Pub Date : 2025-02-01DOI: 10.1016/j.buildenv.2024.112461
Da Yan , Yi Wu , Jeetika Malik , Tianzhen Hong
Weather plays a significant role in building operations as it directly influences HVAC loads and in turn the building energy and thermal performance. In a changing climate, future trends and extreme weather events become critical concerns in the global building decarbonization and clean energy transition. This paper aims to address ten key questions concerning extreme and future weather data for building applications, and more importantly to identify research gaps and guide the curation and selection of future and extreme weather data for use in building performance simulation and assessment. The paper intends to inform architects and engineers, operators, owners, policy makers, and other stakeholders on considering the impacts of future and extreme weather data and adopting strategies for selecting and applying this data in various use cases related to building design, operation, and retrofit for energy efficiency, electrification, and climate resilience.
{"title":"Ten questions on future and extreme weather data for building simulation and analysis in a changing climate","authors":"Da Yan , Yi Wu , Jeetika Malik , Tianzhen Hong","doi":"10.1016/j.buildenv.2024.112461","DOIUrl":"10.1016/j.buildenv.2024.112461","url":null,"abstract":"<div><div>Weather plays a significant role in building operations as it directly influences HVAC loads and in turn the building energy and thermal performance. In a changing climate, future trends and extreme weather events become critical concerns in the global building decarbonization and clean energy transition. This paper aims to address ten key questions concerning extreme and future weather data for building applications, and more importantly to identify research gaps and guide the curation and selection of future and extreme weather data for use in building performance simulation and assessment. The paper intends to inform architects and engineers, operators, owners, policy makers, and other stakeholders on considering the impacts of future and extreme weather data and adopting strategies for selecting and applying this data in various use cases related to building design, operation, and retrofit for energy efficiency, electrification, and climate resilience.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"269 ","pages":"Article 112461"},"PeriodicalIF":7.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.buildenv.2024.112427
Chenxue Song , Xiaohong Zheng , Lei Han , Qinjie Wan , Jiayu Huang , Zhen Ding , Hua Qian
Dental aerosols pose a risk to dental healthcare workers (DHCWs). High-volume evacuators (HVE) are commonly recommended to efficiently eliminate dental aerosols. However, there are limited objective recommendations concerning the operating distance and angle of HVE. This study investigated the contamination of dental aerosol particles during dental drilling, rinsing, and polishing, with and without HVE. The primary objective was to evaluate the efficiency of HVE with various operating parameters both qualitatively and quantitatively. A dental manikin with a complete oral cavity simulated the patient, and aerosol dispersion represented by condensed droplets was visualized by a green laser. A laser particle counter (AeroTrak 9303, TSI) was used to measure particle size. We also employed aerosol monitors (DustTrak II 8530 & 8532, TSI) to determine aerosol mass concentrations. Our results showed that HVE has limited effectiveness in managing large particles (5.0–25.0 μm). Furthermore, we found a significant negative correlation between the reduction rate of HVE and the operation distance (ρ < 0, p < 0.05, Spearman's correlation test), highlighting the necessity of placing the suction nozzle as close as possible to the patient's oral cavity. The maximum effective distance should not exceed 10 cm. Notably, increasing the angle had a substantial negative impact on reducing particle concentration in the breathing zone of the operator, showing the recommendation for parallel positioning of the suction nozzle. These results underscore the importance of optimizing HVE usage to enhance aerosol management during dental procedures, thereby reducing potential health risks.
