI of PVSDs continues to increase. As the panel installation position arises
from the top of the window frame (0 m) to 1 m above the window, the average annual net
EUI continues to decrease. When the PVSDs are installed at the boom of the window
frame (−1.8 m), the average annual net EUI is the lowest, at 34.53 kWh/m2. When the in-
stallation position is at the top of the window frame (0 m), the maximum average annual
net EUI is 38.82 kWh/m2.
Average illumination: 1309.63 lux
MBE 0.69% −9.15%
CV (RMSE) 21.78 15.53%
3.2. Energy Consumption and Daylighting Performance of Fixed PVSDs
3.2.1. The Impact of Fixed PVSDs on Building Energy Consumption and Daylighting
Figure 7shows the impact of year-round fixed PVSDs on indoor energy consumption.
It has been found that, as the width of the PVSDs increases, the average annual net
energy use intensity (EUI) throughout the year gradually decreases. The panel width
increased from 0.2 m to 1.2 m, and the average annual net EUI of the PVSDs decreased
from
42.89 kWh/m2
to 30.57 kWh/m
2
. This also shows that, in Qingdao, PVSD power
generation capacity plays a leading role in the annual energy consumption. At the same
time, it can also be seen that, as the panel tilt angle increases from 0
◦
to 70
◦
, the average
annual net EUI of PVSDs first decreases and then increases. When the panel tilt angle is
20
◦
, the average annual net EUI of all shading solutions is the lowest, at 36.1 kWh/m
2
. The
influence of the panel installation height on the annual net EUI shows different trends on
the above and below the reference position (0 m). As the panel installation position arises
from the bottom of the window frame (
−
1.8 m) to the top of the window frame (0 m), the
annual average net EUI of PVSDs continues to increase. As the panel installation position
arises from the top of the window frame (0 m) to 1 m above the window, the average annual
net EUI continues to decrease. When the PVSDs are installed at the bottom of the window
frame (
−
1.8 m), the average annual net EUI is the lowest, at 34.53 kWh/m
2
. When the
installation position is at the top of the window frame (0 m), the maximum average annual
net EUI is 38.82 kWh/m2.
The influence of PVSDs on indoor UDI (100 lux < Illuminance < 3000 lux) is shown
in Figure 8. It can be seen from the figure that the influence trends of PVSDs on UDI
throughout the year are different. When the panel width is large, the panel tilt angle is
large, or the PVSD installation position is close to the upper edge of the window (0 m), the
UDI distribution is more discrete. As the panel width increases, the average annual UDI
first increases and then decreases. When the panel width is 0.4 m, the maximum average
annual UDI is 79.92%. As the panel tilt angle increases, the average annual UDI gradually
decreases. When the panel tilt angle is 0
◦
, the maximum average annual UDI is 80.52%.
The influence of PVSD installation height on UDI is the same on the above and below the
reference position (0 m). The average UDI throughout the year first increases and then
decreases as the installation height increases. When installed at the reference position
(0 m)
,

Buildings 2024,14, 596 11 of 22
the average annual UDI is the lowest, at 76.49%, and when the installation height is
−1.2 m,
the average annual UDI is at the maximum of 80.77%.
Buildings 2024, 14, x FOR PEER REVIEW 11 of 23
Figure 7. Impact of PVSDs on energy consumption.
The influence of PVSDs on indoor UDI (100 lux < Illuminance < 3000 lux) is shown in
Figure 8. It can be seen from the figure that the influence trends of PVSDs on UDI through-
out the year are different. When the panel width is large, the panel tilt angle is large, or
the PVSD installation position is close to the upper edge of the window (0 m), the UDI
distribution is more discrete. As the panel width increases, the average annual UDI first
increases and then decreases. When the panel width is 0.4 m, the maximum average an-
nual UDI is 79.92%. As the panel tilt angle increases, the average annual UDI gradually
decreases. When the panel tilt angle is 0°, the maximum average annual UDI is 80.52%.
The influence of PVSD installation height on UDI is the same on the above and below the
reference position (0 m). The average UDI throughout the year first increases and then
decreases as the installation height increases. When installed at the reference position (0
m), the average annual UDI is the lowest, at 76.49%, and when the installation height is
−1.2 m, the average annual UDI is at the maximum of 80.77%.
Figure 8. The impact of PVSDs on daylighting.
Generally speaking, PVSDs have different trends and influences on the indoor day-
lighting and energy consumption of buildings. For the annual net EUI, PVSD width has
the greatest impact, followed by the installation height, and the panel tilt angle has the
least impact. For UDI, the installation height of PVSDs has the greatest impact, followed
by the panel tilt angle, and the panel width has the smallest impact.
Figure 7. Impact of PVSDs on energy consumption.
Buildings 2024, 14, x FOR PEER REVIEW 11 of 23
Figure 7. Impact of PVSDs on energy consumption.
The influence of PVSDs on indoor UDI (100 lux < Illuminance < 3000 lux) is shown in
Figure 8. It can be seen from the figure that the influence trends of PVSDs on UDI through-
out the year are different. When the panel width is large, the panel tilt angle is large, or
the PVSD installation position is close to the upper edge of the window (0 m), the UDI
distribution is more discrete. As the panel width increases, the average annual UDI first
increases and then decreases. When the panel width is 0.4 m, the maximum average an-
nual UDI is 79.92%. As the panel tilt angle increases, the average annual UDI gradually
decreases. When the panel tilt angle is 0°, the maximum average annual UDI is 80.52%.
The influence of PVSD installation height on UDI is the same on the above and below the
reference position (0 m). The average UDI throughout the year first increases and then
decreases as the installation height increases. When installed at the reference position (0
m), the average annual UDI is the lowest, at 76.49%, and when the installation height is
−1.2 m, the average annual UDI is at the maximum of 80.77%.
Figure 8. The impact of PVSDs on daylighting.
Generally speaking, PVSDs have different trends and influences on the indoor day-
lighting and energy consumption of buildings. For the annual net EUI, PVSD width has
the greatest impact, followed by the installation height, and the panel tilt angle has the
least impact. For UDI, the installation height of PVSDs has the greatest impact, followed
by the panel tilt angle, and the panel width has the smallest impact.
Figure 8. The impact of PVSDs on daylighting.
Generally speaking, PVSDs have different trends and influences on the indoor day-
lighting and energy consumption of buildings. For the annual net EUI, PVSD width has the
greatest impact, followed by the installation height, and the panel tilt angle has the least
impact. For UDI, the installation height of PVSDs has the greatest impact, followed by the
panel tilt angle, and the panel width has the smallest impact.
3.2.2. Energy-Saving and Daylighting Potential of Fixed PVSDs
The simulation results of all 1350 solutions were sorted, and we found that, when
the width is 1.2 m, the installation height is
−
1.8 m, the tilt angle is 35
◦
, and the net EUI
of the fixed PVSDs throughout the year is the lowest, at 25.49 kWh/m
2
. At this time,
PVSDs only have the function of generating electricity and do not have the function of
shading. Comparing the cooling, heating, and lighting energy consumption of buildings
with and without photovoltaic shading devices, the differences are small. This shows that,
in Qingdao, fixed shading throughout the year has no energy-saving effect. Although
the Qingdao area has a relatively large demand for cooling in summer, it also has a large
demand for heating in winter. The fixed shading cannot avoid increasing the heating

Buildings 2024,14, 596 12 of 22
energy in winter. It can also be seen from Figure 9that PV power generation can account
for 44% of the total energy consumption throughout the year. In March and April, PV
power generation still has residual power after offsetting the cooling load, heating load,
and artificial lighting load. This also confirms that Qingdao has abundant solar energy
resources and that PVSDs have huge application prospects in Qingdao.
Buildings 2024, 14, x FOR PEER REVIEW 12 of 23
3.2.2. Energy-Saving and Daylighting Potential of Fixed PVSDs
The simulation results of all 1350 solutions were sorted, and we found that, when the
width is 1.2 m, the installation height is −1.8 m, the tilt angle is 35°, and the net EUI of the
fixed PVSDs throughout the year is the lowest, at 25.49 kWh/m2. At this time, PVSDs only
have the function of generating electricity and do not have the function of shading. Com-
paring the cooling, heating, and lighting energy consumption of buildings with and with-
out photovoltaic shading devices, the differences are small. This shows that, in Qingdao,
fixed shading throughout the year has no energy-saving effect. Although the Qingdao
area has a relatively large demand for cooling in summer, it also has a large demand for
heating in winter. The fixed shading cannot avoid increasing the heating energy in winter.
