Efficient Shading Device as an Important Part of Daylightophil Architecture; a Designerly Framework of High-Performance Architecture for an Office Building in Tehran

energies Article Efficient Shading Device as an Important Part of Daylightophil Architecture; a Designerly Framework of High-Performance Architecture for an Office Building in Tehran Hassan Bazazzadeh 1, * , Barbara Świt-Jankowska 1 , Nasim Fazeli 2 , Adam Nadolny 1 , Behnaz Safar ali najar 3 , Seyedeh sara Hashemi safaei 3 and Mohammadjavad Mahdavinejad 2 1 2 3 *



 Citation: Bazazzadeh, H.; Świt-Jankowska, B.; Fazeli, N.; Nadolny, A.; Safar ali najar, B.; Hashemi safaei, S.s.; Mahdavinejad, M. Efficient Shading Device as an Important Part of Daylightophil Architecture; a Designerly Framework of High-Performance Architecture for an Office Building in Tehran. Energies 2021, 14, 8272. https://doi.org/10.3390/en14248272 Academic Editor: Tullio De Rubeis Received: 11 October 2021 Accepted: 2 December 2021 Published: 8 December 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Faculty of Architecture, Poznan University of Technology, 61-131 Poznan, Poland; barbara.swit-jankowska@put.poznan.pl (B.Ś.-J.); adam.nadolny@put.poznan.pl (A.N.) Department of Architecture, Faculty of Art and Architecture, Tarbiat Modares University, Tehran 14115-111, Iran; nasim.fazeli@modares.ac.ir (N.F.); mahdavinejad@modares.ac.ir (M.M.) Faculty of Architecture, Jundi-Shapur University of Technology, Dezfu l334-64615, Iran; behnaz.najar@jsu.ac.ir (B.S.a.n.); sarahashemii92@gmail.com (S.s.H.s.) Correspondence: Hassan.bazazzadeh@doctorate.put.poznan.pl; Tel.: +48-503945736 Abstract: (1) Background: considering multiple, and somehow conflicting, design objectives can potentially make achieving a high-performance design a complex task to perform. For instance, shading devices can dramatically affect the building performance in various ways, such as energy consumption and daylight. This paper introduces a novel procedure for designing shading devices as an integral part of daylightophil architecture for office buildings by considering daylight and energy performance as objectives to be optimal. (2) Methods: to address the topic, a three-step research method was used. Firstly, three different window shades (fixed and dynamic) were modeled, one of which was inspired by traditional Iranian structures, as the main options for evaluation. Secondly, each option was evaluated for energy performance and daylight-related variables in critical days throughout the year in terms of climatic conditions and daylight situations (equinoxes and solstices including 20 March, 21 June, 22 September, and 21 December). Finally, to achieve a reliable result, apart from the results of the comparison of three options, all possible options for fixed and dynamic shades were analyzed through a multi-objective optimization to compare fixed and dynamic options and to find the optimal condition for dynamic options at different times of the day. (3) Results: through different stages of analysis, the findings suggest that, firstly, dynamic shading devices are more efficient than fixed shading devices in terms of energy efficiency, occupants’ visual comfort, and efficient use of daylight (roughly 10%). Moreover, through analyzing dynamic shading devices in different seasons and different times of the year, the optimal form of this shading device was determined. The results indicate that considering proper shading devices can have a significant improvement on achieving high-performance architecture in office buildings. This implies good potential for daylightophil architecture, but would require further studies to be confirmed as a principle for designing office buildings. Keywords: high-performance architecture; optimization; daylightophil architecture; shading device; daylight; building energy usage 1. Introduction Copyright: © 2021 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/). Daylight, as a renewable and permanent source, has positive effects on building occupants, including psychological, mental, and physiological. The dynamic nature of daylight can cause issues such as heat gain and visual discomfort, which need to be controlled in real-time operation [1]. In 2020, even while economies bent under the weight of COVID-19 lockdowns, renewable sources of energy continued to grow rapidly, and electric vehicles set new sales records. More than 40% of the actions required are costeffective. Space heating in the European Union accounts for 60% of energy demand Energies 2021, 14, 8272. https://doi.org/10.3390/en14248272 https://www.mdpi.com/journal/energies Energies 2021, 14, 8272 2 of 26 and 80% of direct CO2 emissions in the building sector [2]. Global energy consumption growth declined by 4% in 2020, in the context of the global pandemic, contrasting with an average 2%/year over the 2000–2018 period and a 0.8% slowdown in 2019 [3]. In the European Union (EU), they can potentially reduce the EU’s total energy consumption by 5–6% and lower CO2 emissions by about 5% [4]. Daily data collected for more than 30 countries, representing over one-third of global electricity demand, show that the extent of demand declines depends on the duration and stringency of lockdowns. On average we find that every month of full lockdown reduced the demand by 20% on average, or over 1.5% on an annual basis [5]. Therefore, focusing on methods to optimize the