Comprehensive assessment of energy, daylight and visual comfort of rooftop and shading building-integrated photovoltaics across desert, steppe, and Mediterranean climates

The combined use of dynamic shading and lighting controls can significantly reduce energy consumption while enhancing occupant’s visual comfort. Previous studies have used parametric and optimization methods to determine the ideal combination of building input variables for these shading and lighting controls. These studies often focus on maximizing energy savings and daylight availability using metrics such as Energy Use Intensity (EUI) and Useful Daylight Illuminance (UDI). However, relying solely on EUI and UDI overlooks factors like excessive glare, outdoor views, and thermal comfort, which may result in suboptimal solutions. This study aims to determine the optimal configurations of input variables, such as Window-to-Wall Ratio (WWR) for all four cardinal directions, shade properties, and window overhang depth for dynamically controlled roller shades for different building form factors with the objective is to optimize EUI, UDI, outdoor view, and thermal comfort. This study uses Honeybee and Ladybug to develop a daylighting model and an energy model using Rhino/Grasshopper as the interface for parametric modeling. The findings indicate that for two-variable combinations involving EUI (e.g. EUI and UDI, EUI and View, EUI and PMV), optimal shading and window-to-wall ratio (WWR) strategies depend on orientation. For South and West-facing façades, two patterns of solutions provide the best results, (i) low shade openness factor (1%) paired with high WWR values (50–75%) and (ii) high shade openness factor (5–10%) combined with low WWR values (25–50%) both perform equally well. No clear pattern for WWR emerges for North-facing façades. Regarding shade overhang depth, square buildings perform slightly better without overhangs, while rectangular buildings benefit more from a 1-meter overhang, though the difference is minimal. Overall, the paper presents a framework for analyzing the output of a multi-input daylight and energy simulation model, where multiple output metrics must be optimized simultaneously. By leveraging the Pareto front, the study identifies the best possible combinations of input variables to achieve optimal performance.

Biomimetic design synthesis and digital optimization of building shading skin: A novel conceptual framework for enhanced energy efficiency

Multi-objective optimization of energy and daylight performance for school envelopes in desert, semi-arid, and mediterranean climates of Iran

Climate classification provides a framework for a better understanding of the dominant weather patterns in different regions of the Earth. This study aims at identifying climate zones in Iran based on the analysis of monthly temperature and precipitation over 139 synoptic stations across Iran during the period 1991–2020. Based on the application of the principal component analysis, we identified six distinct climate zones in Iran: mild and humid, cool and sub‐humid, cold and temperate semi‐arid, warm and semi‐arid, cool and arid, and warm and hyperarid. The highest precipitation occurs in the southern coastal plains of the Caspian Sea, characterized by a mild and humid climate. The climate of western Iran is identified as cool and sub‐humid, while northwestern Iran is characterized by a cold and temperate semi‐arid climate. Southwestern Iran is identified as a region with a warm and semi‐arid climate, while northeastern Iran has a cool and arid climate. Southeastern and central Iran are both characterized by a warm and hyperarid climate. The highest monthly and seasonal precipitation values over Iran occur in March (48.6 mm) and winter (134.2 mm), respectively, while the highest monthly and seasonal mean temperature values occur in July (29.1°C) and summer (28.0°C), respectively. In terms of seasonal variation, the maximum precipitation occurs in the southern coastal plains of the Caspian Sea in autumn, while the minimum occurs in southwestern Iran in summer. Our results have important implications for better understanding and analysing the climatic characteristics across Iran.