It can also be seen from Figure 9 that PV power generation can account for 44% of the total
energy consumption throughout the year. In March and April, PV power generation still
has residual power after offseing the cooling load, heating load, and artificial lighting
load. This also confirms that Qingdao has abundant solar energy resources and that
PVSDs have huge application prospects in Qingdao.
Figure 9. Monthly energy consumption of fixed PVSDs.
In terms of daylighting, when the width of fixed PVSDs is 1.2 m, the installation
height is −1 m, and the tilt angle is 10° throughout the year, indoor daylighting is the best.
This is because PVSDs allow sunlight to enter the room from the upper part of the window
and block sunlight from entering the room from the lower part of the window. This not
only reduces excess daylighting near the windows, but also contributes to indoor day-
lighting uniformity. Compared with having no PVSDs, fixed PVSDs with optimal day-
lighting throughout the year reduced indoor UDIup (>3000 lux) by 4.2%. They also re-
duced UDIauto (300–3000 lux) by 2.8%, increased UDIlow (<100 lux) by 2%, and increased
UDIsup (100–300 lux) by 5%. Figure 10 shows the visualization of daylighting without
PVSDs and optimal daylighting throughout the year with fixed PVSDs. It can also be seen
from the figure that PVSDs greatly reduce the proportion of excessive illumination in ar-
eas near windows. At the same time, they also increase the proportion of insufficient illu-
mination deep in the room, reducing the risk of glare and direct exposure. The indoor
illumination distribution is more even, and the UDI (100–3000 lux) increases by 2.2%.
Figure 9. Monthly energy consumption of fixed PVSDs.
In terms of daylighting, when the width of fixed PVSDs is 1.2 m, the installation
height is
−
1 m, and the tilt angle is 10
◦
throughout the year, indoor daylighting is the
best. This is because PVSDs allow sunlight to enter the room from the upper part of the
window and block sunlight from entering the room from the lower part of the window.
This not only reduces excess daylighting near the windows, but also contributes to indoor
daylighting uniformity. Compared with having no PVSDs, fixed PVSDs with optimal
daylighting throughout the year reduced indoor UDIup (>3000 lux) by 4.2%. They also
reduced UDIauto (300–3000 lux) by 2.8%, increased UDIlow (<100 lux) by 2%, and increased
UDIsup (100–300 lux) by 5%. Figure 10 shows the visualization of daylighting without
PVSDs and optimal daylighting throughout the year with fixed PVSDs. It can also be
seen from the figure that PVSDs greatly reduce the proportion of excessive illumination
in areas near windows. At the same time, they also increase the proportion of insufficient
illumination deep in the room, reducing the risk of glare and direct exposure. The indoor
illumination distribution is more even, and the UDI (100–3000 lux) increases by 2.2%.
Buildings 2024, 14, x FOR PEER REVIEW 13 of 23
(a) (b)
Figure 10. Daylighting visualization: (a) Visualization of daylighting throughout the year without
shading; (b) Visualization of daylighting throughout the year under fixed PVSD conditions.
3.3. The Impact of Dynamic PVSDs on Daylighting
3.3.1. The Impact of Rotation Strategy on Daylighting
By rotating the tilt angle of PVSDs, they can beer adapt to the impact of changes in
the sun’s altitude angle on indoor daylighting, effectively improving the indoor daylight-
ing levels. Figure 11 shows the impact of adjusting the tilt angle of PVSDs at different
installation heights on indoor daylighting in January and July when the width of the
PVSDs is 1.2 m. It can be seen from the figure that changes in the panel tilt angle at differ-
ent installation heights have different effects on the indoor daylighting. When the instal-
lation height is 0 m, changes in the panel tilt angle have the greatest impact on indoor
daylighting. Taking the PVSD installation height of 0 m as an example, as the panel tilt
angle increases, UDIauto and UDIup can decrease by a maximum of 25.6% and 5%, re-
spectively, in January, while UDIsup and UDIlow can increase by a maximum of 17.4%
and 14.6%, respectively. In July, UDIlow increased by 33.7%, while UDIup, UDIauto, and
UDIsup decreased by a maximum of 0.5%, 25.2%, and 8.6%, respectively. This shows that
the rotation strategy has a greater impact on indoor daylighting in July and will signifi-
cantly increase the proportion of areas with insufficient illumination. In January, it can
effectively limit the proportion of excessive illumination and also increase the proportion
of supplementary useful illumination.
(a) (b)
Figure 11. The impact of rotation strategy on daylighting: (a) January; (b) July.
Figure 10. Daylighting visualization: (a) Visualization of daylighting throughout the year without
shading; (b) Visualization of daylighting throughout the year under fixed PVSD conditions.

Buildings 2024,14, 596 13 of 22
3.3. The Impact of Dynamic PVSDs on Daylighting
3.3.1. The Impact of Rotation Strategy on Daylighting
By rotating the tilt angle of PVSDs, they can better adapt to the impact of changes in
the sun’s altitude angle on indoor daylighting, effectively improving the indoor daylighting
levels. Figure 11 shows the impact of adjusting the tilt angle of PVSDs at different instal-
lation heights on indoor daylighting in January and July when the width of the PVSDs is
1.2 m.
It can be seen from the figure that changes in the panel tilt angle at different installa-
tion heights have different effects on the indoor daylighting. When the installation height is
0 m, changes in the panel tilt angle have the greatest impact on indoor daylighting. Taking
the PVSD installation height of 0 m as an example, as the panel tilt angle increases, UDIauto
and UDIup can decrease by a maximum of 25.6% and 5%, respectively, in January, while
UDIsup and UDIlow can increase by a maximum of 17.4% and 14.6%, respectively. In July,
UDIlow increased by 33.7%, while UDIup, UDIauto, and UDIsup decreased by a maximum
of 0.5%, 25.2%, and 8.6%, respectively. This shows that the rotation strategy has a greater
impact on indoor daylighting in July and will significantly increase the proportion of areas
with insufficient illumination. In January, it can effectively limit the proportion of excessive
illumination and also increase the proportion of supplementary useful illumination.
Buildings 2024, 14, x FOR PEER REVIEW 13 of 23
(a) (b)
Figure 10. Daylighting visualization: (a) Visualization of daylighting throughout the year without
shading; (b) Visualization of daylighting throughout the year under fixed PVSD conditions.
3.3. The Impact of Dynamic PVSDs on Daylighting
3.3.1. The Impact of Rotation Strategy on Daylighting
By rotating the tilt angle of PVSDs, they can beer adapt to the impact of changes in
the sun’s altitude angle on indoor daylighting, effectively improving the indoor daylight-
ing levels. Figure 11 shows the impact of adjusting the tilt angle of PVSDs at different
installation heights on indoor daylighting in January and July when the width of the
PVSDs is 1.2 m. It can be seen from the figure that changes in the panel tilt angle at differ-
ent installation heights have different effects on the indoor daylighting. When the instal-
lation height is 0 m, changes in the panel tilt angle have the greatest impact on indoor
daylighting. Taking the PVSD installation height of 0 m as an example, as the panel tilt
angle increases, UDIauto and UDIup can decrease by a maximum of 25.6% and 5%, re-
spectively, in January, while UDIsup and UDIlow can increase by a maximum of 17.4%
and 14.6%, respectively. In July, UDIlow increased by 33.7%, while UDIup, UDIauto, and
UDIsup decreased by a maximum of 0.5%, 25.2%, and 8.6%, respectively. This shows that
the rotation strategy has a greater impact on indoor daylighting in July and will signifi-
cantly increase the proportion of areas with insufficient illumination. In January, it can
effectively limit the proportion of excessive illumination and also increase the proportion
of supplementary useful illumination.
(a) (b)
Figure 11. The impact of rotation strategy on daylighting: (a) January; (b) July.