{"title":"Optimizing high-volume evacuator usage: A quantitative analysis of aerosol control in dental procedures","authors":"Chenxue Song , Xiaohong Zheng , Lei Han , Qinjie Wan , Jiayu Huang , Zhen Ding , Hua Qian","doi":"10.1016/j.buildenv.2024.112427","DOIUrl":"10.1016/j.buildenv.2024.112427","url":null,"abstract":"<div><div>Dental aerosols pose a risk to dental healthcare workers (DHCWs). High-volume evacuators (HVE) are commonly recommended to efficiently eliminate dental aerosols. However, there are limited objective recommendations concerning the operating distance and angle of HVE. This study investigated the contamination of dental aerosol particles during dental drilling, rinsing, and polishing, with and without HVE. The primary objective was to evaluate the efficiency of HVE with various operating parameters both qualitatively and quantitatively. A dental manikin with a complete oral cavity simulated the patient, and aerosol dispersion represented by condensed droplets was visualized by a green laser. A laser particle counter (AeroTrak 9303, TSI) was used to measure particle size. We also employed aerosol monitors (DustTrak II 8530 & 8532, TSI) to determine aerosol mass concentrations. Our results showed that HVE has limited effectiveness in managing large particles (5.0–25.0 μm). Furthermore, we found a significant negative correlation between the reduction rate of HVE and the operation distance (<em>ρ</em> < 0, <em>p</em> < 0.05, Spearman's correlation test), highlighting the necessity of placing the suction nozzle as close as possible to the patient's oral cavity. The maximum effective distance should not exceed 10 cm. Notably, increasing the angle had a substantial negative impact on reducing particle concentration in the breathing zone of the operator, showing the recommendation for parallel positioning of the suction nozzle. These results underscore the importance of optimizing HVE usage to enhance aerosol management during dental procedures, thereby reducing potential health risks.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"269 ","pages":"Article 112427"},"PeriodicalIF":7.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.buildenv.2024.112433
Alex Donkers, Dujuan Yang, Bauke de Vries, Nico Baken
Methods to assess the performance of a building have been developed for decades, however, many buildings still do not satisfy their occupants in their indoor comfort preferences. This paper presents methods to generate insights from semantic digital twins on the perceived comfort levels of individuals to tighten the as-designed and as-perceived building performance gap. This paper first reviews existing personal indoor comfort models and shares state-of-the-art semantic web technologies in this domain. The paper then presents a generic framework to integrate heterogeneous data into knowledge graphs and use them in data mining processes. This framework is then applied to a case study in the Vertigo building in Eindhoven, The Netherlands. A wide range of information is collected, including building information models, indoor and outdoor sensor data, personal information, and feedback on indoor environmental quality. The integrated data are then used to create personal comfort models. First, multinomial logistic regression models are used to predict future dissatisfaction, after which a latent class analysis created cohorts of people with similar indoor comfort preferences. The results are stored back into the knowledge graph, after which they could be used in other applications, such as to perform occupant-centric control of systems. The methods presented in this paper are summarized in a generic framework that can be used and extended to other domains that aim to combine data integration and data mining.
{"title":"Personal indoor comfort models through knowledge discovery in cross-domain semantic digital twins","authors":"Alex Donkers, Dujuan Yang, Bauke de Vries, Nico Baken","doi":"10.1016/j.buildenv.2024.112433","DOIUrl":"10.1016/j.buildenv.2024.112433","url":null,"abstract":"<div><div>Methods to assess the performance of a building have been developed for decades, however, many buildings still do not satisfy their occupants in their indoor comfort preferences. This paper presents methods to generate insights from semantic digital twins on the perceived comfort levels of individuals to tighten the as-designed and as-perceived building performance gap. This paper first reviews existing personal indoor comfort models and shares state-of-the-art semantic web technologies in this domain. The paper then presents a generic framework to integrate heterogeneous data into knowledge graphs and use them in data mining processes. This framework is then applied to a case study in the Vertigo building in Eindhoven, The Netherlands. A wide range of information is collected, including building information models, indoor and outdoor sensor data, personal information, and feedback on indoor environmental quality. The integrated data are then used to create personal comfort models. First, multinomial logistic regression models are used to predict future dissatisfaction, after which a latent class analysis created cohorts of people with similar indoor comfort preferences. The results are stored back into the knowledge graph, after which they could be used in other applications, such as to perform occupant-centric control of systems. The methods presented in this paper are summarized in a generic framework that can be used and extended to other domains that aim to combine data integration and data mining.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"269 ","pages":"Article 112433"},"PeriodicalIF":7.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.buildenv.2024.112408
Steven S. Salim , Stefan L. Luxembourg , Francesco Dalla Longa , Bob van der Zwaan