Addressing the challenges of global warming and rising energy demands, this study explores fixed shading systems as passive and sustainable solutions to improve energy efficiency, thermal comfort, and daylight performance in office buildings. Conducted in the hot desert climate of Qom, the research employs advanced simulation tools, including Rhino 8 integrated with Grasshopper, Honeybee, and Ladybug, to model and evaluate shading strategies. A multi-objective optimization (MOO) approach was applied to enhance four key metrics: Thermal Comfort Percent (TCP), Energy Use Intensity (EUI), Annual Sunlight Exposure (ASE), and Spatial Daylight Autonomy (sDA). Optimization and visualization were carried out using Colibri and Design Explorer to identify shading configurations that effectively balance energy savings, thermal comfort, and daylighting. The results highlight substantial improvements achieved through optimized shading designs. Fixed exterior shading systems reduced EUI by up to 14.95% for overhangs, with side fins, light shelves, and H-louvers achieving reductions of 7.28%, 13.45%, and 6.04%, respectively. ASE was effectively mitigated, with side fins and H-louvers achieving reductions of 36.25% and 9.38%. Optimal daylighting performance was observed, as sDA reached 100% for H-louvers, side fins, overhangs, and light shelves, and 98.25% for egg-crates and V-louvers. Regarding TCP, egg-crates exhibited the highest performance at 74.18%, followed by H-louvers at 70.21%. These findings demonstrate that integrating tailored shading systems into office buildings not only enhances occupant comfort and reduces energy consumption but also supports sustainable building practices, offering practical solutions for environmentally conscious architectural design.

Applying a parametric design approach for optimizing daylighting and visual comfort in office buildings

Achieving a balance between visual comfort and high-quality daylighting in school buildings located in semi-arid climates requires careful consideration of design elements such as window glazing and shading devices. This study proposes an analytical approach using parametric modeling to efficiently assess daylighting and visual comfort in classrooms. To evaluate these factors, Spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE) metrics are applied. The findings suggest that optimal daylighting performance can be achieved using interior light shelves or exterior louvers on south-facing windows, combined with specific glazing properties. Exterior louvers of varying sizes also prove effective for regulating daylight. The parametric modeling approach enables the exploration of multiple design options, allowing architects to identify efficient strategies for balanced lighting conditions within a short period. This research provides valuable guidance for designing energy-efficient and comfortable learning environments, helping professionals make informed decisions on achieving a balance between visual comfort, daylight availability, and energy efficiency in semi-arid climates.

Objective assessment of visual comfort for stereoscopic video is of great importance for stereoscopic image safety issue. We propose a novel visual comfort assessment metric framework that systematically exploits human visual attention models. In a stereoscopic video shot, perceptually significant regions where human subjects pay more attention are likely to play an essential role in determining the overall level of visual comfort. As a specific example of this concept, we develop a visual comfort metric that quantifies the level of visual discomfort caused by fast salient object motion. The performance of the proposed visual comfort metric has been evaluated using natural stereoscopic videos. The experimental results show that the proposed visual comfort metric significantly improves the correlations with subjective judgment.

Well daylit public space, which ensures visual comfort, is one of the key design goals that architects and lighting designers seek. The public space such as airport’s waiting hall is characterized by large dimensions which can get efficient daylighting levels from a skylight. In Cairo where the sunny and clear sky, improper skylight design can generate extensive heat gain and discomfort glare problems. This paper aims to study the effect of changing the vertical skylight pattern on the uniformity and availability of daylight in the public space of airport holding room. This investigation conducted through changing the shape of north oriented sawtooth opening from one rectangle opening shape to arched opening shape with multi-divisions. Generating the 3d models and analyzing the daylighting performance conducted through a parametric simulation approach. This approach included three software programs which are Grasshopper, Diva for Rhino and Evalglare. Simulations were conducted using the weather data file of Cairo, Egypt. The performance assessment was based on four metrics; IES approved method -Spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE) -, Daylight Availability (DA) and Daylight Glare Probability (DGP). Results show that the different patterns of sawtooth arched opening in Cairo reached the required daylighting performance and achieved the acceptance criteria of the assessment metrics according to the daylighting requirements of LEED V4.