Figure 11. The impact of rotation strategy on daylighting: (a) January; (b) July.
3.3.2. The Impact of Sliding Strategy on Daylighting
By sliding the PVSD up and down, the position of sunlight entering the room can
be controlled, and the level of daylighting in the room increases. Of course, this sliding
strategy will also be affected by the tilt angle of the PVSDs. Figure 12 shows the impact of
adjusting the PVSD height on indoor daylighting at different inclination angles when the
width of the PVSD is 1.2 m in January and July. It has been found that the larger the panel
tilt angle, the more obvious the effect of the sliding strategy on daylighting. In July, as
the sliding height increases, the changing trends of UDIsup at different inclination angles
are different. When the tilt angle is 60
◦
, the positions above and below 0 m show a trend
of first increasing and then decreasing, which is the result of the joint influence of the tilt
angle and the height. When the height slides from
−
1.8 m to 0 m, UDIlow increases by
31.5% and UDIauto decreases by 29.6%. When the height is
−
1.8 m, UDI (100–3000 lux)
achieves the optimal value. In January, when the inclination angle is 60
◦
and the height
is
−
0.4 m, UDIsup achieves the minimum value, which is 10.6% lower than that found
without shading. When the height is
−
1 m, UDI (100–3000 lux) achieves a maximum value
of 73.2%, which is 4.7% higher than that found without shading.

Buildings 2024,14, 596 14 of 22
Buildings 2024, 14, x FOR PEER REVIEW 14 of 23
3.3.2. The Impact of Sliding Strategy on Daylighting
By sliding the PVSD up and down, the position of sunlight entering the room can be
controlled, and the level of daylighting in the room increases. Of course, this sliding strat-
egy will also be affected by the tilt angle of the PVSDs. Figure 12 shows the impact of
adjusting the PVSD height on indoor daylighting at different inclination angles when the
width of the PVSD is 1.2 m in January and July. It has been found that the larger the panel
tilt angle, the more obvious the effect of the sliding strategy on daylighting. In July, as the
sliding height increases, the changing trends of UDIsup at different inclination angles are
different. When the tilt angle is 60°, the positions above and below 0 m show a trend of
first increasing and then decreasing, which is the result of the joint influence of the tilt
angle and the height. When the height slides from −1.8 m to 0 m, UDIlow increases by
31.5% and UDIauto decreases by 29.6%. When the height is −1.8 m, UDI (100–3000 lux)
achieves the optimal value. In January, when the inclination angle is 60° and the height is
−0.4 m, UDIsup achieves the minimum value, which is 10.6% lower than that found with-
out shading. When the height is −1 m, UDI (100–3000 lux) achieves a maximum value of
73.2%, which is 4.7% higher than that found without shading.
(a) (b)
Figure 12. The impact of sliding strategy on daylighting: (a) January; (b) July.
3.3.3. The Impact of Hybrid Strategy on Daylighting
The hybrid strategy is a shading control strategy that combines rotation and sliding.
It can combine the advantages of the rotation strategy and the sliding strategy to control
indoor daylighting more flexibly. In Figure 13, the impact of the rotation angle and the
sliding height on UDI is shown in the form of a heat map. It has been found that, no maer
whether the results are taken in January or July, when the tilt angle is 70° and the instal-
lation height is 0 m, the UDI is the smallest. The difference is that, in July, the UDI ex-
panded outward from the minimum. In January, the distribution of larger UDI values was
more discrete, and mainly concentrated at −1 m. Therefore, it can be judged that, in Janu-
ary, the impact of the height on UDI is greater than that of the tilt angle. Blocking the
daylighting from the lower part of the windows is more conducive to improving UDI. In
July, PVSDs will reduce UDI, while having no PVSDs will achieve beer daylighting.
Figure 12. The impact of sliding strategy on daylighting: (a) January; (b) July.
3.3.3. The Impact of Hybrid Strategy on Daylighting
The hybrid strategy is a shading control strategy that combines rotation and sliding.
It can combine the advantages of the rotation strategy and the sliding strategy to control
indoor daylighting more flexibly. In Figure 13, the impact of the rotation angle and the
sliding height on UDI is shown in the form of a heat map. It has been found that, no
matter whether the results are taken in January or July, when the tilt angle is 70
◦
and the
installation height is 0 m, the UDI is the smallest. The difference is that, in July, the UDI
expanded outward from the minimum. In January, the distribution of larger UDI values
was more discrete, and mainly concentrated at
−
1 m. Therefore, it can be judged that, in
January, the impact of the height on UDI is greater than that of the tilt angle. Blocking the
daylighting from the lower part of the windows is more conducive to improving UDI. In
July, PVSDs will reduce UDI, while having no PVSDs will achieve better daylighting.
Buildings 2024, 14, x FOR PEER REVIEW 15 of 23
(a) (b)
Figure 13. The impact of hybrid strategy on daylighting: (a) January; (b) July.
3.4. Impact of Dynamic PVSDs on Energy Consumption
3.4.1. The Impact of Dynamic PVSDs on Power Generation
The PVSD power generation is affected by the photovoltaic area, tilt angle, solar ra-
diation, and shading factors. In this study, photovoltaic power generation is mainly af-
fected by the tilt angle and solar radiation. Figure 14 shows the optimal monthly power
generation and the corresponding shading parameters for the three strategies of dynamic
PVSDs. The annual photovoltaic power generation in a rotating (height is 0 m), sliding
(tilt angle is 35°), and hybrid strategy are 20.6 kWh/m2, 20.46 kWh/m2, and 21.25 kWh/m2,
respectively. Among them, since the amount of solar radiation is the largest from March
to May, the photovoltaic power generation is the largest, which is consistent with the re-
sults of the meteorological data analysis. In the rotation strategy, the PVSD can be rotated
to the most suitable angle for power generation in the current month, as shown in Figure
14a. The tilt angle suitable for power generation is smaller in summer and larger in winter,
which is related to the changes in the solar altitude angle throughout the year. In the slid-
ing strategy, the direct solar radiation received by the PVSD changes slightly. However,
the difference in the reflective capabilities of the building exterior walls and window glass
materials can affect the indirect solar radiation received by the PVSDs. This also results in
different heights corresponding to different power generation amounts (but this effect is
small), as shown in Figure 14b. In the hybrid strategy, due to the influence of the sliding
height, the monthly optimal power generation angle changes slightly, but the annual
trend remains unchanged, as shown in Figure 14c.
(a) (b)
Figure 13. The impact of hybrid strategy on daylighting: (a) January; (b) July.
3.4. Impact of Dynamic PVSDs on Energy Consumption
3.4.1. The Impact of Dynamic PVSDs on Power Generation
The PVSD power generation is affected by the photovoltaic area, tilt angle, solar
radiation, and shading factors. In this study, photovoltaic power generation is mainly
affected by the tilt angle and solar radiation. Figure 14 shows the optimal monthly power

Buildings 2024,14, 596 15 of 22
generation and the corresponding shading parameters for the three strategies of dynamic
PVSDs. The annual photovoltaic power generation in a rotating (height is 0 m), sliding (tilt
angle is 35
◦
), and hybrid strategy are 20.6 kWh/m
2
, 20.46 kWh/m
2,
and 21.25 kWh/m
2
,
respectively. Among them, since the amount of solar radiation is the largest from March to
May, the photovoltaic power generation is the largest, which is consistent with the results
of the meteorological data analysis. In the rotation strategy, the PVSD can be rotated to the
most suitable angle for power generation in the current month, as shown in Figure 14a. The
tilt angle suitable for power generation is smaller in summer and larger in winter, which
is related to the changes in the solar altitude angle throughout the year. In the sliding
strategy, the direct solar radiation received by the PVSD changes slightly. However, the
difference in the reflective capabilities of the building exterior walls and window glass
materials can affect the indirect solar radiation received by the PVSDs. This also results in
different heights corresponding to different power generation amounts (but this effect is
small), as shown in Figure 14b. In the hybrid strategy, due to the influence of the sliding
height, the monthly optimal power generation angle changes slightly, but the annual trend
remains unchanged, as shown in Figure 14c.