energy efficiency and promoting sustainability in the building’s component while considering the quality of indoor spaces has been one of the trend topics among researchers [6–13]. In Iran, artificial lighting is reported to contribute up to 25% of electricity consumption in office buildings [14]. Considering the significance of sustainable and efficient buildings in the rate of energy consumption in the total energy consumption rate [15,16], it is, therefore, important to develop methods to minimize the electricity usage for lighting through best practice design decisions [17]. One efficient method is to utilize daylight in buildings which is a common way of reducing energy consumption since it reduces the need for artificial lightings during the day and, thus, the reduction of electricity usage [18]. In response, smart design approaches on the type, pattern, size of shading device, and the effect of daylight on occupant behavior have been investigated in several studies [19–22]. Nowadays, there are many highly glazed facades, which provide daylight and pleasant external views. At the same time, the risks of having high thermal loads should be considered in the design stage by using shading devices and controlling shading patterns in facades with large windows or transparent elements [23,24]. Several designs for building façades have been developed to provide comfortable conditions for occupants [1]. A comprehensive and multi-objective framework for designing shading devices has been suggested by scholars, in which there are two main steps: (1) the search of non-dominant solutions and (2) multi-criteria decision making (MCDM) [25]. Accordingly, the optimization of shading devices design in buildings normally involves making a balance of the following objectives: maximizing energy efficiency through daylight and improving the occupants’ visual comfort. The literature in modeling and simulation of shading devices deals with models for calculating solar properties of shading devices and approaches for obtaining these results by using building energy simulation tools. Moreover, the influence of the orientation of the shading system was examined. Some of the relevant research is shown in Table 1. Table 1. Selected research and methods. Reference [26] Dutta et al. (2017) [27] Investigated an automatic movable exterior shading system, associated with the sun path. The results showed that the view factor models cannot be efficient because the cooling energy demand and the peak power were underestimated; additionally the authors did not consider internal reflections in the shading system. The authors observed that the use of movable shading devices caused an annual reduction in energy consumption of about 9.8%, varying from 14.9% in summer to 4.5% in winter. Saelens et al. (2014) Used a ray-tracing method to explain the total solar transmission of shading systems. Results Authors (year) Method Energies 2021, 14, 8272 3 of 26 Table 1. Cont. Reference [28] [30] The results revealed that simple metrics, such as Eh-room and Eh-task, outperformed more complex evaluation metrics, such as Daylight Autonomy (DA), when studying visual comfort. A comparison of the most used control functions and their implications on user comfort and energy load in different climatic zones. Results showed that climatic conditions impact the shading control scenario significantly and the optimal scenario was an open-loop algorithm based on direct solar radiation due to the earlier activation of blind closure to block solar radiation while increasing lighting load at the same time. Photovoltaic integrated shading devices and provided a reference for future studies. [32] An adaptive solar skin [31] [29] A comparison of current metrics with human visual perception to analyze the applicability of the developed EBD-SIM framework (evidence-based design-simulation) was performed through computer simulation and questionnaires, both instantaneously and annually. Compared and classified several types of shading devices by using a systematic approach to identify patterns and trends. Tabadkani Zhang et al. (2018) et al. (2019) Results showed that there are three recognized categories of solar shading systems based on the energy contribution: (1) active systems, (2) passive systems, and (3) hybrid systems linked with a biomimetic approach. Results showed that photovoltaic integrated shading devices should adapt under several design conditions, such as position, orientation, and others. A parametric approach was developed for an office block in Tehran to analyze point-in-time illuminance (PIT) and visual discomfort occurrence, using Honeybee plug-in. Tabadkani et al. (2021) The results showed that all the shading systems are not the same efficiency. Davoodi et al. (2020) Investigated the role of in-stalling shading devices to improve thermal comfort and keep adequate illuminance levels in existing buildings characterized by glazed surfaces. Masrani et al. (2018) In this research, to estimate the daylight control system and evaluate the performance of the system, a building energy model, a daylight model, and a Kriging model were combined. Evola et al. (2017) Implemented a method for evaluating a new daylight control system to the analysis of a new dynamically tunable system. Yi et al. (2018) Results Authors (year) Method [33] [34] On the other hand, the source of inspiration for designing a shading device