Energy and Buildings最新文献

{"title":"The thermal sensitivity (Griffiths constant) of occupants for naturally ventilated buildings in China","authors":"Wen Cao ,&nbsp;Xiaodong Xuan ,&nbsp;Yuchen Wang ,&nbsp;Weidong Wu","doi":"10.1016/j.enbuild.2025.115786","DOIUrl":"10.1016/j.enbuild.2025.115786","url":null,"abstract":"<div><div>The thermal sensitivity (Griffiths constant, G value) of occupants is a crucial parameter in the adaptive thermal comfort domain, which affects the neutral temperature and adaptive models. In existing studies, the Griffiths constant is typically 0.5 °C⁻¹, which is derived mainly from office buildings. There are few studies on whether the use of 0.5 °C⁻¹ is suitable for all buildings and what factors influence human thermal sensitivity. This study derived the Griffiths constant based on the field survey for naturally ventilated buildings in China, and the factors influencing the G value were deeply analysed. Instrument tests and subjective questionnaires were carried out simultaneously in the survey. There were 1448 valid participants from October 2021 to May 2023. The results indicate that the Griffiths constant varies across different types of buildings: 0.261 °C⁻¹ in the dormitory and 0.318 °C⁻¹ in the library. The gender difference in G values is statistically significant. The thermal sensitivity of females is 9.4 % greater than that of males. The Griffiths constant increases with increasing indoor operative temperature and decreases with increasing clothing insulation within the relatively comfortable thermal environment. Thermal perception significantly affects the Griffiths constant. Notably, the more comfortable the conditions are, the greater the thermal sensitivity. The neutral temperature calculated from the G value obtained via field surveys is more accurate than that from the universal G value. This study enriches adaptive thermal comfort theory and provides a valuable reference for the accurate establishment of the adaptive model of the Griffiths method.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115786"},"PeriodicalIF":6.6,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143907775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-condition building decarbonization using deep reinforcement learning and large language model","authors":"Limao Zhang ,&nbsp;Jiaxin Huang ,&nbsp;Chao Chen","doi":"10.1016/j.enbuild.2025.115810","DOIUrl":"10.1016/j.enbuild.2025.115810","url":null,"abstract":"<div><div>The subjectivity of building management and the lack of human–machine interaction make building operation decarbonization challenging. This work designs a building control-oriented optimization framework to reduce carbon emissions and automatically produce the strategies. Firstly, building information modeling is constructed by referring to on-site data and weather conditions to create a multi-condition dataset. Secondly, the surrogate models regarding carbon emissions and thermal comforts correspond to 22-30℃, 30-35℃, and above 35℃ during the hot season. Thirdly, the optimal decarbonization strategy under different weather conditions is identified by deep reinforcement learning. Finally, a human–machine interactive interface is developed to display the results and provide valuable suggestions. The proposed decarbonization framework has been validated in a green building in China, and the results reveal that: (1) The building information model can accurately simulate actual carbon emissions with an error of 0.1%. (2) The surrogate models show excellent prediction for carbon emissions and thermal comforts with an R² of 0.93 in the testing sets. (3) The optimization rates corresponding to 22-30℃, 30-35℃, and above 35℃ are 47.28%, 17.75%, and 13.58%, respectively, and the decarbonization-based LLM can provide practical strategy according to outdoor temperature and user preferences. The work contributes to weather-based building control optimization and the development of a building decarbonization large language interaction model.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115810"},"PeriodicalIF":6.6,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Students’ thermal comfort and cognitive performance in tropical climates: A comparative study","authors":"Chao Cen ,&nbsp;Erna Tan ,&nbsp;Selvam Valliappan ,&nbsp;Edward Ang ,&nbsp;Zhimin Chen ,&nbsp;Nyuk Hien Wong","doi":"10.1016/j.enbuild.2025.115817","DOIUrl":"10.1016/j.enbuild.2025.115817","url":null,"abstract":"<div><div>Group differences in thermal comfort have been widely observed in previous studies. Understanding these disparities is crucial for creating comfortable and conducive thermal environments for diverse population groups. This study investigated the thermal perceptions, physiological responses, and cognitive performances of teenage secondary school and adult university students in Elevated Air Temperature and Velocity (EATAV) settings in tropical Singapore. A comprehensive comparative analysis was conducted through experiments with university students in a mixed-mode ventilation university classroom and field studies with secondary school students in fan-assisted naturally ventilated classroom environments. Results revealed significant distinctions between secondary school and university students. Secondary school students perceived warm thermal conditions less intensely and had 1.2 °C higher neutral Standard Effective Temperature (SET*) compared to the university students, indicating their better thermal resilience to the EATAV environments. Physiologically, secondary school students exhibit higher value of arm skin temperature, wrist skin temperature, and heart rate across all the SET*s and thermal sensations. In terms of cognitive performance, it was observed that warm sensations negatively impacted cognitive performance test scores for both groups, with secondary school students experiencing a more pronounced effect (17 % reduction) compared to university students (10 % reduction). Interestingly, cool sensations did not significantly affect cognitive performance for either group. These findings emphasize the necessity of considering demographic differences when designing EATAV environments to ensure comfort and productivity across diverse populations, particularly in educational settings.