The urgency to combat global climate change has prompted the implementation of many regional and national policies, with the European Green Deal standing out as one of the most significant initiatives. We use the recently developed TIMES-Europe energy system model to study the pivotal role of the residential sector in achieving the ambitious energy savings goal formulated by the EU in the broad framework of its Fit-for-55 policy package. We observe improved energy efficiency in households across a set of scenarios that differ in terms of climate policy ambition and level of collaboration between European countries. We find that, by 2030, residential buildings can realize up to 3000 PJ in overall energy savings. Cross-country collaboration may yield an additional 11% reduction in residential energy consumption, which could increase to 18% when combined with dwelling retrofitting subsidies. Our analysis of the transformation of the residential fuel mix, particularly the interplay between biomass and natural gas usage, reveals a trade-off between short-term energy efficiency gains and long-term CO2 reduction goals. In the short run, scenarios lacking stringent climate control measures exhibit higher energy savings in comparison to those embracing ambitious climate targets. This initial difference, however, diminishes over time: energy savings from climate-compliant scenarios nearly align with those in scenarios without strict climate compliance around 2040. Scenarios lacking effective climate policy result in significant delays in decarbonizing the European residential sector. Our findings underscore the critical role of ambitious climate targets and accelerated retrofitting rates, highlighting the necessity to imminently and proactively implement directed policy interventions.
{"title":"Decarbonisation scenarios for the European residential sector","authors":"Steven S. Salim , Stefan L. Luxembourg , Francesco Dalla Longa , Bob van der Zwaan","doi":"10.1016/j.buildenv.2024.112408","DOIUrl":"10.1016/j.buildenv.2024.112408","url":null,"abstract":"<div><div>The urgency to combat global climate change has prompted the implementation of many regional and national policies, with the European Green Deal standing out as one of the most significant initiatives. We use the recently developed TIMES-Europe energy system model to study the pivotal role of the residential sector in achieving the ambitious energy savings goal formulated by the EU in the broad framework of its Fit-for-55 policy package. We observe improved energy efficiency in households across a set of scenarios that differ in terms of climate policy ambition and level of collaboration between European countries. We find that, by 2030, residential buildings can realize up to 3000 PJ in overall energy savings. Cross-country collaboration may yield an additional 11% reduction in residential energy consumption, which could increase to 18% when combined with dwelling retrofitting subsidies. Our analysis of the transformation of the residential fuel mix, particularly the interplay between biomass and natural gas usage, reveals a trade-off between short-term energy efficiency gains and long-term CO<sub>2</sub> reduction goals. In the short run, scenarios lacking stringent climate control measures exhibit higher energy savings in comparison to those embracing ambitious climate targets. This initial difference, however, diminishes over time: energy savings from climate-compliant scenarios nearly align with those in scenarios without strict climate compliance around 2040. Scenarios lacking effective climate policy result in significant delays in decarbonizing the European residential sector. Our findings underscore the critical role of ambitious climate targets and accelerated retrofitting rates, highlighting the necessity to imminently and proactively implement directed policy interventions.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"269 ","pages":"Article 112408"},"PeriodicalIF":7.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.buildenv.2024.112446
Lei Wang , Fei Li , Chengwen Yang , Lihang Feng , Xiaodong Cao
The bionic electronic nose mimics the olfactory system of living organisms, enabling the detection and differentiation of odors or gas mixtures. With appropriate data processing and pattern recognition algorithms, it can be used in environmental quality analysis and process control. The existing electronic nose algorithms have problems such as data processing difficulties, model complexity and high computational cost. In this study, we investigated the performances of various pattern recognition algorithms for classifying and identifying different gases, especially the performances of the lightweight algorithms, based on publicly available dataset. First, the response signals from the sensor array were converted into response maps. Then, several pattern recognition algorithms, including HOG_SVM, VGG16, SqueezeNet, ShuffleNet v2, MobileNet v3, and GhostNet were used to identify the gas species in unmixed and mixed gas datasets. And the performances of the pattern recognition algorithms were evaluated. The results show that VGG16 achieves the highest accuracy, attaining a perfect 100% on the unmixed gas dataset and 95.14% on the mixed gas dataset. Among lightweight models, SqueezeNet demonstrated superior performance, achieving an accuracy of 94.44% on the mixed gas dataset. Furthermore, SqueezeNet has the fewest parameters, totaling just 0.74 million. Meanwhile, MobileNet v3 is notable for its minimal computational cost, requiring only 0.059 billion Floating Point Operations Per Second (FLOPs). Deployment on an edge computing platform, such as the NVIDIA Jetson Xavier, enabled MobileNet v3 to achieve optimal performance in terms of inference latency and memory consumption.