in this project was one of the ornamental elements in the historic architecture of Iran. Muqarnas (used ornaments), indeed, is one of the most sophisticated ornaments, which has been considered a symbol of Islamic architecture [35]. Muqarnas originally belongs to the early 10th century, and they have changed to a great extent over time in terms of their design and construction methods in different geographical areas, from East Asia to Spain and West Africa [36]. It is considered a complex element for decoration that initially aims to create 3D facades involving shadow and light by using unparalleled lines and to develop more surfaces to apply more micro decorations [37]. Although there are four different types of muqarnas based on 3D geometry and 2D pattern plan [38], in their geometric patterns, the Energies 2021, 14, 8272 4 of 26 existence of a five-pointed star‐ and pentagonal shape is common [39]. Used to respond ‐ to the required light in space, the five-pointed star and pentagonal shape have formed dynamic and fixed shading devices, respectively, through an accurate parametric modeling process in grasshopper coupled with Rhinoceros 3D software (Figure 1). Figure 1. The process of forming a fixed and dynamic shading pattern. Thus, this research tries to address the challenge of designing an optimal window for office spaces by which the rate of energy consumption would be low and the rate of‐ occupants’ visual comfort would be high. However, this paper goes further to fulfill this gap by analyzing the results of the multi-objective evaluation and developing a computerized ‐ model of responsive envelope based on visual comforts index assessment: the evaluation of Daylight Autonomy (DA) and Useful Daylight Illuminance (UDI), Daylight Glare Index (DGI), Daylight Glare Probability (DGP), energy consumption, and, more specifically,‐ Energy Use Intensity (EUI) to achieve the features of an efficient envelope. To that purpose, different daylight and shading strategies coupled with energy effi-‐ ciency objectives have been investigated by scholars [40–42]. Similarly, this study aims to develop a protocol for optimization of a sun responsive shading device, by which the‐ energy usage by optimized daylight performance would be achievable. Thus, a comprehen-‐ sive review of the current state of literature of the optimization of dynamic shading devices‐ and design of shading device for energy efficiency and optimized daylight is followed by‐ the proposed methodology and an example of its implementation. This research aims to study the effect of dynamics parametric facades on optimal control of glare and access to maximum optimal illuminance in an office room. 2. Materials and Methods To optimize the shading device, this study proposes a framework for the evaluation of Daylight Autonomy (DA) and Useful Daylight Illuminance (UDI), Daylight Glare Index (DGI), Daylight Glare Probability (DGP), energy consumption, and, more specifically, Energy Use Intensity (EUI) in office buildings and employs an optimization method to minimize the energy consumption and optimize the daylight. To see the detailed research workflow, see Figure 2. ‐ 5 of 26 ‐ Energies 2021, 14, 8272 Figure 2. Research workflow. The basic measurements in the process of optimization are performed by performance ‐ simulation software. Then, this method will be checked in a case study of a typical office building located in Tehran, Iran, to determine the most appropriate shading device dimensions. The results from the application of the suggested optimization method in the studied case will be followed by a thorough discussion of the selection criteria (Figure 3). Figure 3. Simulation workflow. Energies 2021, 14, 8272 6 of 26 In this respect, the proposed models of shading devices for the studied office building were parametrically modeled in Rhinoceros using the Grasshopper plugin. Generated patterns were then used as the shading device of the window in the studied office space (Figure 4). In the next stage, different analysis, according to the goal of this research, was performed including energy performance analysis (Energyplus and Honeybee), daylightrelated variables, and occupants’ visual comfort (Ladybug and Honeybee). Simultaneously, to have a general comparison between fixed and dynamic options and to find the optimal ‐ opening ratio of dynamic options, a multi-objective optimization by using the Colibri component in Grasshopper was performed. Figure 4. Modelling shading patterns and using them as shading device. To begin with, the simulation process of this paper comprises of two parts. Before modeling shading devices, to validate the model, MIT Reference Office was used as the base model, and then required modifications were added to turn it into our case study [43]. Firstly, the initial idea of shading design in this research is derived from Chinese knot patterns and “Muqarnas” in Iranian architecture Secondly, light and energy consumption of the case study were simulated using Ladybug and Honeybee plugins in Grasshopper. The dates used for simulation were the spring equinox (3/20), the summer solstice (6/21), fall equinox (9/22), and the winter solstice as (12/21). These days are the most critical in terms of daylight, which was why the performance of the case study on these days would check the workability of the proposed shading device throughout the year according to previous studies. In