Application of machine learning methods as an alternative for building simulation software has been progressive in recent years. This research is mainly focused on the assessment of machine learning algorithms in prediction of daylight and visual comfort metrics in the early design stages and providing a framework for the required analyses. A dataset was primarily derived from 2880 simulations developed from Honeybee for Grasshopper. The simulations were conducted for a side-lit shoebox model. The alternatives emerged from different physical features, including room dimensions, interior surfaces’ reflectance factor, window dimensions, room orientations, number of windows, and shading states. Five metrics were applied for daylight evaluations, including useful daylight illuminance, spatial daylight autonomy, mean daylight autonomy, annual sunlit exposure, and spatial visual discomfort. Moreover, view quality was analyzed via a grasshopper-based algorithm, developed from the LEED v4 evaluation framework. The dataset was further analyzed with an artificial neural network algorithm. The proposed predictive model had an architecture with a single hidden layer consisting of 40 neurons. The predictive model learns through a trial and error method with the aid of loss functions of mean absolute error and mean square error. The model was further analyzed with a new set of data for the validation process. The accuracy of the predictions was estimated at 97% on average. The View range metric in the quality view assessment, mean daylight autonomy and useful daylight illuminance had the best prediction accuracy among others respectively. The developed model which is presented as a framework could be used in early design stage analyses without the requirement of time-consuming simulations.

As advanced technologies become prevalent, they are being used more widely in numerous fields. The building sector is not an exception. One of these cutting-edge technologies is responsive facades, which are used in buildings and have an undeniable effect on daylighting. However, they have not been adequately evaluated for improving visual comfort in hospitals. This study investigates visual comfort in a standard patient room, based on applying four responsive facades. Simulations were conducted using HoneybeePlus, a plugin in the Grasshopper. Simulation-based results of annual indicators, including Annual Sunlight Exposure (ASE) and spatial Daylight Autonomy (sDA), showed that different facades could result in several optimal modes. Furthermore, a more comprehensive investigation should consider factors such as Daylight Glare Probability (DGP) and Daylight Glare Index (DGI). Glare indicators revealed that facade directly affects patient visual comfort and can even have an adverse effect. When the optimal responsive facade is chosen, it enhances users' visual comfort throughout the year, yet there will be still glare probability in some cases. Based on the results, this probability decreases as patient distance increases, and Window to Wall Ratio (WWR) is not particularly effective in reducing glare. Nevertheless, when it comes to daylight availability, WWR cannot be ignored, and the first façade with WWR 60% showed the best overall performance.

Probability-based visual comfort assessment and optimization in national fitness halls under sports behavior uncertainty

Impact of different control strategies of perforated curved louvers on the visual comfort and energy consumption of office buildings in different climates

Sporters' visual comfort assessment in gymnasium based on subjective evaluation & objective physiological response

Global greenhouse gases increase could be a threat to sustainable agriculture since it might affect both green water and air temperature. Using the outputs of 15 general circulation models (GCMs) under three SRES scenarios of A1B, A2 and B1, the projected annual and seasonal precipitation (P) and cardinal temperatures (T) were analyzed for five climatic zones in Iran. In addition, the probable effects of climate change on cereal production were studied using AquaCrop model. Data obtained from the GCMs were downscaled using LARS-WG for 52 synoptic stations up to 2100. An uncertainty analysis was done for the projected P and T associated to GCMs and SRES scenarios. Based on station observations, LARS-WG was capable enough for simulating both P and T for all the climatic zones. The majority of GCMs as well as the median of the ensemble for each scenario project positive P and T changes. In all the climatic zones, wet seasons have a higher P increase than dry seasons, with the highest increase (27.9–83.3%) corresponding to hyper-arid and arid regions. A few GCMs project a P reduction mainly in Mediterranean and hyper-humid climatic regions. The highest increase (11.2–44.5%) in minimum T occurred in Mediterranean climatic regions followed by semi-arid regions in which a concurrent increase in maximum T (2.9–14.6%) occurred. The largest uncertainty in P and cardinal T projection occurred in rainy seasons as well as in hyper-humid regions. The AquaCrop simulation results revealed that the increased cardinal T under global warming will cause 0–28.5% increase in cereal water requirement as well as 0–15% reduction in crop yield leading to 0–30% reduction in water use efficiency in 95% of the country.