{"title":"Performance evaluation of lightweight pattern recognition algorithms for portable environmental monitoring electronic noses","authors":"Lei Wang , Fei Li , Chengwen Yang , Lihang Feng , Xiaodong Cao","doi":"10.1016/j.buildenv.2024.112446","DOIUrl":"10.1016/j.buildenv.2024.112446","url":null,"abstract":"<div><div>The bionic electronic nose mimics the olfactory system of living organisms, enabling the detection and differentiation of odors or gas mixtures. With appropriate data processing and pattern recognition algorithms, it can be used in environmental quality analysis and process control. The existing electronic nose algorithms have problems such as data processing difficulties, model complexity and high computational cost. In this study, we investigated the performances of various pattern recognition algorithms for classifying and identifying different gases, especially the performances of the lightweight algorithms, based on publicly available dataset. First, the response signals from the sensor array were converted into response maps. Then, several pattern recognition algorithms, including HOG_SVM, VGG16, SqueezeNet, ShuffleNet v2, MobileNet v3, and GhostNet were used to identify the gas species in unmixed and mixed gas datasets. And the performances of the pattern recognition algorithms were evaluated. The results show that VGG16 achieves the highest accuracy, attaining a perfect 100% on the unmixed gas dataset and 95.14% on the mixed gas dataset. Among lightweight models, SqueezeNet demonstrated superior performance, achieving an accuracy of 94.44% on the mixed gas dataset. Furthermore, SqueezeNet has the fewest parameters, totaling just 0.74 million. Meanwhile, MobileNet v3 is notable for its minimal computational cost, requiring only 0.059 billion Floating Point Operations Per Second (FLOPs). Deployment on an edge computing platform, such as the NVIDIA Jetson Xavier, enabled MobileNet v3 to achieve optimal performance in terms of inference latency and memory consumption.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"269 ","pages":"Article 112446"},"PeriodicalIF":7.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.buildenv.2024.112329
Shanshan Liu , Alex Vlachokostas , Eleftheria Kontou
School buses transport 20 million students annually and are currently undergoing electrification in the U.S. With Vehicle-to-Building (V2B) technology, electric school buses (ESBs) can supply energy to school buildings during power outages, ensuring continued operation and youth safety. This study proposes assessing the resilience of secondary schools during outages by leveraging ESB fleets as backup power providers. The findings reveal that the current fleet of ESBs in representative U.S. cities across nine climate regions is insufficient to meet the power demand of an entire school or even its HVAC system. However, we estimated the number of ESBs required to support a school’s power needs, and we showed that the use of V2B technology significantly reduces carbon emissions compared to backup diesel generators. While adjusting HVAC setpoints and installing solar panels have limited impacts on enhancing school resilience, gathering students in classrooms during outages significantly improved resilience in our case study in Houston, Texas. It is essential for schools to complement ESBs with stationary batteries and other backup power sources or smart management strategies to effectively address prolonged outages. Determining the deployment of direct current fast and Level 2 chargers can reduce infrastructure costs while maintaining the resilience benefits of ESBs.