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115817"},"PeriodicalIF":6.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Demand response for renewable energy communities: Exploring coordination of prosumer-generated PV and flexible aggregated demand in the Italian framework","authors":"Patricia Ercoli ,&nbsp;Alice Mugnini ,&nbsp;Alessia Arteconi","doi":"10.1016/j.enbuild.2025.115814","DOIUrl":"10.1016/j.enbuild.2025.115814","url":null,"abstract":"<div><div>Renewable Energy Communities (RECs) offer a decentralized approach to integrate Distributed Energy Resources (DER) and non-programmable Renewable Energy Sources (RESs), such as photovoltaics (PVs). However, achieving full self-sufficiency and maximizing the use of RESs remains a challenge due to seasonal variability and differences between supply and demand. This study explores energy flexibility in RECs using centralized Demand Response (DR) strategies adapted to the Italian context. A single prosumer with PV generation and multiple consumers with varying building characteristics are considered. Centralized linear programming (LP) is used to optimize energy management and coordinate the collective consumption of prosumer-generated PV. The results show that coordinated building response increases PV self-consumption and reduces electricity costs, but the potential for flexibility depends on factors such as community composition, PV availability, and energy sharing strategies. For example, aggregate displaceable energy for pre-cooling drops from 56.39% (one consumer) to 23.33% (six consumers), while larger PV systems improve flexibility (e.g., from 23.33% with 5.4 kWp to 50.56% with 10.8 kWp of energy shifted for pre-cooling strategies). In addition, DR strategies aligned with prosumer self-consumption demands can allow up to 51.96% of displaceable energy for pre-cooling and 30.91% for pre-heating. Thus, tailored strategies in REC design and operation are essential to maximize energy and economic performance, emphasizing the importance of customized solutions for sustainable and resilient energy systems.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"340 ","pages":"Article 115814"},"PeriodicalIF":6.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903251","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impacts of people’s behavioral patterns and built environment features on daily carbon footprints","authors":"Jianwei Huang ,&nbsp;Mei-Po Kwan","doi":"10.1016/j.enbuild.2025.115819","DOIUrl":"10.1016/j.enbuild.2025.115819","url":null,"abstract":"<div><div>Green and compact built environmental features are believed to be sustainable urban environmental designs that can facilitate low-carbon behaviors by reshaping people’s daily behaviors. Previous studies in this field tend to use spatially aggregated data or ignore people’s daily mobility, which might generate misleading empirical findings. Therefore, this study seeks to go beyond previous studies by using individual-level data to examine the associations between individuals’ daily carbon footprints with their daily behavioral patterns and built environmental features around their residential areas and daily activity locations. Specifically, using individual-level data collected by portable real-time sensors, an activity-travel diary, and a questionnaire from four communities in Hong Kong, we found that the daily travel radius of gyration and the out-of-home time ratio were strongly negatively associated with daily carbon footprints. Additionally, built environment features, particularly the density of open space and recreational land, woodland, shrubland, and commercial land, were directly and indirectly associated with carbon footprints. These associations varied across different communities and different measurements of built environment features. These findings have significant implications for sustainable built environmental design and low-carbon society transition strategies. They also highlight the significance of using individual-level data to examine the impacts of people’s behavioral patterns and built environment features on daily carbon footprints, thus providing a broader perspective for future research in this field.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115819"},"PeriodicalIF":6.