Multi-objective optimization of window configuration and furniture arrangement for the natural ventilation of office buildings using Taguchi-based grey relational analysis

• Published in: Energy and buildings 2023-10, Vol.296, p.113385, Article 113385
• Main Authors: Yin, Xin, Muhieldeen, Mohammed W., Razman, Ruzaimah, Yong Chung Ee, Jonathan
• Format: Article
• Language: English
• Subjects: Computational fluid dynamics; Furniture layout; Grey relational analysis; Natural ventilation; Taguchi method; Window
• ISSN: 0378-7788
• DOI: 10.1016/j.enbuild.2023.113385

•The joint effect of the window configuration and furniture arrangement on the natural ventilation performance is explored.•The window type and window opening degree significantly influence the indoor air change rate.•The furniture position is recognised as an important factor to enhance the indoor air exchange efficiency.•The low-cost strategy has been identified to provide resilient ventilation in the post-occupancy period.•The benefits of the Taguchi-based grey relational analysis approach with the computational fluid dynamics technique of the complexed multi-objective optimization problem are examined. Natural window ventilation plays a key role in energy-efficient building design to achieve the Sustainable Development Goal 11. However, window ventilation effectiveness has often been overestimated in past studies because indoor obstacles were not considered. This study investigated the improvement of natural window ventilation efficiency for an office room in a humid subtropical climate area. Various design parameters were simultaneously investigated, including the window type, opening percentage, position, and furniture arrangement, with several factor levels. The Taguchi method provided quantitative data from an L27 orthogonal array, so the effects of these variables on multiple objectives such as the air change rate and ventilation efficiency were determined and ranked. The signal-to-noise ratio calculations further confirmed the relevant importance of examined parameters, and the percentage contribution of each parameter was defined by analysis of variance (ANOVA). Additionally, grey relational analysis (GRA) based on grey system theory was used to determine the multiple-optimization design. A confirmation test showed that the optimal combination case enhanced the room air change rate (ACR) and air exchange efficiency (AEE) by up to 0.00293 s-1 and 1.09%, respectively.