{"title":"Resilience and environmental benefits of electric school buses as backup power for educational functions continuation during outages","authors":"Shanshan Liu , Alex Vlachokostas , Eleftheria Kontou","doi":"10.1016/j.buildenv.2024.112329","DOIUrl":"10.1016/j.buildenv.2024.112329","url":null,"abstract":"<div><div>School buses transport 20 million students annually and are currently undergoing electrification in the U.S. With Vehicle-to-Building (V2B) technology, electric school buses (ESBs) can supply energy to school buildings during power outages, ensuring continued operation and youth safety. This study proposes assessing the resilience of secondary schools during outages by leveraging ESB fleets as backup power providers. The findings reveal that the current fleet of ESBs in representative U.S. cities across nine climate regions is insufficient to meet the power demand of an entire school or even its HVAC system. However, we estimated the number of ESBs required to support a school’s power needs, and we showed that the use of V2B technology significantly reduces carbon emissions compared to backup diesel generators. While adjusting HVAC setpoints and installing solar panels have limited impacts on enhancing school resilience, gathering students in classrooms during outages significantly improved resilience in our case study in Houston, Texas. It is essential for schools to complement ESBs with stationary batteries and other backup power sources or smart management strategies to effectively address prolonged outages. Determining the deployment of direct current fast and Level 2 chargers can reduce infrastructure costs while maintaining the resilience benefits of ESBs.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"269 ","pages":"Article 112329"},"PeriodicalIF":7.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.buildenv.2024.112428
Shihong Zhang , Ya Zhou , Liutao Chen , Yixin Huang , Zhe Wang
Building type is among the most important inputs for building energy model. However, the information of building type is always missing in urban scale building energy modeling. This paper presents a novel approach to infer building type from building name. First, we created the building name text dataset through the fusion of GIS spatial data. A rule-based method was developed to estimate building types using naming features. We then trained five machine learning classifiers, including four transformer models and one Multilayer Perceptron model, to predict building types. Finally, we leveraged the inferred building type information for building energy consumption simulation, addressing the crucial data scarcity issue in urban-scale building energy models. Experimental results indicated that our rule-based classification method achieved a precision of 84.3%. The RoBERTa model, the best-performing natural language processing (NLP) model, reached a precision of 91.6% with both Chinese and English names as NLP model inputs, showcasing a 1.3% enhancement compared to solely utilizing the Chinese dataset and a 1.8% improvement compared to solely utilizing the English dataset. This research proposes a useful framework to infer building type by leveraging the state-of-art NLP techniques, paving the way for more accurate and efficient urban-scale building energy modelling.