Buildings 2024, 14, x FOR PEER REVIEW 15 of 23
(a) (b)
Figure 13. The impact of hybrid strategy on daylighting: (a) January; (b) July.
3.4. Impact of Dynamic PVSDs on Energy Consumption
3.4.1. The Impact of Dynamic PVSDs on Power Generation
The PVSD power generation is affected by the photovoltaic area, tilt angle, solar ra-
diation, and shading factors. In this study, photovoltaic power generation is mainly af-
fected by the tilt angle and solar radiation. Figure 14 shows the optimal monthly power
generation and the corresponding shading parameters for the three strategies of dynamic
PVSDs. The annual photovoltaic power generation in a rotating (height is 0 m), sliding
(tilt angle is 35°), and hybrid strategy are 20.6 kWh/m2, 20.46 kWh/m2, and 21.25 kWh/m2,
respectively. Among them, since the amount of solar radiation is the largest from March
to May, the photovoltaic power generation is the largest, which is consistent with the re-
sults of the meteorological data analysis. In the rotation strategy, the PVSD can be rotated
to the most suitable angle for power generation in the current month, as shown in Figure
14a. The tilt angle suitable for power generation is smaller in summer and larger in winter,
which is related to the changes in the solar altitude angle throughout the year. In the slid-
ing strategy, the direct solar radiation received by the PVSD changes slightly. However,
the difference in the reflective capabilities of the building exterior walls and window glass
materials can affect the indirect solar radiation received by the PVSDs. This also results in
different heights corresponding to different power generation amounts (but this effect is
small), as shown in Figure 14b. In the hybrid strategy, due to the influence of the sliding
height, the monthly optimal power generation angle changes slightly, but the annual
trend remains unchanged, as shown in Figure 14c.
(a) (b)
Buildings 2024, 14, x FOR PEER REVIEW 16 of 23
(c)
Figure 14. The impact of dynamic PVSDs on photovoltaic power generation: (a) The impact of rota-
tion strategy on photovoltaic power generation; (b) The impact of sliding strategy on photovoltaic
power generation; (c) The impact of hybrid strategy on photovoltaic power generation.
3.4.2. The Impact of Dynamic PVSDs on Energy Consumption
In the Qingdao area, the heating load dominates from November to April, and the
cooling load dominates from May to October. PVSDs can reduce indoor heat gain, which
is beneficial in summer but harmful in winter in terms of energy consumption. PVSDs will
also increase the use of artificial lighting, which will also have an impact on energy con-
sumption, although the impact is small. Figure 15 shows the optimal monthly energy con-
sumption for the three strategies of dynamic PVSDs. In the figure, “a” represents the ro-
tation strategy (the height is 0 m), “b” represents the sliding strategy (the tilt angle is 0°),
and “c” represents the hybrid strategy. The total annual energy consumption of the three
strategies is 38.91 kWh/m2, 35.38 kWh/m2, and 34.04 kWh/m2, respectively. In summer, the
rotation strategy has a greater impact on cooling energy consumption than the sliding
strategy, while in winter the sliding strategy has a greater impact on heating energy con-
sumption than the rotation strategy.
Figure 15. The impact of dynamic PVSDs on energy consumption.
3.4.3. The Impact of Dynamic PVSDs on Lighting Energy Consumption
Artificial lighting is a supplement to daylighting. In Qingdao, although PVSDs can
adjust the level of indoor daylighting, they will also increase the proportion of insufficient
indoor illumination (especially deep in a room). Figure 16 shows the impact of dynamic
PVSDs on lighting energy consumption. It has been found that, when the dynamic PVSDs
are at a height of 0 m and a tilt angle of 70° in January and July, the lighting energy con-
sumption reaches the maximum value and decreases diffusely to the surrounding areas.
The lighting energy consumption increases with the angle. Taking the height of 0 m as an
example, as the tilt angle increases, the lighting energy consumption increases by 0.2
Figure 14. The impact of dynamic PVSDs on photovoltaic power generation: (a) The impact of
rotation strategy on photovoltaic power generation; (b) The impact of sliding strategy on photovoltaic
power generation; (c) The impact of hybrid strategy on photovoltaic power generation.
3.4.2. The Impact of Dynamic PVSDs on Energy Consumption
In the Qingdao area, the heating load dominates from November to April, and the
cooling load dominates from May to October. PVSDs can reduce indoor heat gain, which
is beneficial in summer but harmful in winter in terms of energy consumption. PVSDs
will also increase the use of artificial lighting, which will also have an impact on energy
consumption, although the impact is small. Figure 15 shows the optimal monthly energy
consumption for the three strategies of dynamic PVSDs. In the figure, “a” represents the
rotation strategy (the height is 0 m), “b” represents the sliding strategy (the tilt angle is
0
◦
), and “c” represents the hybrid strategy. The total annual energy consumption of the

Buildings 2024,14, 596 16 of 22
three strategies is 38.91 kWh/m
2
, 35.38 kWh/m
2
, and 34.04 kWh/m
2
, respectively. In
summer, the rotation strategy has a greater impact on cooling energy consumption than
the sliding strategy, while in winter the sliding strategy has a greater impact on heating
energy consumption than the rotation strategy.
Buildings 2024, 14, x FOR PEER REVIEW 16 of 23
(c)
Figure 14. The impact of dynamic PVSDs on photovoltaic power generation: (a) The impact of rota-
tion strategy on photovoltaic power generation; (b) The impact of sliding strategy on photovoltaic
power generation; (c) The impact of hybrid strategy on photovoltaic power generation.
3.4.2. The Impact of Dynamic PVSDs on Energy Consumption
In the Qingdao area, the heating load dominates from November to April, and the
cooling load dominates from May to October. PVSDs can reduce indoor heat gain, which
is beneficial in summer but harmful in winter in terms of energy consumption. PVSDs will
also increase the use of artificial lighting, which will also have an impact on energy con-
sumption, although the impact is small. Figure 15 shows the optimal monthly energy con-
sumption for the three strategies of dynamic PVSDs. In the figure, “a” represents the ro-
tation strategy (the height is 0 m), “b” represents the sliding strategy (the tilt angle is 0°),
and “c” represents the hybrid strategy. The total annual energy consumption of the three
strategies is 38.91 kWh/m2, 35.38 kWh/m2, and 34.04 kWh/m2, respectively. In summer, the
rotation strategy has a greater impact on cooling energy consumption than the sliding
strategy, while in winter the sliding strategy has a greater impact on heating energy con-
sumption than the rotation strategy.
Figure 15. The impact of dynamic PVSDs on energy consumption.
3.4.3. The Impact of Dynamic PVSDs on Lighting Energy Consumption
Artificial lighting is a supplement to daylighting. In Qingdao, although PVSDs can
adjust the level of indoor daylighting, they will also increase the proportion of insufficient
indoor illumination (especially deep in a room). Figure 16 shows the impact of dynamic
PVSDs on lighting energy consumption. It has been found that, when the dynamic PVSDs
are at a height of 0 m and a tilt angle of 70° in January and July, the lighting energy con-
sumption reaches the maximum value and decreases diffusely to the surrounding areas.
The lighting energy consumption increases with the angle. Taking the height of 0 m as an
example, as the tilt angle increases, the lighting energy consumption increases by 0.2
Figure 15. The impact of dynamic PVSDs on energy consumption.
3.4.3. The Impact of Dynamic PVSDs on Lighting Energy Consumption
Artificial lighting is a supplement to daylighting. In Qingdao, although PVSDs can
adjust the level of indoor daylighting, they will also increase the proportion of insufficient
indoor illumination (especially deep in a room). Figure 16 shows the impact of dynamic
PVSDs on lighting energy consumption. It has been found that, when the dynamic PVSDs
are at a height of 0 m and a tilt angle of 70
◦
in January and July, the lighting energy
consumption reaches the maximum value and decreases diffusely to the surrounding
areas. The lighting energy consumption increases with the angle. Taking the height of
0 m
as an example, as the tilt angle increases, the lighting energy consumption increases
by 0.2 kWh/m
2
and 0.29 kWh/m
2
in January and July, respectively. The lighting energy
consumption first increases and then decreases with the increase in height, reaching the
maximum value at 0 m. In general, dynamic PVSDs have a greater impact on lighting
energy consumption in July. This is determined by the indoor daylighting conditions.