this way, on the selected days, the position of the sun is the highest, lowest, or in the middle, and the lengths of daytime are the longest, shortest, and in the middle range. Thus, patterns of daylight, from only these 4 days, could be fully understood. This method has been used widely in the analysis of daylight and its impact on the performance of the windows and ‐ ‐ ‐ Energies 2021, 14, 8272 7 of 26 the building [44,45]. The case study for analysis was a simple south-facing ‐ office room (like the Reinhart reference room) in an office building in District 6 of Tehran (Figure 5). Figure 5. Simulated office building and office room plan. In this study, to simulate and control selected variables, some assumptions were‐ considered, which are described below (Table 2). Based on Tehran climatic data, dynamic shading and reflection of the sun were selected from the average sky with the sun by CIE standard for instantaneous calculations and annual calculations of the sky based on climatic‐ data. The simulation has been conducted forfor working hours from 7:00 to 18:00. Energy has been conducted simulations are performed annually and daily in four critical days throughout the year and over a one-day ‐and 24-h interval, followed by three 9-h values. In addition, for simulating‐ ‐ ‐ daylight for glare and brightness analysis, three times, 9:00, 12:00, and 15:00, during these four days were considered for energy optimization and to select simultaneous optimization‐ of light and energy. The cooling and heating fuel is electricity. The thermal loads and coefficients included in the models of this research are based on the configurations and application of defaults to Honeybee algorithms for closed and open office space. Table 2. Default settings of air flow coefficients in calculating heat loads [46]. Space Type ASHRAE 2004-1. 62 Guide Office Conference room Office: Office space Office: Conference/Meeting Restaurant: Restaurant and dining room Office: Office space Office: Office space Office: Office space Office: Office space Public space: corridor Office: Office space Rest room Elevator W.C Step Office lobby Hallway and corridor IT Room Air Conditioning Per Person (cfm/Person) Ventilation Per Area (Cfm/ft2 ) 5 5 0.06 0.06 7.5 0.18 0 0 0 5 0 5 0.00 1.04 0.00 0.06 0.06 0.06 ‐ Energies 2021, 14, 8272 8 of 26 In the simulation of energy plus energy output, the wall materials were designed and selected according to the status quo, and in the simulation of daylight and radiance analysis, the reflection of interior walls, floor, and room ceilings were 50, 20, and 70, respectively. The percentages for the outer walls are 50% for the inner envelope section and 35% for the outer envelope [47]. U-values are ‐calculated using Honeybee_EnergyPlus Construction by adding different layers of materials in each envelope according to the construction details of the case study (Figure 6). ‐ Figure 6. U-value calculations. The U-value for the outer wall is assumed to be 0.365 W/m2 K. Double-glazed window glass is transparent and uncoated, with a visible light transmission coefficient of 77%, a U-value of 0.65 W/m2 K, and a reflectance coefficient of 1.52 (Table 3). Table 3. Window features. Type Visible Light Coefficient (%) U-Value (W/m2 K) WWR (%) Double Glazing 77 5 40 Window dimensions: for envelope optimization, the ratio of window to wall area is assumed to be 40%. The floor of the North and South windows is 80 cm from the inside floor and the size of the window above the finished ceiling is 26 cm. Daylight Sensors: the sensors predicted for this room are located 0.5 m away from each other at the height of 80 cm (Figure 7). ‐ ‐ ‐ ‐ ‐ Energies 2021, 14, 8272 9 of 26 Figure 7. Optical sensors considered in sample office room. Semi-transparent dynamic external envelope defaults: the envelope material is semi‐ transparent with a reflectance coefficient of 35%. The semi-transparent materials pass ‐ through part of the light but scatter part of it all around. In this study, to evaluate the envelope performance, shaded window options, fixed shading with 20% pop-up, 70% ‐ pop-up, and finally parametric with minimum and maximum pop-ups between 70–20% ‐ ‐ were considered. Other settings: since the radiance software calculates the interior light with reflection spectra and reflections from the surrounding environment, it was chosen to increase the accuracy of the simulation settings as set out below. These settings were selected assuming the case samples and the radiance defaults were to achieve higher accuracy, less fluctuation, and error (Table 4). Table 4. Simulation variables in radiance software with good precision. ab: Number of Ambient Bounces ad: number of Ambient Divisions as: Ambient Super-samples ar: Ambient Resolution aa: Ambient Accuracy‐ 2 512 256 128–250 0.15 3. Discussion and Results Among the criteria for measuring the dynamic daylight in office space, in this study, two main indicators, Daylight Autonomy (DA) and Useful Daylight Illuminance (UDI) were selected for analysis. DA (Daylight Autonomy) is a widely acceptable indicator for determining the frequency of light for various activities using only daylight [48]. It has been proven that the threshold for illumination in the office is 300 lux [49], and the daylight autonomy is shown as 300 DA, which means the average percentage of time in a room is above 300 lux. The initial stage of study (Figure 8) focused on