{"title":"Inferring building type using textual data and Natural Language Processing for urban building energy modelling","authors":"Shihong Zhang , Ya Zhou , Liutao Chen , Yixin Huang , Zhe Wang","doi":"10.1016/j.buildenv.2024.112428","DOIUrl":"10.1016/j.buildenv.2024.112428","url":null,"abstract":"<div><div>Building type is among the most important inputs for building energy model. However, the information of building type is always missing in urban scale building energy modeling. This paper presents a novel approach to infer building type from building name. First, we created the building name text dataset through the fusion of GIS spatial data. A rule-based method was developed to estimate building types using naming features. We then trained five machine learning classifiers, including four transformer models and one Multilayer Perceptron model, to predict building types. Finally, we leveraged the inferred building type information for building energy consumption simulation, addressing the crucial data scarcity issue in urban-scale building energy models. Experimental results indicated that our rule-based classification method achieved a precision of 84.3%. The RoBERTa model, the best-performing natural language processing (NLP) model, reached a precision of 91.6% with both Chinese and English names as NLP model inputs, showcasing a 1.3% enhancement compared to solely utilizing the Chinese dataset and a 1.8% improvement compared to solely utilizing the English dataset. This research proposes a useful framework to infer building type by leveraging the state-of-art NLP techniques, paving the way for more accurate and efficient urban-scale building energy modelling.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"269 ","pages":"Article 112428"},"PeriodicalIF":7.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.buildenv.2024.112436
Zhiyu Chen , Xuan Yu , Chenyu Wang , Tran Quoc Khanh , Qiang Liu
Correlated Colour Temperature (CCT) is a crucial parameter for white light sources and it is closely related to the colour rendition quality of indoor lighting. In this study, two psychophysical experiments were conducted to examine the CCT recognition error of human observers and then investigate the relevant influence on the qualification of colour rendition quality. In Experiment 1, 36 lighting designers were invited to participate in a CCT recognition test with CCT values ranging from 3000 K to 6000 K, by which we obtained their recognition errors and discriminable intervals. It was found that even lighting professionals cannot recognize the CCT value very accurately and that CCT did not correlate linearly with visual perception. In Experiment 2, we further explored the influence of lighting designers’ CCT recognition errors on colour preference and discrimination judgements toward 8 kinds of objects with 50 naïve subjects by constructing a typical indoor lighting scenario. It was found that due to the CCT recognition errors revealed in Experiment 1, even professional lighting designers could hardly select the most appropriate CCT for a specific lighting scenario in terms of colour preference and colour discrimination. We hope those findings could provide a deeper understanding for the concepts of CCT and colour rendition of indoor lighting. As we believe, for quantifying the visual attributes like colour preference and discrimination, an absolute and global metric whose calculation protocol is independent of CCT value should be advocated.
{"title":"Limitations of correlated colour temperature in visual perception and its implications for evaluating the colour rendition quality of indoor lighting","authors":"Zhiyu Chen , Xuan Yu , Chenyu Wang , Tran Quoc Khanh , Qiang Liu","doi":"10.1016/j.buildenv.2024.112436","DOIUrl":"10.1016/j.buildenv.2024.112436","url":null,"abstract":"<div><div>Correlated Colour Temperature (CCT) is a crucial parameter for white light sources and it is closely related to the colour rendition quality of indoor lighting. In this study, two psychophysical experiments were conducted to examine the CCT recognition error of human observers and then investigate the relevant influence on the qualification of colour rendition quality. In Experiment 1, 36 lighting designers were invited to participate in a CCT recognition test with CCT values ranging from 3000 K to 6000 K, by which we obtained their recognition errors and discriminable intervals. It was found that even lighting professionals cannot recognize the CCT value very accurately and that CCT did not correlate linearly with visual perception. In Experiment 2, we further explored the influence of lighting designers’ CCT recognition errors on colour preference and discrimination judgements toward 8 kinds of objects with 50 naïve subjects by constructing a typical indoor lighting scenario. It was found that due to the CCT recognition errors revealed in Experiment 1, even professional lighting designers could hardly select the most appropriate CCT for a specific lighting scenario in terms of colour preference and colour discrimination. We hope those findings could provide a deeper understanding for the concepts of CCT and colour rendition of indoor lighting. As we believe, for quantifying the visual attributes like colour preference and discrimination, an absolute and global metric whose calculation protocol is independent of CCT value should be advocated.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"269 ","pages":"Article 112436"},"PeriodicalIF":7.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.buildenv.2024.112449
Yijia Wang , Zihan Wang , Nan Zhang , Wenjie Ji , Yingxin Zhu , Bin Cao
Field studies are important in thermal comfort research, as they help to better understand people's needs for thermal conditions in real life. Over the past 30 years, researchers have completed numerous thermal comfort field studies in China. However, since most of the field investigations were conducted under certain conditions, such as specific building types, regions, etc., the wider reference significance from those studies was limited. Hence, this article aims to provide a comprehensive understanding of those studies. A total of 324 publications were considered. The literature was analyzed to extract and summarize key information, mainly including the measured environmental parameters and the thermal comfort perceptions from occupants. Through this analysis, the differences between climate zones, building types, and urban and rural areas were identified. It was found that indoor temperatures in the severe cold (SC) and cold (C) zones in winter were relatively higher than those in the hot summer and cold winter (HSCW) zone. In each climate zone where both district heating and non-district heating exist, the average indoor air temperature under the former condition was higher than that under the latter. Residential buildings had lower average indoor air temperatures in winter compared with office and teaching buildings, but higher temperatures in summer. In addition, the indoor air temperature in urban areas was generally higher than that in rural areas in winter. This study also presents the variations of indoor temperatures over the years. These results cover a variety of factors and may provide reference for more reasonable indoor environment control. Tables of key information from all the selected publications are provided in the appendix for other researchers to check and conduct further studies.