The greater the PVSD angle and the closer the PVSD height is to the upper eaves of the
window (0 m), the lower the indoor daylighting illumination and the greater the lighting
energy consumption.
Buildings 2024, 14, x FOR PEER REVIEW 17 of 23
kWh/m2 and 0.29 kWh/m2 in January and July, respectively. The lighting energy consump-
tion first increases and then decreases with the increase in height, reaching the maximum
value at 0 m. In general, dynamic PVSDs have a greater impact on lighting energy con-
sumption in July. This is determined by the indoor daylighting conditions. The greater the
PVSD angle and the closer the PVSD height is to the upper eaves of the window (0 m), the
lower the indoor daylighting illumination and the greater the lighting energy consump-
tion.
(a) (b)
Figure 16. The impact of dynamic PVSDs on lighting energy consumption: (a) January; (b) July.
3.5. Energy-Saving and Daylighting Potential of Dynamic PVSDs
3.5.1. Energy-Saving Potential of Dynamic PVSDs
In this section, we use net EUI as the evaluation index to explore the energy-saving
potential of the three strategies. In Section 3.2, it can be seen that the wider the width, the
more energy-saving potential it has; therefore, the width selected in this section is 1.2 m.
When the installation height is 0 m, the rotation strategy is adopted. When the panel tilt
angle is 20°, the sliding strategy is adopted. The optimal net energy consumption of the
three strategies is shown in Figure 17. From the perspective of energy saving, the annual
net EUI of the three strategies are 31.09 kWh/m2, 24.18 kWh/m2, and 22.73 kWh/m2, re-
spectively. The annual net EUI of the sliding strategy and the hybrid strategy are both
smaller than the annual net EUI of the fixed PVSD (25.49 kWh/m2). It is worth mentioning
that the position of the fixed PVSD is −1.8 m and has no shading effect. In the rotation
strategy, the height of the PVSD is 0 m, which has a shading effect, thus increasing the
heating energy consumption. Compared with the sliding and hybrid strategies, the rota-
tion strategy reduces the cooling energy consumption but increases the heating and arti-
ficial lighting energy consumption. However, the photovoltaic power generation is small,
and the energy-saving effect of the rotation strategy is not ideal. In the rotation strategy,
the PVSD tilt angle is smaller in winter, which is not conducive to power generation, but
is beneficial to reducing the heating energy consumption. This shows that the impact of
heating energy consumption on the net energy consumption is greater than that of pho-
tovoltaic power generation at this time. In the sliding strategy, the PVSD is located at the
lower eaves of the windows from November to May, indicating that shading is detri-
mental to energy conservation in these months. In the hybrid strategy, the tilt angle of the
PVSD becomes the most beneficial to power generation in winter, which is why the hybrid
strategy is beer than the rotating and sliding strategies.
Figure 16. The impact of dynamic PVSDs on lighting energy consumption: (a) January; (b) July.

Buildings 2024,14, 596 17 of 22
3.5. Energy-Saving and Daylighting Potential of Dynamic PVSDs
3.5.1. Energy-Saving Potential of Dynamic PVSDs
In this section, we use net EUI as the evaluation index to explore the energy-saving
potential of the three strategies. In Section 3.2, it can be seen that the wider the width, the
more energy-saving potential it has; therefore, the width selected in this section is 1.2 m.
When the installation height is 0 m, the rotation strategy is adopted. When the panel tilt
angle is 20
◦
, the sliding strategy is adopted. The optimal net energy consumption of the
three strategies is shown in Figure 17. From the perspective of energy saving, the annual
net EUI of the three strategies are 31.09 kWh/m
2
, 24.18 kWh/m
2
, and 22.73 kWh/m
2
,
respectively. The annual net EUI of the sliding strategy and the hybrid strategy are both
smaller than the annual net EUI of the fixed PVSD (25.49 kWh/m
2
). It is worth mentioning
that the position of the fixed PVSD is
−
1.8 m and has no shading effect. In the rotation
strategy, the height of the PVSD is 0 m, which has a shading effect, thus increasing the
heating energy consumption. Compared with the sliding and hybrid strategies, the rotation
strategy reduces the cooling energy consumption but increases the heating and artificial
lighting energy consumption. However, the photovoltaic power generation is small, and the
energy-saving effect of the rotation strategy is not ideal. In the rotation strategy, the PVSD
tilt angle is smaller in winter, which is not conducive to power generation, but is beneficial
to reducing the heating energy consumption. This shows that the impact of heating energy
consumption on the net energy consumption is greater than that of photovoltaic power
generation at this time. In the sliding strategy, the PVSD is located at the lower eaves of
the windows from November to May, indicating that shading is detrimental to energy
conservation in these months. In the hybrid strategy, the tilt angle of the PVSD becomes the
most beneficial to power generation in winter, which is why the hybrid strategy is better
than the rotating and sliding strategies.
Buildings 2024, 14, x FOR PEER REVIEW 18 of 23
(a) (b)
(c)
Figure 17. Energy-saving potential of dynamic PVSDs: (a) Rotation strategy; (b) Sliding strategy; (c)
Hybrid strategy.
3.5.2. Dynamic PVSD Daylighting Potential
This section explores the potential of dynamic PVSDs to improve daylighting with
the goal of UDI. Referring to the results in Section 3.2, this section takes the width of 1.2
m as an example. The rotation strategy is adopted when the installation height is −1.2 m,
and the sliding strategy is adopted when the inclination angle is 20°. The optimal UDI of
the three strategies is shown in Figure 18. The optimal average UDI of the three strategies
throughout the year is 81.8%, 82.3%, and 82.6%, respectively. Compared with the fixed
PVSD (81.7%) and having no PVSD (79.5%), the indoor daylighting level has improved.
Compared with having no PVSD, the three strategies reduce UDIup in each month, and
the reduction rate is the largest in January. The three strategies of rotation, sliding, and
mixing reduce UDIup by 6%, 7.6%, and 8.7%, respectively. The reduction rate of UDIup
in summer is smaller than that observed in winter. The three strategies improve UDIlow
in each month, and the improvement rate in winter is greater than that observed in sum-
mer. This is due to the fact that the sun has a lower altitude angle and shorter sunshine
hours in winter, therefore, direct sunlight has a greater impact on indoor daylighting.
PVSDs can block a large amount of direct sunlight from entering the room.
(a) (b)
123456789101112
0
20
40
60
80
100
UDI (%)
Figure 17. Energy-saving potential of dynamic PVSDs: (a) Rotation strategy; (b) Sliding strategy;
(c) Hybrid strategy.

Buildings 2024,14, 596 18 of 22
3.5.2. Dynamic PVSD Daylighting Potential
This section explores the potential of dynamic PVSDs to improve daylighting with
the goal of UDI. Referring to the results in Section 3.2, this section takes the width of
1.2 m
as an example. The rotation strategy is adopted when the installation height is
−
1.2 m,
and the sliding strategy is adopted when the inclination angle is 20
◦
. The optimal UDI of
the three strategies is shown in Figure 18. The optimal average UDI of the three strategies
throughout the year is 81.8%, 82.3%, and 82.6%, respectively. Compared with the fixed
PVSD (81.7%) and having no PVSD (79.5%), the indoor daylighting level has improved.
Compared with having no PVSD, the three strategies reduce UDIup in each month, and
the reduction rate is the largest in January. The three strategies of rotation, sliding, and
mixing reduce UDIup by 6%, 7.6%, and 8.7%, respectively. The reduction rate of UDIup in
summer is smaller than that observed in winter. The three strategies improve UDIlow in
each month, and the improvement rate in winter is greater than that observed in summer.
This is due to the fact that the sun has a lower altitude angle and shorter sunshine hours in
winter, therefore, direct sunlight has a greater impact on indoor daylighting. PVSDs can
block a large amount of direct sunlight from entering the room.
Buildings 2024, 14, x FOR PEER REVIEW 18 of 23
(a) (b)
(c)
Figure 17. Energy-saving potential of dynamic PVSDs: (a) Rotation strategy; (b) Sliding strategy; (c)
Hybrid strategy.
