Effectiveness of one-click feedback of building energy efficiency in supporting early-stage architecture design: An experimental study

As early-stage design decisions are critical to building energy efficiency, performance-oriented design supporting methods have been intensively investigated using various cutting-edge technologies. However, applying such methods in real design scenarios is rather limited, causing their actual effectiveness remains questionable. To address this problem, a design experiment is reported in this paper, where 41 designers conducted the early-stage design of an office building, first without, and then with the help of MOOSAS, a user-friendly tool providing rapid predictions of building energy efficiency. The results showed that after the use of MOOSAS, the energy efficiency of the design outcomes improved significantly, with the mean energy use intensity decreasing by 10%. Moreover, participants reached new design possibilities with higher energy efficiency during their design explorations, and had a better chance to choose the "optimal" or "near-optimal" design from their intermediate options. Other positive changes in the design process included a greater variety of design explorations, the achievement of an upward trend in energy efficiency, and more comprehensive coverage of the early-stage design subthemes. The questionnaire following the experiment showed that 76% of the participants regarded MOOSAS as effective or even very effective, whereas 56% claimed to use MOOSAS voluntarily in the future. It is further implied that a more hands-on educational approach in building science courses could be advantageous, while forming a culture of regarding building performance as architects' responsibility and using the supporting tools in the pre-design stage may benefit the design practice. •Effectiveness of rapid performance predictions is studied under real design scenarios.•The mean energy use intensity of the final design outcomes drops by 10.2%.•The mean energy use intensity of the optimal intermedia designs drops by 6.9%.•Gap between the final and the optimal intermediate designs shrinks by 72% averagely.•76% designers regard the method as effective in supporting early-stage design.