four different types of windows, namely: base model (without shading device), type 1 (fixed shaded window with maximum porosity area of 70%), type 2 (fixed shaded windows with maximum porosity area of 20%), and type 3 (the dynamic shaded window with minimum and maximum openings of 20% and 70%, respectively). ‐ ‐ ‐ Energies 2021, 14, 8272 Daylight Autonomy on Mar. 20th 10 of 26 100% 90% 80% 70% 96% 71% 60% 50% 40% 30% 20% 36% 10% 18% 9:00 12:00 59% 56% 33% 21% 10% 1% 0% 9:00 Daylight Autonomy on Sep. 22 Daylight Autonomy on Dec. 21st 10% 0% 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 18% 5% 1% 12:00 94% 15:00 Base model (without shading device) Shading device type 1 (Fixed Shading windows with 20% Openning) 73% Shading device type 2 (Fixed Shading windows with 70% Openning) Shading device type 3 (Dynamic Shading windows with 20-70% Openning) 70% 60% 20% 18% 8% 100% 50% 40% 30% 15:00 72% 40% 30% 20% 10% 90% 80% 10% 7% 100% 90% 80% 70% 60% 50% 33% 30% 20% 10% 0% Daylight Autonomy on Jun. 21 67% 55% 64% 55% 42% 32% 22% 12% 32% 17% 15% 6% 9:00 12:00 97% 93% 100% 89% 60% 57% 30% 15:00 37% 44% 32% 30% 18% 9:00 12:00 15:00 Figure 8. Daylight Autonomy analysis on each critical day of the year for each shading device type. The simulation results imply that while DA was by far higher in shading device type 1 in all studied hours, it showed the biggest differences between studied hours in spring and fall and the smallest differences in summer and winter. For type 2, the rate of DA did not show any notable changes of more than 10%, whereas type 3 and type 4 had considerable changes in their patterns at different times of the year. Therefore, among the studied alternatives (types 2,3, and 4), in terms of DA percentage, type 3 had the highest rate and it was followed by type 4, and type 2, respectively (Figure 9). ‐ ‐ ‐ ‐ Energies 2021, 14, 8272 11 of 26 Shading device type 3 (Dynamic Shading windows with 20-70% Openning) Shading device type 2 (Fixed Shading windows with 70% Openning) Shading device type 1 (Fixed Shading windows with 20% Openning) 25% Base model (without shading device) Shading device type 3 (Dynamic Shading windows with 20-70% Openning) Shading device type 2 (Fixed Shading windows with 70% Openning) 43% Shading device type 1 (Fixed Shading windows with 20% Openning) Base model (without shading device) 25% 13% 43% 78% 13% 78% 0% 0% 10% 10% 20% 20% 30% 40% 50% 60% 70% Average Daylight Autonomy in 4 critical days of a typical year 30% 40% 50% 60% 70% Average Daylight Autonomy in 4 critical days of a typical year 80% 80% 90% 90% 100% 100% Figure 9. Average Daylight Autonomy in critical days of a typical year. The next step in the discussion is studying the effect of constant and dynamic external shading on useful daylight. To be more precise, the second set of simulation results, such as daylight autonomy analysis, room illumination in three windowless modes (base model), and fixed shading, as well as dynamic and responsive parametric shading to establish‐ a useful daylighting interval between 1800–300 lux based on fundamental studies, [36] has been analyzed for more space in the room (Figure 10). Figure 10. Useful Daylight Illuminance of critical days in the base model. In this regard, the percentages of the average time that the lattice points on the work surface in the room receive intervals above 1800 lux, below 300 lux, and between 300 and ‐ Energies 2021, 14, 8272 12 of 26 1800 lux, are calculated. The sensors were positioned at a grid surface of 50 cm in width and 80 cm from the floor of the room (desk surface). To compare two fixed shading models with porosity percentages of 20% and 70% and a parametric model with minimum and maximum percentages of cavity area between 20% and 70%, the results were assumed and compared with the baseline model (without shading). The results of the analysis were presented graphically and, finally, the ratio of useful daylight illumination to the covered area sensor points was compared to study the effects of shading (Figures 11–13). The results of the useful lighting modeling were analyzed in intervals above 1800 and between 1800–300. Percentages above 1800 can cause overheating and excessive temperatures of over 300 due to inadequate daylighting, requiring more artificial light. One of the advantages of this criterion for Daylight Autonomy was considering three useful intervals, above the maximum and below the threshold. By comparing graphical analysis (UDI > 1800), it was found that the room without shade received the highest amount of illumination above optimal, with excessive and average heat. On the first day of January, the percentages of optimal illumination percentages in shading device type 2 were 3.12%, 2.08%, and 1.04% at 9 a.m., 12 a.m., and 3 p.m., respectively. The first results in October showed a 1.04 percent increase in optimal brightness at noon. Figure 11. Useful Daylight Illuminance of critical days in the model with shading device type 1. Energies 2021, 14, 8272 13 of 26 Figure 12. Useful Daylight Illuminance of critical days in the model with shading device type 2. Figure 13. Useful Daylight Illuminance of critical days in the model with shading device type 3. Energies 2021, 14, 8272 14 of 26 Daylight Autonomy on Mar. 20th Daylight Autonomy on Jun. 21st Therefore, by comparing the optimal useful brightness and the useful brightness above the optimal constant brightness envelope with a porosity of 70%, 17.27% more than the dynamic parametric envelope received daylight usefulness, and 0.06% less than the dynamic parametric envelope above the maximum brightness (Figure 14). It is desirable and, in comparison, the performance of a constant envelope with a porosity of 70 is more than parametric with a minimum and maximum of 20–70. 