Al Ain is the second-largest city in the Abu Dhabi Emirate, and the population of Al Ain has been growing rapidly for the last 50 years. The residential units in Al Ain are arranged using different concepts in relation to household social and economic behaviors. While Al Ain city has mostly low-rise and mid-rise residential buildings, the local population tends to live in traditional low-rise villas. The governmental statistics show a high ratio of energy consumption in the form of electricity for cooling loads, and it is estimated to increase with the rapid growth of the population. In this context, it is important to investigate different strategies to control the energy consumption of residential buildings. The purpose of this study was to assess the energy usage and demand of an existing villa in Al Ain and see how a newer design approach can help to reduce the annual energy consumption of households. The newer design option is based on a parametric (application of a parametric façade) approach whilst taking sustainable design approaches. The newer design options are compared to the existing villa and a traditional extension villa attached to the existing villa in terms of annual electricity consumption. The process of design and energy modeling of all cases used the Estidama baseline standards for technical and construction specifications. The process started with selecting an existing six-bedroom villa in Al Ain. Moreover, the selected villa had a planned extension to be constructed in the future. Then, an annual energy model of the existing villa was created in Rhinoceros 7.0 with the Grasshopper 3D plug-in. The energy results were validated against the real energy bills of the villa. Once the energy model was validated, the newer options of the design were modeled, and the projected energy consumption was compared with the base case results to see how energy-efficient the newer model would be. The research shows that it is possible to save up to 60% of electricity annually by carefully selecting a sustainable design in the early stages.

The construction domain in the Middle East region has experienced significant growth in recent years. In the United Arab Emirates (UAE), for instance, the number of newly constructed areas with mixed-use development buildings is increasing yearly. Given the region’s harsh climatic conditions, characterized by an extremely hot climate, designing sustainable and energy-efficient buildings is crucial. Under these conditions, shading is a primary strategy. This research explores how parametric design and optimization based on genetic algorithms (GAs) can improve shading structures to reduce solar radiation and lower cooling energy consumption. It is focused on parametric design and optimization of window shading structures, either for retrofitting existing buildings or for new construction. Advanced tools are essential to achieve this goal, as they enable designers to incorporate various architectural features that enhance energy efficiency. The parametric structures are meant to increase the shaded parts by reducing the amount of solar radiation on building facades, reducing the amount of energy consumption for air conditioning and improving overall energy efficiency. The methodology follows the following steps: selection of the case study, weather analysis, modeling and simulation, parametrization process (maximize shaded area and reduce total solar radiation), results, and analysis. The modeling, simulation, and parametrization are completed using Rhino/GH. While the research focuses on a mid-rise building in Abu Dhabi as a case study, the approach can be applied more broadly to buildings in hot climates. Due to excessive solar radiation in arid climate regions, shading of fenestration is a primary focus. The findings show that the GA-based optimized shading system, based on the cumulative radiation, decreased the total radiation amount by 19% and reduced cooling energy use by 26.2% for the case study. This demonstrates that parametric design can contribute to more sustainable and energy-efficient architecture.

The city of Al Ain is a fast-developing area. With building typology varying from low-rise to mid-rise, sustainable design in buildings is needed. As the majority of the city’s population is Emirati Citizens, the percentage of expats is increasing. The expats tend to live in mid-rise buildings. One of the central midrise areas is AL Ain Square. This study aims to investigate how an optimized mashrabiya pattern can impact the energy and the Predicted Mean Vote (PMV) in a 3-bedroom apartment, fully oriented to the south, of an expat family. The methodology is as follows: case study selection, Weather analysis, Modeling/Validation of the base case scenario, Optimization of the mashrabiya pattern, Simulation of various scenarios, and Results. Analyzing the selected case study is the initial step of the methodology. This analysis begins with the district, building typology, and the chosen apartment. The weather analysis is relevant for using the mashrabiya (screen device) and the need to improve energy consumption and thermal comfort. The modeling of the base case shall be performed in Rhino Grasshopper. The validation is based on a one-year electricity bill provided by the owner. The optimization of mashrabiya patterns is an innovative process, where various designs are compared and then optimized to select the most efficient pattern. The solutions to the selected scenarios will then yield the results of the optimal scenario. This study is relevant to industry, academia, and local authorities as an innovative approach to retrofitting buildings. Additionally, the research presents a creative vision that suggests optimized mashrabiya patterns can significantly enhance energy savings, with the hexagonal grid configuration demonstrating the highest efficiency. This finding highlights the potential for geometry-driven shading optimization tailored to specific climatic and building conditions. Contrasting earlier mashrabiya studies that assess one static pattern, we couple a geometry-agnostic evolutionary solver with a utility-calibrated EnergyPlus model to test thousands of square, hexagonal, and triangular permutations. This workflow uncovers a previously undocumented non-linear depth perforation interaction. It validates a hexagonal screen that reduces annual cooling energy by 12.3%, establishing a replicable, grid-specific retrofit method for hot-arid apartments.

Structural systems in housing projects contribute to the energy consumption of buildings during and after construction, and the climate plays a significant role in this energy consumption. Housing projects in harsh environments such in the United Arab Emirates (UAE) directly contribute to energy performance, specifically in low-density residential areas. By selecting the most efficient structural system, prior construction can enhance energy performance in such housing projects. The present study investigates the energy performance of three different structural systems used in UAE national housing: precast concrete, reinforced concrete, and light gauge steel (LGS). The case studies used for this study were constructed in Abu Dhabi, the capital of the UAE, specifically in Alain city. Second, through qualitative analysis and average annual billing of each housing project, the study identifies the thermal defects and determines the electricity consumptions of each system. The results indicate that the LGS system provides high thermal conductivity through its structural elements whereas reinforced concrete leads to the highest annual energy consumption in kW h/m². Finally, precast concrete has few thermal defects in its junctions and structural elements. The results indicate that LGS and reinforced concrete should be further studied, especially with regard to insulation and improvement, prior to implementation in housing systems.