[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123910-en":3,"doc-seo-123910-105":30,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123910,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Optimization of Indoor Quality and Thermal Comfort for University Classrooms Using Data-Based Machine Learning","Improving indoor environment quality in university classrooms is addressed through seasonal onsite experiments covering temperature, humidity, air pollutants, lighting, and acoustics. Results indicate that under severe outdoor pollution in autumn with natural ventilation, nearly 25% of indoor particulate matter exceeded GB18883, while over 20% of students reported symptoms such as drowsiness, dizziness, chest tightness, poor breathing, and mood changes. Based on occupant demand, the work integrates students’ satisfaction with indoor environmental features to build an optimal IEQ prediction model. Back-propagation neural networks achieve the highest prediction accuracy (~75%) compared with the PMV-PPD thermal sensation model (28%).","Optimization of indoor quality and thermal comfort for university classrooms using data-based machine learning  \nQiwen Jiang1 *, Jialu Liu1, and Xian Yang1  \n1School of Human Settlements and Civil Engineering, Xi’an Jiaotong University, Xi’an, China  \nAbstract. Improving indoor environment quality on university classrooms is a hot research topic. The onsite experiment was conducted on indoor environmental quality regarding temperature, humidity, air pollutants, light and acoustics during different seasonal conditions. The result shows that nearly 25% of indoor particulate matters exceeded the GB18883 standard when the outdoor environmental pollution was severe under natural ventilation conditions in autumn. More than 20% of students experienced symptoms of drowsiness, dizziness, chest tightness, poor breathing, as well as depression and irritability. From the analysis of occupant demand, indoor air pollution and thermal comfort are the most anticipated areas for students to improve their learning environment. This paper proposes an optimal IEQ prediction model integrated with students’ satisfaction and indoor environmental features using machine-learning classification algorithms. The back-propagation neural network shows the high prediction accuracy among different algorithms. The traditional PMV-PPD model shows an accuracy rate of only 28% for thermal sensation prediction, while the highest prediction accuracy obtained through machine learning algorithms isabout 75% . Moreover, the influence of individual's thermal adaptation ability, including gender, long-term  \nthermal experience, and psychological factors, and environmental factors was analyzed in this study.  \n1 Introduction  \nWith the pandemic of corona-virus disease and severe world wildfires, there is an increasing focus on indoor environment quality (IEQ) . Poor IEQ in university classrooms can significantly reduce students’ learning efficiency and affect their health [1-2] . Thermal comfort and perceived air quality (PAQ) are two main subjective effects to the indoor environment. Fanger [3] proposed a PMV and PPD model to evaluate the thermal comfort with air conditioning, which has been adopted in ASHRAE 55 and ISO 7730 standards. PAQ defines asthe perception of indoor air by occupants in CENCR1752 report [4] . Most literatures focus solely on thermal comfort or air quality, while few studies evaluate the university classroom environment from four aspects covering thermal, air quality, light and acoustics [5-7] .  \nIt has been widely believed that improving indoor environmental quality comes at the cost of increasing building energy consumption for a long time [8] . However, there is no trade-off between the comfort of indoor environments and building energy efficiency. The key to solving the contradiction between the two lies in whether people can fully utilize their ability to regulate the environment, accurately judge the environmental needs of building users and match energy consumption with environmental needs. Then it can improve energy utilization efficiency and achieve the dual goals of comfort and energy conservation.  \nAs university classrooms are quite unique semipublic spaces, students are freedom to make their decision on course attendance and to express their opinions on education quality [9] . Recent studies indicate that various perceptions of learning environment were received from students due to individual characteristics, such as thermal adaptation, gender, culture background, and psychological factors [10-13] . It is necessary to combine personal characteristics into traditional indoor environmental analysis in order to estimate the needs of user and environment.  \nThe purpose of this study is to evaluate the indoor environment quality of university classrooms, including thermal, air quality, light and acoustics, through experimental and subjective measurements under different building ventilation and air-conditioning operation modes. Consider","cbCaipVD6m32WmjM","https://ap.wps.com/l/cbCaipVD6m32WmjM","pdf",660764,1,4,"English","en",105,"# Introduction\n## Indoor environment quality challenges in classrooms\n## Need to integrate personal characteristics\n# Methods\n## Site description\n## Data collection\n## Subjective questionnaire design\n## Data analysis and prediction model","[{\"question\":\"What indoor conditions were measured in the university classroom experiments?\",\"answer\":\"The study measured temperature, humidity, air pollutants, lighting, and acoustics across different seasonal ventilation and HVAC operation modes.\"},{\"question\":\"What key findings were observed about indoor particulate matter and student symptoms?\",\"answer\":\"During autumn with natural ventilation under severe outdoor pollution, nearly 25% of indoor particulate matter exceeded GB18883, and more than 20% of students reported drowsiness, dizziness, chest tightness, poor breathing, and affective symptoms.\"},{\"question\":\"How does the proposed machine-learning IEQ model compare with the traditional PMV-PPD approach?\",\"answer\":\"The back-propagation neural network provides high prediction accuracy (about 75%), whereas the PMV-PPD model achieves only about 28% for thermal sensation prediction.\"}]","Optimization of Indoor Quality and Thermal Comfort for University Classrooms Using Data-Based Machine Learning | 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indoor conditions were measured in the university classroom experiments?","Question",{"text":74,"@type":75},"The study measured temperature, humidity, air pollutants, lighting, and acoustics across different seasonal ventilation and HVAC operation modes.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What key findings were observed about indoor particulate matter and student symptoms?",{"text":79,"@type":75},"During autumn with natural ventilation under severe outdoor pollution, nearly 25% of indoor particulate matter exceeded GB18883, and more than 20% of students reported drowsiness, dizziness, chest tightness, poor breathing, and affective symptoms.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the proposed machine-learning IEQ model compare with the traditional PMV-PPD approach?",{"text":83,"@type":75},"The back-propagation neural network provides high prediction accuracy (about 75%), whereas the PMV-PPD model achieves only about 28% for thermal sensation 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