{"title":"Field studies on thermal comfort in China over the past 30 years","authors":"Yijia Wang , Zihan Wang , Nan Zhang , Wenjie Ji , Yingxin Zhu , Bin Cao","doi":"10.1016/j.buildenv.2024.112449","DOIUrl":"10.1016/j.buildenv.2024.112449","url":null,"abstract":"<div><div>Field studies are important in thermal comfort research, as they help to better understand people's needs for thermal conditions in real life. Over the past 30 years, researchers have completed numerous thermal comfort field studies in China. However, since most of the field investigations were conducted under certain conditions, such as specific building types, regions, etc., the wider reference significance from those studies was limited. Hence, this article aims to provide a comprehensive understanding of those studies. A total of 324 publications were considered. The literature was analyzed to extract and summarize key information, mainly including the measured environmental parameters and the thermal comfort perceptions from occupants. Through this analysis, the differences between climate zones, building types, and urban and rural areas were identified. It was found that indoor temperatures in the severe cold (SC) and cold (C) zones in winter were relatively higher than those in the hot summer and cold winter (HSCW) zone. In each climate zone where both district heating and non-district heating exist, the average indoor air temperature under the former condition was higher than that under the latter. Residential buildings had lower average indoor air temperatures in winter compared with office and teaching buildings, but higher temperatures in summer. In addition, the indoor air temperature in urban areas was generally higher than that in rural areas in winter. This study also presents the variations of indoor temperatures over the years. These results cover a variety of factors and may provide reference for more reasonable indoor environment control. Tables of key information from all the selected publications are provided in the appendix for other researchers to check and conduct further studies.</div></div>","PeriodicalId":9273,"journal":{"name":"Building and Environment","volume":"269 ","pages":"Article 112449"},"PeriodicalIF":7.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143164246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-02-01DOI: 10.1016/j.buildenv.2024.112310
Ayca Duran , Christoph Waibel , Valeria Piccioni , Bernd Bickel , Arno Schlueter
This review applies a transformer-based topic model to reveal trends and relationships in Artificial Intelligence (AI)-driven facade research, with a focus on architectural, environmental, and structural aspects. AI methods reviewed include Machine Learning (ML), Deep Learning (DL), and Computer Vision (CV). Overall, a significantly growing interest in applying AI methods can be observed across all research areas. However, noticeable differences exist between the three topics. While CV and DL techniques are applied to image data in research on the architectural design of facades, research on environmental aspects of facades often uses numerical data with relatively small datasets and classical ML models. Research on facade structure also tends to use image data but also incorporates numerical performance prediction. A major limitation remains a lack of generalizability, which could be addressed by more comprehensive datasets and novel DL techniques. These include concepts such as Physics-Informed Neural Networks, where domain knowledge is integrated into hybrid data-driven models, and multi-modal diffusion models, which offer generative modeling capabilities to support inverse and forward design tasks. The trends and directions outlined in this review suggest that AI will continue to advance facade research and, in line with other domains, has the potential to achieve a level of maturity suitable for adoption beyond academia and into practice.
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