Reframing the early-stage design process of residential buildings based on an energy-efficient, designerly decision support system (DDSS)

The literature emphasizes the role of the early-stage design process, particularly early design decisions related to mid-rise residential buildings. On the other hand, the futuristic concepts of high-performance architecture represent a paradigm shift that requires a data-conscious approach to climate change mitigation. This research adopts a designer approach to address the complex and ill-defined sci-tech problems within the architectural field. The study aims to develop a framework for a user-friendly, data-driven Designerly Decision Support System (DDSS) to categorize and automate the architectural design process, with a particular focus on the early design stage. The methodology is based on in-depth structured interviews with architects to identify and classify influential parameters in the early design stages. These parameters were extracted to construct a metamodel. Subsequently, sensitivity analysis was employed to investigate the background of key performance metrics and the relationships among them. The research calculates the energy loads of nine mid-rise residential building patterns in Tehran using Energy Plus software. Based on the quantitative results, three representative patterns—1) high-consumption, 2) low-consumption, and 3) mid-rise—were selected for further sensitivity analysis. The findings indicate that a reference database can be created to comprehensively guide designers working on mid-rise residential patterns. This database can also serve as a resource for revising urban planning guidelines with energy metrics in mind. Additionally, the north and south Window-to-Wall Ratios (WWRs) are identified as the most significant design parameters, directly and interactively influencing heating, cooling, and lighting functions.