3.5.2. Dynamic PVSD Daylighting Potential
This section explores the potential of dynamic PVSDs to improve daylighting with
the goal of UDI. Referring to the results in Section 3.2, this section takes the width of 1.2
m as an example. The rotation strategy is adopted when the installation height is −1.2 m,
and the sliding strategy is adopted when the inclination angle is 20°. The optimal UDI of
the three strategies is shown in Figure 18. The optimal average UDI of the three strategies
throughout the year is 81.8%, 82.3%, and 82.6%, respectively. Compared with the fixed
PVSD (81.7%) and having no PVSD (79.5%), the indoor daylighting level has improved.
Compared with having no PVSD, the three strategies reduce UDIup in each month, and
the reduction rate is the largest in January. The three strategies of rotation, sliding, and
mixing reduce UDIup by 6%, 7.6%, and 8.7%, respectively. The reduction rate of UDIup
in summer is smaller than that observed in winter. The three strategies improve UDIlow
in each month, and the improvement rate in winter is greater than that observed in sum-
mer. This is due to the fact that the sun has a lower altitude angle and shorter sunshine
hours in winter, therefore, direct sunlight has a greater impact on indoor daylighting.
PVSDs can block a large amount of direct sunlight from entering the room.
(a) (b)
123456789101112
0
20
40
60
80
100
UDI (%)
Buildings 2024, 14, x FOR PEER REVIEW 19 of 23
(c) (d)
Figure 18. Dynamic PVSD daylighting potential: (a) Daylighting potential without PVSDs; (b) Ro-
tation strategy; (c) Sliding strategy; (d) Hybrid strategy.
4. Discussion
From the analysis of the results, in Qingdao, the main effect of PVSDs on indoor en-
ergy consumption is reflected in their power generation capacity. Although it is possible
to reduce a certain amount of cooling energy, the effect is limited. For indoor daylighting,
the biggest role of dynamic PVSDs is to significantly reduce the proportion of excessive
illumination, thereby effectively reducing the risk of glare. Of course, this will also in-
crease the proportion of insufficient indoor illumination, and a good control strategy can
weaken this deficiency. Therefore, in office buildings in cold areas, flexible and appropri-
ate dynamic control strategies can effectively enhance the application potential of PVSDs
in terms of energy saving and daylighting.
However, in dynamic PVSD design, it is often necessary to consider the costs of var-
ious dynamic control strategies. More flexible control often means higher costs. The per-
formance improvements brought by more complex control strategies are sometimes not
directly proportional to the cost. For example, in this study, the hybrid strategy and the
sliding strategy only reduced the net EUI by 2.76 kWh/m2 and 1.31 kWh/m2, respectivley,
compared with the optimized fixed PVSD. Of course, the fixed PVSD at this time cannot
improve indoor daylighting. The energy-saving performance of dynamic PVSDs will be
improved in other building types in other climate zones. Krarti [23] studied the energy-
saving performance of sliding (left–right sliding) and rotating strategies in US apartment
buildings and found that, in San Francisco, California, the use of dynamic PVSDs could
meet the entire energy needs of the apartments. Meysam [22] studied the performance of
movable PVSDs on the south windows of an apartment in Tehran and found that the
building’s annual heating load was 12% lower with movable PVSDs than with fixed
PVSDs. Their power generation can also meet part of the energy consumption needs of
the apartment. Only the dynamic control strategy of PVSDs adapted to local conditions
can meet the design requirements.
In addition, dynamic PVSDs can beer adjust the contradiction between daylighting
and energy saving. Although traditional fixed PVSDs can prevent glare in winter, they
will also significantly reduce the indoor heat gain and increase heating load. In summer,
although the cooling energy can be reduced, the proportion of insufficient illumination
will increase. Figure 19 shows a comparison of daylighting and energy consumption be-
tween fixed PVSDs (height 0 m, tilt angle 20°) and dynamic PVSDs. In terms of daylight-
ing, the best UDI scenario for dynamic PVSDs in January reduced UDIup by 2.02% and
increased UDIauto by 2.8%, compared with fixed PVSDs. In terms of energy consumption,
although heating energy consumption increased by 0.32 kWh/m2, photovoltaic power
generation also increased by 0.25 kWh/m2. Therefore, the best UDI scenario for dynamic
PVSDs in January not only improved indoor daylighting, but also reduced energy con-
sumption. In July, the optimal dynamic PVSDs for energy consumption also increased
UDIsup and UDIauto by 0.27% and 1.96%, respectively.
Figure 18. Dynamic PVSD daylighting potential: (a) Daylighting potential without PVSDs; (b) Rota-
tion strategy; (c) Sliding strategy; (d) Hybrid strategy.
4. Discussion
From the analysis of the results, in Qingdao, the main effect of PVSDs on indoor
energy consumption is reflected in their power generation capacity. Although it is possible
to reduce a certain amount of cooling energy, the effect is limited. For indoor daylighting,
the biggest role of dynamic PVSDs is to significantly reduce the proportion of excessive
illumination, thereby effectively reducing the risk of glare. Of course, this will also increase
the proportion of insufficient indoor illumination, and a good control strategy can weaken
this deficiency. Therefore, in office buildings in cold areas, flexible and appropriate dynamic
control strategies can effectively enhance the application potential of PVSDs in terms of
energy saving and daylighting.
However, in dynamic PVSD design, it is often necessary to consider the costs of
various dynamic control strategies. More flexible control often means higher costs. The
performance improvements brought by more complex control strategies are sometimes not

Buildings 2024,14, 596 19 of 22
directly proportional to the cost. For example, in this study, the hybrid strategy and the
sliding strategy only reduced the net EUI by 2.76 kWh/m
2
and 1.31 kWh/m
2
, respectivley,
compared with the optimized fixed PVSD. Of course, the fixed PVSD at this time cannot
improve indoor daylighting. The energy-saving performance of dynamic PVSDs will be
improved in other building types in other climate zones. Krarti [
23
] studied the energy-
saving performance of sliding (left–right sliding) and rotating strategies in US apartment
buildings and found that, in San Francisco, California, the use of dynamic PVSDs could
meet the entire energy needs of the apartments. Meysam [
22
] studied the performance
of movable PVSDs on the south windows of an apartment in Tehran and found that
the building’s annual heating load was 12% lower with movable PVSDs than with fixed
PVSDs. Their power generation can also meet part of the energy consumption needs of the
apartment. Only the dynamic control strategy of PVSDs adapted to local conditions can
meet the design requirements.
In addition, dynamic PVSDs can better adjust the contradiction between daylighting
and energy saving. Although traditional fixed PVSDs can prevent glare in winter, they
will also significantly reduce the indoor heat gain and increase heating load. In summer,
although the cooling energy can be reduced, the proportion of insufficient illumination will
increase. Figure 19 shows a comparison of daylighting and energy consumption between
fixed PVSDs (height 0 m, tilt angle 20
◦
) and dynamic PVSDs. In terms of daylighting, the
best UDI scenario for dynamic PVSDs in January reduced UDIup by 2.02% and increased
UDIauto by 2.8%, compared with fixed PVSDs. In terms of energy consumption, although
heating energy consumption increased by 0.32 kWh/m
2
, photovoltaic power generation
also increased by 0.25 kWh/m
2
. Therefore, the best UDI scenario for dynamic PVSDs
in January not only improved indoor daylighting, but also reduced energy consumption.
In July, the optimal dynamic PVSDs for energy consumption also increased UDIsup and
UDIauto by 0.27% and 1.96%, respectively.
Buildings 2024, 14, x FOR PEER REVIEW 20 of 23
Energy consumption (kWh/m2) Daylighting (%)
(a) (b)
Figure 19. Comparison of energy consumption and daylighting between fixed PVSD and dynamic
PVSD: (a) Energy consumption; (b) Daylighting.
Generally speaking, the application of dynamic PVSDs in office buildings in cold ar-
eas meets the needs of office buildings. On the basis of energy saving, it reduces the risk
of glare, improves the uniformity of indoor daylighting, and also meets the needs of visual
comfort. If the PVSD is used in other cities in cold regions of China, the PVDS is suitable
for cities with sufficient radiation and cooling needs.
