A Case Study: Intelligent Shading Retrofit To Existing Home-office Using Multi-objective Optimization

Improved energy performance and occupant comfort are driving building design decisions due to the increasing demand for sustainable and green buildings. However, despite the variety of technological developments in other fields, the range of solutions to improve building performance is limited. One of the main limitations for an early designer is a performance evaluation method to facilitate the design process. This paper offers a new shading performance optimization process that can help designers evaluate both daylighting and energy performance and generate optimized and flexible designs that can be further improved by implementing user-specific automation.

The proposed performance optimization method utilizes parametric design, building simulation models, and Genetic Algorithms. Common shading design systems are explored through parametric design, and daylighting and energy modeling simulations are performed to evaluate shading device performance. Genetic Algorithms are used to identify design options with optimal energy and daylighting performance. A case study is conducted to verify the effectiveness of the overall process. Results are used to analyze the influence of design decisions among different shading designs. Finally, future directions in both shading design and energy optimization are presented.