Scipediacontent: Scipediacontent moved page Review 670620565908 to Zhang et al 2026b

2026-01-26T11:25:27Z

Scipediacontent moved page Review 670620565908 to Zhang et al 2026b

Scipediacontent: Scipediacontent moved page Draft content 433018295 to Review 670620565908

2026-01-26T11:19:42Z

Scipediacontent moved page Draft content 433018295 to Review 670620565908

Scipediacontent: Created page with " == Abstract ==
With increasing awareness of energy conservation and environmental protection, optimizing indoor air quality and energy consumption through rational contro..."

2026-01-26T11:19:38Z

Created page with " == Abstract == <p>With increasing awareness of energy conservation and environmental protection, optimizing indoor air quality and energy consumption through rational contro..."

New page

== Abstract ==

<p>With increasing awareness of energy conservation and environmental protection, optimizing indoor air quality and energy consumption through rational control of window opening behavior (WOB) has become a crucial issue in building design and environmental management. However, existing research primarily focuses on buildings for adults, with relatively few studies on buildings for children, particularly those used by children aged 3–6. Moreover, previous studies often overlook the impact of functional differences between buildings on occupant behavior patterns. This study focuses on a kindergarten and proposes an event-based method for analyzing and modeling WOB. The results show that events such as arrival, class, and departure are associated with higher frequencies of window opening (exceeding 50%), whereas events such as dietary activity, indoor/outdoor activity, sleep, and tidying exhibit lower probabilities. WOB is more sensitive to indoor air quality during events with higher student activity (e.g., class, dietary activity, and indoor activity), resulting in more frequent ventilation. In terms of modeling, the Random Forest (RF) algorithm achieved higher prediction accuracy than Logistic Regression (LR) and Support Vector Machine (SVM). To reduce the complexity associated with multi-model integration, a stacking model was introduced, further enhancing predictive performance. Finally, the generalizability of the proposed method was validated using office building data from the ASHRAE occupant behavior database, achieving a maximum accuracy improvement of 3.87%. This study presents a novel approach for modeling WOB in functional buildings such as kindergartens and provides theoretical support for energy efficiency optimization and indoor air quality management.</p>

== Document ==
<pdf>Media:Draft_content_433018295-1375-document.pdf</pdf>

← Older revision	Revision as of 11:25, 26 January 2026
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← Older revision	Revision as of 11:19, 26 January 2026
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Zhang et al 2026b - Revision history

Scipediacontent: Scipediacontent moved page Review 670620565908 to Zhang et al 2026b

Scipediacontent: Scipediacontent moved page Draft content 433018295 to Review 670620565908

Scipediacontent: Created page with " == Abstract == With increasing awareness of energy conservation and environmental protection, optimizing indoor air quality and energy consumption through rational contro..."

Scipediacontent: Created page with " == Abstract ==
With increasing awareness of energy conservation and environmental protection, optimizing indoor air quality and energy consumption through rational contro..."