5. Conclusions
Compared with existing research, the novelty of this paper is the creation of a dy-
namic PVSD performance evaluation method that integrates parametric design and per-
formance evaluation. This parametric performance design method can provide non-pro-
fessionals with a visual toolbox to optimize and evaluate PVSD design solutions. In this
paper, we use this method to analyze the dynamic PVSD potential of Qingdao, providing
a good reference for the evaluation and optimization of PVSDs in other cities in different
climate zones. This research can provide theoretical, methodological, and data support for
the application of the PVSD in cold-climate regions in China. In this paper, we investigate
the daylighting and energy-saving performance of a south-facing dynamic PVSD in an
office building located in Qingdao, a cold region in China. The impact of three strategies
(rotation, sliding, and hybrid) on daylighting and energy consumption was investigated.
The main results of this study are summarized below, as follows:
(1) The fixed PVSD in Qingdao can increase the annual average UDI by 2.2% and reduce
the annual average UDIup by 4.2%. When the fixed PVSD is installed at an angle of
35° and installed under the window eaves, the net EUI is the lowest, at 25.49 kWh/m2.
This is caused by the climate environment (winter dominance).
(2) The simulation results show that the dynamic PVSD is superior to the fixed PVSD
and the photovoltaic panel in terms of energy performance, and it can also effectively
improve the indoor daylighting environment. This is due to the flexibility of the hy-
brid strategy, which can beer adapt to changes in the building environment and
user needs.
(3) Simultaneous changes in height and tilt angle (hybrid strategy) can achieve maxi-
mum energy-saving efficiency and higher daylighting levels.
(4) In terms of daylighting, the greater the tilt angle of the PVSD and the closer it is to
the upper eaves of the window (height is 0 m), the lower the indoor daylighting level
will be. In terms of energy consumption, when the PVSD area is constant, the tilt
angle has the greatest impact on power generation, while the height has a greater
impact on energy consumption.
(5) Compared with having no PVSD, the rotation strategy (installation height is 0 m),
sliding strategy (tilt angle is 20°), and hybrid strategy can save energy by 32.13%,
47.22%, and 50.38%, respectively. The three strategies increase the average UDI by
1.39%, 2.8%, and 3.1%, respectively.
Figure 19. Comparison of energy consumption and daylighting between fixed PVSD and dynamic
PVSD: (a) Energy consumption; (b) Daylighting.
Generally speaking, the application of dynamic PVSDs in office buildings in cold areas
meets the needs of office buildings. On the basis of energy saving, it reduces the risk of
glare, improves the uniformity of indoor daylighting, and also meets the needs of visual
comfort. If the PVSD is used in other cities in cold regions of China, the PVDS is suitable
for cities with sufficient radiation and cooling needs.
5. Conclusions
Compared with existing research, the novelty of this paper is the creation of a dynamic
PVSD performance evaluation method that integrates parametric design and performance
evaluation. This parametric performance design method can provide non-professionals
with a visual toolbox to optimize and evaluate PVSD design solutions. In this paper, we
use this method to analyze the dynamic PVSD potential of Qingdao, providing a good

Buildings 2024,14, 596 20 of 22
reference for the evaluation and optimization of PVSDs in other cities in different climate
zones. This research can provide theoretical, methodological, and data support for the
application of the PVSD in cold-climate regions in China. In this paper, we investigate
the daylighting and energy-saving performance of a south-facing dynamic PVSD in an
office building located in Qingdao, a cold region in China. The impact of three strategies
(rotation, sliding, and hybrid) on daylighting and energy consumption was investigated.
The main results of this study are summarized below, as follows:
(1)
The fixed PVSD in Qingdao can increase the annual average UDI by 2.2% and reduce
the annual average UDIup by 4.2%. When the fixed PVSD is installed at an angle of
35
◦
and installed under the window eaves, the net EUI is the lowest, at 25.49 kWh/m
2
.
This is caused by the climate environment (winter dominance).
(2)
The simulation results show that the dynamic PVSD is superior to the fixed PVSD
and the photovoltaic panel in terms of energy performance, and it can also effectively
improve the indoor daylighting environment. This is due to the flexibility of the
hybrid strategy, which can better adapt to changes in the building environment and
user needs.
(3) Simultaneous changes in height and tilt angle (hybrid strategy) can achieve maximum
energy-saving efficiency and higher daylighting levels.
(4) In terms of daylighting, the greater the tilt angle of the PVSD and the closer it is to the
upper eaves of the window (height is 0 m), the lower the indoor daylighting level will
be. In terms of energy consumption, when the PVSD area is constant, the tilt angle
has the greatest impact on power generation, while the height has a greater impact on
energy consumption.
(5)
Compared with having no PVSD, the rotation strategy (installation height is 0 m),
sliding strategy (tilt angle is 20
◦
), and hybrid strategy can save energy by 32.13%,
47.22%, and 50.38%, respectively. The three strategies increase the average UDI by
1.39%, 2.8%, and 3.1%, respectively.
This study also has some limitations. In this paper, we only studied the application
of dynamic PVSDs in office buildings in cold-climate areas in China, and the paper lacks
research on other climate areas and other types of buildings. We only studied the perfor-
mance of south-facing PVSDs, and the paper lacks research on other orientations. In future
studies, it may be necessary to consider dynamic control strategies for PVSDs in multiple
types of buildings in different climate zones under future climate conditions.
Author Contributions: Conceptualization, Q.M. and Y.J.; methodology, Y.J. and Q.M.; software, S.R.
and Z.Q.; validation, Z.Q. and S.R.; writing—original draft preparation, Z.Q. and S.R.; writing—
review and editing, Y.J. and Q.M.; visualization, Z.Q. and S.R.; funding acquisition, Q.M. All authors
have read and agreed to the published version of the manuscript.
Funding: This research was funded by the National Natural Science Foundation of China, grant
number 52108015.
Data Availability Statement: The data presented in this study are available upon request from the
corresponding author. The data are not publicly available due to privacy.
Acknowledgments: We would like to express our gratitude to the editors and reviewers for their
thoughtful comments and constructive suggestions on improving the quality of this paper.
Conflicts of Interest: The authors declare no conflicts of interest.
References
1.
Building on the Past and Starting a New Journey to Address Climate Change Globally. Available online: http://www.gov.cn/
gongbao/content/2020/content_5570055.htm (accessed on 12 December 2023).
2.
National Development and Reform Commission. Action Plan to Peak Carbon Emissions before 2030. Available online: https:
//en.ndrc.gov.cn/policies/202110/t20211027_1301020.html (accessed on 19 February 2024).

Buildings 2024,14, 596 21 of 22
3.
China Association of Building Energy Efficiency. 2022 Research Report of China Building Energy Consumption and Carbon
Emissions. 2022. Available online: https://www.cabee.org/upload/file/20230104/1672820934145324.pdf (accessed on 19
February 2024).
4.
Hao, D.; Qi, L.; Tairab, A.M.; Ahmed, A.; Azam, A.; Luo, D.; Pan, Y.; Zhang, Z.; Yan, J. Solar energy harvesting technologies for
PV self-powered applications: A comprehensive review. Renew. Energy 2022,188, 678–697. [CrossRef]
5.
Peng, C.; Huang, Y.; Wu, Z. Building-integrated photovoltaics (BIPV) in architectural design in China. Energy Build. 2011,43,
3592–3598. [CrossRef]
6.
Ekoe A Akata, A.M.; Njomo, D.; Agrawal, B. Assessment of Building Integrated Photovoltaic (BIPV) for sustainable energy
performance in tropical regions of Cameroon. Renew. Sustain. Energy Rev. 2017,80, 1138–1152. [CrossRef]
7.
Jakica, N. State-of-the-art review of solar design tools and methods for assessing daylighting and solar potential for building-
integrated photovoltaics. Renew. Sustain. Energy Rev. 2018,81, 1296–1328. [CrossRef]
8.
Taveres-Cachat, E.; Lobaccaro, G.; Goia, F.; Chaudhary, G. A methodology to improve the performance of PV integrated shading
devices using multi-objective optimization. Appl. Energy 2019,247, 731–744. [CrossRef]
9.