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 5% 13% 0% 0% 0% 0% 0% 0% 9:00 5% 0% 0% 0% 12:00 15:00 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 20% 18% 0% 0% 0% 16% 1% 1% 1% 0% 0% 0% 9:00 12:00 15:00 Daylight Autonomy on Sep. 22nd 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 21% 18% 3% 4% 3% 0% 0% 0% 9:00 15% 0% 0% 0% 12:00 15:00 Daylight Autonomy on Dec. 21st 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Base model (without shading device) Shading device type 1 (Fixed Shading windows with 20% Openning) Shading device type 2 (Fixed Shading windows with 70% Openning) Shading device type 3 (Dynamic Shading windows with 20-70% Openning) 38% 31% 8% 15% 9:00 11% 9% 19% 25% 10% 12:00 8% 10% 9% 15:00 Figure 14. Useful Daytime Illumination above 1800 lux. In the next step, different shading devices were analyzed for glare, inside the room. The observer stands in a room 5 m away from the window and in the middle of the room while the height of the observer is assumed to be 2 m. Evaluation is done with two criteria of daylight glare index (DGI) and daylight glare probability (DGP), illustrated in Figures 15 and 16. Energies 2021, 14, 8272 15 of 26 Figure 15. Daylight Glare Index (DGI) analysis. Energies 2021, 14, 8272 16 of 26 0.40 DGP on Jun. 21st 0.35 0.30 0.273 0.264 0.272 0.25 0.284 0.246 0.282 0.272 0.269 0.257 0.267 0.203 0.166 0.20 0.15 0.10 0.05 0.00 9:00 12:00 15:00 0.40 DGP on Mar. 20th 0.35 0.30 0.273 0.267 0.258 0.300 0.252 0.277 0.266 0.262 0.250 0.265 0.216 0.25 0.175 0.20 0.15 0.10 0.05 0.00 9:00 12:00 15:00 0.40 DGP on Sep. 22nd 0.35 0.30 0.275 0.227 0.25 0.269 0.260 0.295 0.251 0.277 0.265 0.260 0.263 0.20 0.246 0.146 0.15 0.10 0.05 0.00 9:00 0.40 DGP on Dec. 21st 0.35 0.30 0.25 12:00 0.346 15:00 Base model (without shading device) Shading device type 1 (Fixed Shading windows with 20% Openning) Shading device type 2 (Fixed Shading windows with 70% Openning) 0.287 Shading device type 3 (Dynamic Shading windows with 20-70% Openning) 0.238 0.257 0.248 0.253 0.293 0.264 0.20 0.259 0.242 0.221 0.146 0.15 0.10 0.05 0.00 9:00 12:00 15:00 Figure 16. Daylight Glare Probability (DGP) analysis. The diagrams show that the lowest glare is related to the fixed shading shell with 20% porosity, and then the dynamic parametric shell. The probability of glare in the dynamic‐ parametric shell compared to the baseline state (without canopy) decreased by 13.8% on 21 December at 9:00 a.m., 23.7% at 12 noon, 14.95% at 3 p.m., and 3.54% on 21 June at 9:00 a.m. Noon shows a decrease of 4.39% and 3 p.m. shows a decrease of 3.59%. The probability of glare in the dynamic parametric shell, compared to the baseline, decreased by 5.35% on 20 March at 9:00 a.m., 11.44% at noon, 5.65% at 3 p.m., 5.28% on 22 September at 9 a.m., and 10.31% at noon. Percentage at 3 p.m. shows a decrease of 6.37%. Then, the total energy consumption including heating, cooling, electric lighting, and a load of consuming equipment for the studied office room was analyzed and the results were calculated with the EnergyPlus output motor and analyzed in Excel software (Figure 17). The results show that the fixed shading device (70%) performed better than the other fixed option and, in general, the dynamic option had a better level of performance than the two other options. Energies 2021, 14, 8272 ENERGY CONSUMPTION IN DEC. 21TH (KW) ENERGY CONSUMPTION IN SEP. 22TH (KW) ENERGY CONSUMPTION IN JUN. 21TH (KW) ENERGY CONSUMPTION IN MAR. 20TH (KW) 17 of 26 Electrical Load (Equipments) Electrical Load (Lighting) Heating Load Cooling load 3 2.5 2 1.5 1 0.5 0 Dynamic Fixed Fixed Based shade- shade shade model9:00 (20%)- (70%)- 9:00 9:00 9:00 Dynamic Fixed Fixed Based shade- shade shade model12:00 (20%)- (70%)- 12:00 12:00 12:00 Dynamic Fixed Fixed Based shade- shade shade model15:00 (20%)- (70%)- 15:00 15:00 15:00 Electrical Load (Equipments) Electrical Load (Lighting) Heating Load Cooling load 3 2.5 2 1.5 1 0.5 0 Dynamic Fixed Fixed Based shade- shade shade model9:00 (20%)- (70%)- 9:00 9:00 9:00 Dynamic Fixed Fixed Based shade- shade shade model12:00 (20%)- (70%)- 12:00 12:00 12:00 Dynamic Fixed Fixed Based shade- shade shade model15:00 (20%)- (70%)- 15:00 15:00 15:00 Electrical Load (Equipments) Electrical Load (Lighting) Heating Load Cooling load 3 2.5 2 1.5 1 0.5 0 Dynamic Fixed Fixed Based shade- shade shade model9:00 (20%)- (70%)- 9:00 9:00 9:00 Dynamic Fixed Fixed Based shade- shade shade model12:00 (20%)- (70%)- 12:00 12:00 12:00 Dynamic Fixed Fixed Based shade- shade shade model15:00 (20%)- (70%)- 15:00 15:00 15:00 Electrical Load (Equipments) Electrical Load (Lighting) Heating Load Cooling load 3 2.5 2 1.5 1 0.5 0 Dynamic Fixed Fixed Based shade- shade shade model9:00 (20%)- (70%)- 9:00 9:00 9:00 Dynamic Fixed Fixed Based shade- shade shade model12:00 (20%)- (70%)- 12:00 12:00 12:00 Dynamic Fixed Fixed Based shade- shade shade model15:00 (20%)- (70%)- 15:00 15:00 15:00 Figure 17. Energy consumption of the building with each shading device in studied dates and hours. Comparative analysis: Energies 2021, 14, 8272 18 of 26 ‐ Comparing