Evaluation of an Existing Validated Emirati House versus a New Parametric Design Based on the Local Social Environment through the Application of Advanced Tools

Abstract

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.