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interpretable building energy performance prediction using XGBoost Quantile Regression","authors":"Sinem Guler Kangalli Uyar ,&nbsp;Bilge Kagan Ozbay ,&nbsp;Berker Dal","doi":"10.1016/j.enbuild.2025.115815","DOIUrl":"10.1016/j.enbuild.2025.115815","url":null,"abstract":"<div><div>This study focuses on predicting the Building Energy Performance Ratio (BEPR) of 3,594 residential buildings in Istanbul using machine learning algorithms. The main objective is to identify the factors affecting BEPR, examine their influence, and analyze how these factors differ across buildings with low and high energy efficiency. To achieve this, seven machine learning models were evaluated: Multiple Linear Regression (MLR), Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Artificial Neural Networks (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The results show that XGBoost yields the highest accuracy among all models. To improve the interpretability of XGBoost, the Shapley Additive Explanations (SHAP) method was employed, enabling the assessment of the impact of various features (such as wall U-value, window U-value, and building age) on BEPR. The analysis revealed that building thermal properties and age are critical factors in determining BEPR. Additionally, by applying the XGBoost Quantile Regression (XGBoost-QR) algorithm, the distribution of BEPR across different quantiles (low, medium, and high) was analyzed more effectively. This approach demonstrated that the features influencing BEPR vary between buildings with low and high energy efficiency. Specifically, in the lower quantiles, structural features such as wall and window insulation have a greater impact on BEPR, whereas in the higher quantiles, building age and roof insulation become more influential. This research contributes to a better understanding of the determinants of residential energy performance, introduces the integration of XGBoost-QR into energy performance analysis, and offers valuable insights for enhancing energy efficiency strategies.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"340 ","pages":"Article 115815"},"PeriodicalIF":6.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermal performance of traditional cavity walls: Accounting for complex cavity geometry and bonding variability","authors":"Xiaolin Chen ,&nbsp;Qing Chun ,&nbsp;Nathan Van Den Bossche","doi":"10.1016/j.enbuild.2025.115816","DOIUrl":"10.1016/j.enbuild.2025.115816","url":null,"abstract":"<div><div>As a significant feature of traditional dwellings and modern rural buildings in China, traditional Chinese cavity walls have always been valued for their material efficiency and thermal performance. However, the thermal implications of their irregular cavity geometries—shaped by diverse bond patterns—remain underexplored. This research, for the first time, aligns traditional Chinese cavity bond patterns with brickwork in international handbooks and combine experimental validation with 3D modeling to quantify the impact of bond patterns and boundary conditions on the thermal performance.</div><div>Validated simulation results, with deviations under 10 %, show that radiation accounts for approximately 60 % of heat transfer across cavities, while convection has a comparatively minor effect. Thermal resistance ranges from 0.30 to 0.43  W/m²·K for 240  mm cavity walls, with the Flemish bond showing the lowest performance. Larger cavities and increased wall thickness enhance thermal resistance, and cavity walls generally outperform solid walls, albeit modestly. Brick thermal conductivity and surface emissivity are identified as dominant factors influencing energy performance. Moreover, clay infill as a practical strategy is confirmed in improving the thermal performance. This research provides new insights into the optimization of cavity walls for historic building, providing a critical foundation for future hygrothermal simulation and conservation-informed decision-making. The findings suggest that leveraging the unique cavity wall geometry and material strategies could inspire innovative, energy-efficient solutions for building retrofit.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115816"},"PeriodicalIF":6.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification of aleatoric and epistemic uncertainty in Data-Driven occupant behavior model for building performance simulation","authors":"Sunghyun Kim ,&nbsp;Cheol Soo Park","doi":"10.1016/j.enbuild.2025.115818","DOIUrl":"10.1016/j.enbuild.2025.115818","url":null,"abstract":"<div><div>Occupant behavior (OB) is a major source of uncertainty in building performance simulations, significantly influencing energy consumption and indoor environmental conditions. While machine learning-based OB models have shown strong predictive capabilities, their reliability is often undermined by data limitations, extrapolation errors, and a lack of explicit uncertainty quantification. This study introduces a Bayesian Deep Learning (BDL) framework to address these challenges by quantifying OB-related uncertainties. Using Monte Carlo (MC) dropout, the framework distinguishes between aleatoric uncertainty (inherent randomness) and epistemic uncertainty (knowledge limitations).</div><div>Experiments were conducted using data from six residential households in Seoul, South Korea, over a three-month summer period. This study examines how the training period affects uncertainty and evaluates its impact on energy predictions through a co-simulation with EnergyPlus and BCVTB. Results indicate that aleatoric uncertainty was the dominant factor during model validation, primarily due to sensor noise and unpredictable occupant behavior. However, epistemic uncertainty increased in the co-simulation, especially under extrapolated conditions, leading to greater variability in energy predictions. Extending the training period reduced epistemic uncertainty, lowering the coefficient of variation in energy predictions from 54.3% to 20.4%, but had no noticeable effect on aleatoric uncertainty, which substantiates the inherent unpredictability of OB.