the research-dependent variables (related to visual comfort) with each other: while the base model had mostly among the highest DA and UDA, in terms of PIT ‐ option. As far as fixed shading‐ (Point In Time) brightness analysis and GDP, it is a high-risk devices are concerned, the first one (20%) had by far the lowest DA and UDA and in GDP it had no superiority compared to the other options and, in general, the second fixed shade (70%) performed better. Finally, the dynamic option had a lower DA and UDA than the fixed shading device (70%) on most studied days (Figure 18). Comparative Percentage 100% 80% 60% 40% 20% 0% Comparative Percentage Base Model Comparative Percentage UDI PIT Studied Daylight‐related Variables on Jun. 21st on average Fixed Shade (20%) Fixed Shade (70%) GDP Dynamic Shade (20–70%) 100% 80% 60% 40% 20% 0% Base Model DA UDI PIT GDP Studied Daylight‐related Variables on Dec. 21st on average Fixed Shade (20%) Fixed Shade (70%) Dynamic Shade (20–70%) 100% 80% 60% 40% 20% 0% Base Model Comparative Percentage DA 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Base Model DA UDI PIT Studied Daylight‐related Variables on Mar. 20th on average Fixed Shade (20%) DA Fixed Shade (70%) GDP Dynamic Shade (20–70%) UDI PIT GDP Studied Daylight‐related Variables on Sep. 22nd on average Fixed Shade (20%) Fixed Shade (70%) Dynamic Shade (20–70%) Figure 18. Comparative analysis of daylight‐related variables. Figure 18. Comparative analysis of daylight-related variables. Energies 2021, 14, 8272 19 of 26 ‐ Summarizing the results and comparing the energy efficiency, the total energy con- ‐ sumption for heating, cooling, and lighting is presented in the following diagram (Figure 19). This graph shows that while using shading devices the rate of energy consumption has‐increased compared to the base model, the dynamic shading device shows better performance ‐ on most occasions than other options. Base model (without any shading device) 6 5.452 5 Energy consumption on average (kW) Energy consumption on average (kW) 6 4.404 5 4.404 4 Shading device type 1 (Fixed shading windows Base model (without any shading device)with 20% opening) 5.452 Shading devicedevice type 2 type (Fixed shading windows with 70% Shading 1 (Fixed shading windows withopening) 20% opening) 4.680 Shading devicedevice type 3 type (Dynamic shading windows with 20-70% Shading 2 (Fixed shading windows with 70% opening) opening) Shading device type 3 (Dynamic shading windows with 20-70% 4.680 opening) 3.588 4 3.588 3 3 2 2 1 1 0 0 0.447 0.045 0.724 0.306 0.447 0.045 0.163 0.306 March, 20th 0.795 0.724 0.795 0.724 0.163 March, 20th June, 21th June, 21th 0.795 0.724 0.795 September, 22th September, 22th 0.790 0.790 0.790 0.580 0.790 0.790 0.790 0.580 December, 21th December, 21th Figure 19. Comparative analysis of energy consumption in proposed options on average. Optimization: ‐ Finally, to check all possible options in increasing the reliability of the results and ‐ to find the optimal condition of the dynamic shading device at each time and each hour ‐ according to the visual comfort and energy consumption variables, a multi objectives ‐ optimization process was launched. According to the results of the study of visual comfort variables by simulation in Rhino environment, HoneyBee Plus, and Ladybug Plus, and due to the multivariate nature of this research, to select the best options, it is advised to use the Calibri plugin in Grasshopper and transfer the results to Design Explorer. The appropriate option was analyzed and the results showed the superiority and efficiency of fixed canopy with a porosity of 70% of the cavity area compared to the other cases (Figure 20). Figure 20. Cont. Energies 2021, 14, 8272 20 of 26 Figure 20. Comparative analysis of the performance of 33 options for fixed shading devices on June 21st (top) and December 21st (down). The next step is selecting the optimal condition for studied dates and hours. Due to the multivariate nature of this research, to select the best options, the Calibri plugin ‐ was used in the Grasshopper and the results were transferred to Design Explorer. The ‐ minimum area of openings was within the range of 50–20% and at intervals of 5%, and the maximum area of openings was in the range of 50–70% at intervals of 5%. A total of ‐ 105 modes were created by considering the minimum and maximum between the above ‐ ‐ values in the dynamic outer shell, which was performed for all 105 models of light and ‐ ‐ energy simulation and scoring indicators of the lead system, and the best options in the ‐ studied dates (Figures 21–24 and Tables 5–7). Figure 21. Cont. Energies 2021, 14, 8272 21 of 26 Figure 21. Choosing the optimal conditions for different hours on 21st June. Table 5. Best values for the opening ratio of shading device on 21st June. The Maximum Ratio of Opening Minimum Ratio of Opening Hour 65% 70% 70% 50% 50% 50% 9:00 12:00 15:00 ‐‐ From the best options extracted from the optimization process, the following conditions for each hour of the selected date were achieved. Indeed, the results show how ‐‐ we can achieve the optimal daylight-related variables and consume less energy. Similarly, performing this analysis for the rest of the studied dates could guide us in setting opening rates for dynamic shading at each time of year. Figure 22. Cont. Energies 2021, 14, 8272 22 of 26 Figure 22. Choosing the optimal condition for different hours