Determining the Optimum Geometrical Design Parameters of Windows in Commercial Buildings: Comparison between Humid Subtropical and Humid Continental Climate Zones in the United States

Abstract

Several studies indicate that the building sector has the highest contribution to world energy consumption. Also, it is predicted that the energy demand in commercial buildings will increase to 1.2% per annum from 26 to 23 due to population and economic growth. This has forced governments to focus on a reduction in the energy costs of commercial buildings by promoting the construction of new buildings and retrofitting existing ones. Many different parameters, such as building orientation, thermal mass, and building-envelope elements, affect building energy performance. The building envelope, as the mediator between buildings’ outside and inside conditions, plays a critical role in reducing energy consumption. Windows, as the eyes of the building, are the most sensitive elements of the building envelope and should be given considerable attention. Due to the number of passive-design variables for windows (e.g., window-to-wall ratio, shading, reveal, and aspect ratio) involved in the design process, selecting suitable design parameters for windows is always a major challenge for designers. This article presents a simulation-based optimization model that is used during the early stages of design to identify the optimum window design parameters to minimize the energy consumption of office buildings. The proposed optimization model employs a harmony search algorithm coupled with EnergyPlus 8.4. software to identify the optimum or near-optimum design parameters. Additionally, a case study of an office building in two different climate regions is presented to illustrate the application of this model. The results show that by identifying the optimum window design parameters, the total energy consumption of the office building model can be reduced by 37%. In addition, the computational tool can be valuable for architects and engineers in determining the optimum design parameters at the early stages of the design.