Tzempelikos, A.; Athienitis, A.K. The impact of shading design and control on building cooling and lighting demand. Sol. Energy
2007,81, 369–382. [CrossRef]
10.
Yu, G.; Yang, H.; Luo, D.; Cheng, X.; Ansah, M.K. A review on developments and researches of building integrated photovoltaic
(BIPV) windows and shading blinds. Renew. Sustain. Energy Rev. 2021,149, 111355. [CrossRef]
11.
Skandalos, N.; Karamanis, D. An optimization approach to photovoltaic building integration towards low energy buildings in
different climate zones. Appl. Energy 2021,295, 117017. [CrossRef]
12.
Noorzai, E.; Bakmohammadi, P.; Garmaroudi, M.A. Optimizing daylight, energy and occupant comfort performance of classrooms
with photovoltaic integrated vertical shading devices. Archit. Eng. Des. Manag. 2023,19, 394–418. [CrossRef]
13.
Liu, J.; Bi, G.; Gao, G.; Zhao, L. Optimal design method for photovoltaic shading devices (PVSDs) by combining geometric
optimization and adaptive control model. J. Build. Eng. 2023,69, 106101. [CrossRef]
14.
Long, W.; Chen, X.; Ma, Q.; Wei, X.; Xi, Q. An Evaluation of the PV Integrated Dynamic Overhangs Based on Parametric
Performance Design Method: A Case Study of a Student Apartment in China. Sustainability 2022,14, 7808. [CrossRef]
15.
Ma, Q.; Ran, S.; Chen, X.; Li, L.; Gao, W.; Wei, X. Study on the effect of photovoltaic louver shading and lighting control system on
building energy consumption and daylighting. Energy Sources Part A Recovery Util. Environ. Eff. 2023,45, 10873–10889. [CrossRef]
16.
Mandalaki, M.; Zervas, K.; Tsoutsos, T.; Vazakas, A. Assessment of fixed shading devices with integrated PV for efficient energy
use. Sol. Energy 2012,86, 2561–2575. [CrossRef]
17. Marzouk, M.A.; Atwa, M. Daylighting and Energy Performance of PVSDs. Sci. Res. J. 2020,8, 69–75. [CrossRef]
18.
Sadatifar, S.; Johlin, E. Multi-objective optimization of building integrated photovoltaic solar shades. Sol. Energy 2022,242,
191–200. [CrossRef]
19.
Chen, H.; Cai, B.; Yang, H.; Wang, Y.; Yang, J. Study on natural lighting and electrical performance of louvered photovoltaic
windows in hot summer and cold winter areas. Energy Build. 2022,271, 112313. [CrossRef]
20.
Kirimtat, A.; Tasgetiren, M.F.; Brida, P.; Krejcar, O. Control of PV integrated shading devices in buildings: A review. Build. Environ.
2022,214, 108961. [CrossRef]
21.
Svetozarevic, B.; Begle, M.; Jayathissa, P.; Caranovic, S.; Shepherd, R.F.; Nagy, Z.; Hischier, I.; Hofer, J.; Schlueter, A. Dynamic
photovoltaic building envelopes for adaptive energy and comfort management. Nat. Energy 2019,4, 671–682. [CrossRef]
22.
Akbari Paydar, M. Optimum design of building integrated PV module as a movable shading device. Sustain. Cities Soc. 2020,
62, 102368. [CrossRef]
23.
Krarti, M. Evaluation of PV integrated sliding-rotating overhangs for US apartment buildings. Appl. Energy 2021,293, 116942.
[CrossRef]
24.
Kim, M.; Konstantzos, I.; Tzempelikos, A. Real-time daylight glare control using a low-cost, window-mounted HDRI sensor.
Build. Environ. 2020,177, 106912. [CrossRef]
25.
Huo, H.; Xu, W.; Li, A.; Lv, Y.; Liu, C. Analysis and optimization of external venetian blind shading for nearly zero-energy
buildings in different climate regions of China. Sol. Energy 2021,223, 54–71. [CrossRef]
26.
Grynning, S.; Lolli, N.; Wågø, S.; Risholt, B. Solar Shading in Low Energy Office Buildings—Design Strategy and User Perception.
J. Daylighting 2017,4, 1–14. [CrossRef]
27.
Barzegar Ganji, H.; Utzinger, D.M.; Bradley, D.E. Create and Validate Hybrid Ventilation Components in Simulation Using
Grasshopper and Python in Rhinoceros. In Proceedings of the Building Simulation 2019: 16th Conference of IBPSA, Roma, Italy,
2–4 September 2019; pp. 4345–4352.
28.
Feng, K.; Lu, W.; Wang, Y. Assessing environmental performance in early building design stage: An integrated parametric design
and machine learning method. Sustain. Cities Soc. 2019,50, 101596. [CrossRef]
29.
Ministry of Housing and Urban-Rural Development of the People’s Republic of China. General Code for Energy Efficiency and
Renewable Energy Application in Buildings. 2021. Available online: http://www.jianbiaoku.com/webarbs/book/160785/47397
56.shtml (accessed on 19 February 2024).
30.
Carlucci, S.; Causone, F.; De Rosa, F.; Pagliano, L. A review of indices for assessing visual comfort with a view to their use in
optimization processes to support building integrated design. Renew. Sustain. Energy Rev. 2015,47, 1016–1033. [CrossRef]
31. Mardaljevic, J.; Heschong, L.; Lee, E. Daylight metrics and energy savings. Light. Res. Technol. 2009,41, 261–283. [CrossRef]

Buildings 2024,14, 596 22 of 22
32.
Reinhart, C.F.; Mardaljevic, J.; Rogers, Z. Dynamic daylight performance metrics for sustainable building design. Leukos 2006,3,
7–31. [CrossRef]
33.
Reinhart, C.F.; Walkenhorst, O. Validation of dynamic RADIANCE-based daylight simulations for a test office with external
blinds. Energy Build. 2001,33, 683–697. [CrossRef]
34.
Rogers, Z.; Goldman, D.; Daylighting Metric Development Using Daylight Autonomy Calculations in the Sensor Placement Opti-
mization Tool. Boulder, Colorado, USA: Architectural Energy Corporation. 2006. Available online: http://dayinnov.demo.pcr-
webdesign.com/system/public_assets/original/SPOT_Daylight%20Autonomy%20Report.pdf (accessed on 19 February 2024).
35.
IES Daylight Metrics Committee. Approved Method: IES Spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure
(ASE). Illum. Eng. Soc. North Am. 2012, 2–3.
36.
Nabil, A.; Mardaljevic, J. Useful daylight illuminances: A replacement for daylight factors. Energy Build. 2006,38, 905–913.
[CrossRef]
37. Mardaljevic, J. Climate-Based Daylight Modelling and Its Discontents; CIBSE Technical Symposium: London, UK, 2015; pp. 1–12.
38.
Mardaljevic, J.; Andersen, M.; Roy, N.; Christoffersen, J. Daylighting metrics: Is there a relation between useful daylight
illuminance and daylight glare probabilty? In Proceedings of the Building Simulation and Optimization Conference BSO12,
Loughborough, UK, 10–11 September 2012.
39.
Energy Star. What Is Energy Use Intensity (EUI). Available online: https://www.energystar.gov/buildings/benchmark/
understand_metrics/what_eui (accessed on 19 February 2024).
40.
Guideline 14-2014; Measurement of Energy, Demand, and Water Savings. ASHRAE Guideline: Peachtree Corners, GA, USA, 2014;
Volume 4, pp. 1–150.
41.
Merghani, A.H.; Bahloul, S.A. Comparison between Radiance Daylight Simulation Software Results andMeasured on-Site Data. J.
Build. Road Res. 2017,20, 48–69. [CrossRef]
42.
Lakhdari, K.; Sriti, L.; Painter, B. Parametric optimization of daylight, thermal and energy performance of middle school
classrooms, case of hot and dry regions. Build. Environ. 2021,204, 108173. [CrossRef]
43.
Yoon, Y.; Moon, J.W.; Kim, S. Development of annual daylight simulation algorithms for prediction of indoor daylight illuminance.
Energy Build. 2016,118, 1–17. [CrossRef]
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