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Xiaoyu Ying

As seen in: Nature, MDPI
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Articles

Cross-scenario evaluation of explainable machine learning for non-invasive summer occupancy detection across five building scenarios

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By Jiantao Weng, Zhitong Ye, Jingqi Zhao, Jindong Wu, Yujie Zhao, Xiaoyu Ying, Xihao Tian
| Nature Verified
Abstract Accurate identification of indoor occupant presence is crucial for intelligent building energy management. Traditional monitoring methods are often invasive and lack standardized quantitative indicators. Therefore, this study proposes a non-invasive occupant presence state prediction method based on machine learning.
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Influencing Factors on Air Conditioning Energy Consumption of Naturally Ventilated Research Buildings Based on Actual HVAC Behaviours

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By Jiajing Wu, ShuQin Chen, Xiaoyu Ying, Jinbiao Shu
| MDPI
3.1. Quantitative Description of Actual HVAC Behavioural Characteristics 3.1.1. Typical HVAC Behavioural Patterns and Their Distribution Characteristics According to the research procedure described in , clustering analysis of the measured data was carried out to classify the HVAC behaviour into six typical modes with representative and mutual differences under heating and cooling conditions.
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IJERPH | Free Full-Text | Field Measurements and Analysis of Indoor Environment, Occupant Satisfaction, and Sick Building Syndrome in University Buildings in Hot Summer and Cold Winter Regions in China

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By Jiantao Weng, Yuhan Zhang, Zefeng Chen, Xiaoyu Ying
| MDPI
Abstract : Teachers and students work and study in classrooms for long durations. The indoor environment directly affects the health and satisfaction of teachers and students.
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