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• W4221083921 abstract "Understanding the associations of indoor air temperature with building physical characteristics is essential for human comfort and energy saving. While traditional studies depended heavily on simulation tools, statistical approaches have recently provided higher flexibility and efficiency to explore factors associated with indoor air temperature. Under such background, this study systematically constructed building spatial design and occupancy features for 53 university classrooms in 4 teaching buildings in Beijing, China, and used statistical models to quantify their impacts on indoor air temperature. Both multiple linear regression (MLR) and random forest regression (RFR) were conducted for different time periods in a day, and the MLR coefficients and RFR feature importance were examined. 16 spatial design features and 5 occupancy features were identified as significant. Among spatial design features, floor number of the classroom had the most notable positive impacts throughout a day. Window-to-wall ratios from different orientations had distinctive influences and added up to make a huge difference. Impacts from classroom size and position, parent building's forms and envelopes were certain but less important. Among occupancy features, it was found for teaching buildings that the whole-buildings’ occupancy had stronger impacts over the classrooms' one, and daily occupancy over temporal one, with building daily occupancy rate being the most influential. The results highlight the potential to adjust thermal environment through proper organization of spatial design and occupancy features addressing their rankings. The RFR models obtained adjusted R2 of above 98%, testifying the effectiveness of predicting indoor air temperature with well-selected building features." @default.
• W4221083921 created "2022-04-03" @default.
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• W4221083921 date "2022-05-01" @default.
• W4221083921 modified "2023-10-01" @default.
• W4221083921 title "Associating indoor air temperature with building spatial design and occupancy features: A statistical analysis on university classrooms" @default.
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• W4221083921 doi "https://doi.org/10.1016/j.buildenv.2022.109009" @default.
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