</div><div>These findings highlight the need to incorporate uncertainty metrics alongside accuracy metrics when evaluating data-driven OB models, as prediction confidence is crucial for simulation-based decision-making. Moreover, the observed uncertainty propagation in energy predictions underscores the advantages of probabilistic modeling over deterministic approaches. This study provides a systematic framework for integrating uncertainty analysis into data-driven OB modeling, offering insights into improving model robustness, generalizability, and practical applicability in building performance simulations.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"340 ","pages":"Article 115818"},"PeriodicalIF":6.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143903250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulating retrofit incentives and distributional effects of four tenancy law policies A model analysis using concrete buildings and retrofit options within the German regulatory context","authors":"Leo Reutter ,&nbsp;Bernadetta Winiewska","doi":"10.1016/j.enbuild.2025.115805","DOIUrl":"10.1016/j.enbuild.2025.115805","url":null,"abstract":"<div><div>The primary landlord-tenant dilemma arising from rent control prevents landlords from recovering costs of energy-efficiency retrofits, which mainly benefit tenants if the landlord undertakes the retrofit. This requires tenancy law to allocate retrofit and energy costs adequately. Previous research discusses the dilemma generally or examines policy options without numeric estimations of their long-term effects. This paper addresses this research gap by conducting a thorough comparative quantitative analysis of several policy options. The study analyzes the impact of Germany’s current system and three reform options on landlords’ and tenants’ financial costs and benefits using simulations across various building sizes and retrofit ambitions. Investment costs exceed energy savings in 12 of the 15 retrofit projects examined. The discounted project payoff — the lifetime benefit of the retrofit compared to maintaining the status quo — ranges from −1.07 to 0.11 €₂₀₂₃/m²/month. The status quo system and one reform option almost always incentivize landlords to forego retrofits. Only two reform options consistently incentivize landlord investment, albeit at tenants’ expense. A sensitivity analysis shows these systems’ effectiveness is not affected by the details of German general tenancy law and local rent markets’ characteristics (rent levels and their inflation, valuation of energy efficiency). Designing landlords’ retrofit premia to depend on the technically estimated energy demand cost savings is especially promising as it incentivizes retrofits when profitable. Seven cases where a retrofit is profitable for the landlord but not from a project perspective are primarily due to the rebound effect. Under this system, landlords’ and tenants’ benefits from the retrofit, compared to continued maintenance, range from −0.28 to 0.42 and −1.10 to −0.02 €₂₀₂₃/m²/month, respectively.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115805"},"PeriodicalIF":6.6,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143907774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel method of calculating mean skin temperature with high thermal sensitivity for thermal sensation evaluation","authors":"Xiaowen Su ,&nbsp;Ongun Berk Kazanci ,&nbsp;Bjarne W. Olesen ,&nbsp;Liangliang Sun ,&nbsp;Yanping Yuan","doi":"10.1016/j.enbuild.2025.115809","DOIUrl":"10.1016/j.enbuild.2025.115809","url":null,"abstract":"<div><div>Mean skin temperature of the body is an important physiological parameter reflecting human thermal sensation and state of heat exchange between human body and its surrounding thermal environment. There is limited research on the mean skin temperature distinguishing the cool and warm sensation, even though the sensitivity of local body parts to cold and warm is not identical. Therefore, a novel method of calculating mean skin temperatures (<em>t</em>_nsk) with the weighted mean of the influential body parts was established to reflect personal thermal sensation in moderate cool and warm environments. An experimental study was conducted in a climate chamber where sedentary occupants were exposed to the temperatures between 18 °C to 28 °C with different clothing ensembles to maintain thermal statuses of being between “slightly cool” to “slightly warm”. Thermal sensations and local skin temperatures of occupants were compared between neutral and non-neutral conditions to determine the influential body sites for <em>t</em>_nsk. It was found that in slightly cool conditions with the winter clothing of 1.0 clo, <em>t</em>_nsk could be explained by the weighted mean of instep (coefficient of 0.28), back lower leg (0.23), front thigh (0.18), low arm (0.16) and upper arm (0.15), while in slightly warm conditions with the summer clothes of 0.5 clo, <em>t</em>_nsk could be explained by the weighted mean of instep (0.15), hand (0.17), front thigh (0.19), neck (0.14), low arm (0.20) and upper arm (0.15). The results of this study could guide designs of personal thermal comfort systems by preserving individual body temperature homeostasis.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"340 ","pages":"Article 115809"},"PeriodicalIF":6.6,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143900399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}