on 22nd September. Table 6. Best values for the opening ratio of shading device on 22nd September. Hour Minimum Ratio of Opening The Maximum Ratio of Opening 9:00 12:00 15:00 50% 50% 50% 65% 70% 70% ‐ While the results of the simulation differ for this date and the previous one, inter‐ estingly the optimization process reaches the same answer. This helps the researcher to achieve the optimal option without ignoring one specific date. In the following section the results of optimization for the other two dates are presented. Figure 23. Cont. Energies 2021, 14, 8272 23 of 26 Figure 23. Choosing the optimal conditions for different hours on 20th March. Figure 23. Choosing the optimal conditions for different hours on 20th March. Table 7. Best values for the opening ratio of shading device on 20th March. The Maximum Ratio of Opening Minimum Ratio of Opening Hour 70% 70% 70% 50% 50% 50% 9:00 12:00 15:00 Figure 24. Choosing the optimal conditions for different hours on December 21st. Energies 2021, 14, 8272 24 of 26 Finally, taking all different dates and hours into account, the optimal range for opening the dynamic shading is determined (Table 8). This means that it is now possible to set a schedule for the dynamic shading that shows how it should act at different times of the year. Table 8. Best values for the opening ratio of shading device on December 21st. The Maximum Ratio of Opening Minimum Ratio of Opening Hour 60% 55% 65% 50% 30% 45% 9:00 12:00 15:00 4. Conclusions While designing building elements seems to be a straightforward task to do within the design stage, due to different and sometimes conflicting design objectives, it can be very challenging. As glazing areas are among the most critical elements of each space and by using shading devices, architects have been trying to control daylight and improve the performance of the building in general. This research aims to answer the question of how we could decide about the optimal shading device in office spaces. A novel procedure for designing shading devices for an office building in Tehran by considering daylight and energy performance as objectives was presented to select the optimal option. Therefore, through a sophisticated state of the art method, adaptation of building to the environment to provide occupants with comfort and energy-saving was conducted. There are two main categories of option; a fixed shading device with a different opening rate, and a dynamic one that can change the opening rate, inspired by one of the elements in traditional Islamic architecture (Muqarnas). The result implies in almost all critical days of the year (the spring equinox as 20th March, the summer solstice as 21st June, the fall equinox as 22nd September, and the winter solstice as 21st December) fixed shading with 70% opening in most conditions works better than the other fixed option with 20% opening in terms of energy consumption and daylight-related variables. Moreover, the performance of the dynamic shading device is by far better than all other options in all senses of the word. Finally, to determine the ratio of the opening part in a dynamic shading device, all possible options were analyzed and the result for three different hours of studied dates were listed. As these dates are the most critical days in terms of the position of the sun and the length of the day, we could make decisions about the whole year based on these dates, which have been among acceptable methods in daylight analysis. While this research demonstrated the better performance of the dynamic shading device as an example of responsive facades and found the opening range for this device for different times of the year, it also showed that the proposed method is workable, and it could be used for a similar challenge in different functions and climate conditions to optimize occupant’s visual and thermal comfort. The authors also must acknowledge the limitations of the work. Although analyses using equinox solstice could reflect the behavior of the studied object throughout the year, it has its limitation as it is based on 4 days of the year. Moreover, the view provided by the window is an important criterion that was not considered in this research. For future studies, considering a wider range of variables, including view, and using a machine learning algorithm that can facilitate this process is highly recommended. Author Contributions: Conceptualization, H.B., A.N., N.F. and M.M.; methodology, H.B., B.Ś.-J., A.N., B.S.a.n., N.F. and S.s.H.s.; software, H.B., N.F. and B.S.a.n.; validation, H.B.; formal analysis, H.B. and N.F.; investigation, B.S.a.n. and S.s.H.s.; resources, S.s.H.s.; data curation, S.s.H.s. and N.F.; writing—original draft preparation, H.B., B.S.a.n. and S.s.H.s.; writing—review and editing, H.B., B.Ś.-J. and B.S.a.n.; visualization, H.B.; supervision, B.Ś.-J., A.N., M.M. and H.B.; project administration, B.Ś.-J., M.M. and A.N.; funding acquisition, H.B., B.Ś.-J. and A.N. All authors have read and agreed to the published version of the manuscript. Energies 2021, 14, 8272 25 of 26 Funding: This research was funded by Poznan University of Technology, within the framework of the research project entitled “Mapping of architectural space, the history, theory, practice, contemporaneity II”, grant number 0112/SBAD/0185. Institutional Review Board Statement: Not applicable. 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