Energy and Buildings最新文献

{"title":"Assessing the thermal performance of green walls for solar shading: Model validation and implementation","authors":"Roberto Rugani ,&nbsp;Giacomo Salvadori ,&nbsp;Caterina Gargari ,&nbsp;Marco Picco ,&nbsp;Patrizia De Rossi ,&nbsp;Arianna Latini ,&nbsp;Carlo Bibbiani ,&nbsp;Fabio Fantozzi","doi":"10.1016/j.enbuild.2025.116539","DOIUrl":"10.1016/j.enbuild.2025.116539","url":null,"abstract":"<div><div>In response to rising urban temperatures and growing energy demands, green walls have gained attention as passive solutions that enhance building performance and urban sustainability. However, the detailed design of green walls and their integration into building simulations remains a methodological challenge due to the complex thermal and moisture dynamics they involve. This study aims to address this gap by developing and validating a simplified, dynamic thermal model based on a grey-box approach using electrothermal analogy. The objective is to provide a practical and scalable tool for assessing the thermal performance of green façades during summer conditions. The model was developed in MATLAB/Simulink and validated using field data recorded on an experimental prototype of green façades installed on a case study building in the north of Rome, Italy. Simulations were then conducted across three summer days every three years (2019, 2021, 2022) and five different wall assemblies, comparing configurations with and without the green façades. Results show that the green wall reduces thermal wave amplitude by 24–39 % and incoming heat flux by 35–48 %, with indoor temperature reductions reaching up to 5.2 °C. The effect on phase shift instead was minimal, increasing delay by no more than 1.5 h. The model also performed well across wall types, with greater impact observed on lightweight and non-insulated assemblies. This validated tool, in addition to its derived coefficients (AF*, Kv, t*), can support designers and policymakers during early-stage decisions, encouraging broader adoption of green walls as a strategy to enhance building resilience within warm climates.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"349 ","pages":"Article 116539"},"PeriodicalIF":7.1,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145263910","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":"Environmental impact assessment of building envelope insulation standards for decarbonization efforts in a developing country: a case study for Türkiye","authors":"Mehmet Akif Aydın,&nbsp;Gül Koçlar Oral","doi":"10.1016/j.enbuild.2025.116537","DOIUrl":"10.1016/j.enbuild.2025.116537","url":null,"abstract":"<div><div>Aligned with global decarbonization goals, the building sector is a key focus due to its large share of energy use and emissions. Improving building envelope performance is central to passive design and emission reduction. While developed countries advanced building envelope standards, progress in developing economies has been slower. In this context, Türkiye revised its insulation regulation, TS 825, to better align with decarbonization targets. The updated TS 825:2024, enforced April 1, 2025, redefined climatic zones and introduced stricter U-value limits. However, its environmental and economic impacts have not yet been systematically assessed against national targets. This study offers the first systematic comparison of the 2024 and 2013 editions, quantitatively demonstrating their implications for operational and embodied carbon and costs, and contributing to both scientific literature and policy development. Using a reference residential building across climate zones, the study estimates operational carbon through energy simulations, calculates embodied carbon with life cycle assessment, and evaluates energy and construction costs for economic feasibility. Results indicate that TS 825:2024 reduces operational carbon (1.5-5 %) but raises embodied carbon (1.2-2 %) due to higher insulation demands. In cold, very cold, and severe cold regions, carbon payback periods are 8.5, 8.9, and 5.9 years, with investment payback periods of 22.3, 23.1, and 16.3 years. In contrast, hot zones show carbon payback periods of 11.8–17.4 years and investment paybacks up to 45.4 years. While the revised standard achieves better performance in cold climates, its limited impact in warmer zones highlights the need for region-specific measures to improve cost-effectiveness and emission outcomes.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"349 ","pages":"Article 116537"},"PeriodicalIF":7.1,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145263966","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 relationship between outdoor thermal environment, individual thermal exposure, and chest skin temperature: A field study in real-life conditions","authors":"Wenli Jian ,&nbsp;Ruyi Liang ,&nbsp;Yuxin Yao ,&nbsp;Yuyang Zhao ,&nbsp;Yichen Liu ,&nbsp;Anqi Guan ,&nbsp;Jiarui Hu ,&nbsp;Yuting Wang ,&nbsp;Bin Wang ,&nbsp;Jing Wang ,&nbsp;Yuchen Hou ,&nbsp;Yingxin Zhu ,&nbsp;Xiuqing Cui ,&nbsp;Weihong Chen ,&nbsp;Bin Cao","doi":"10.1016/j.enbuild.2025.116544","DOIUrl":"10.1016/j.enbuild.2025.116544","url":null,"abstract":"<div><div>Environmental epidemiology traditionally relies on outdoor parameters to assess individual thermal exposure, despite individuals spending most of their time indoors.<!--> <!-->To quantitatively analyze the relationships among outdoor thermal environments, individual thermal exposure and chest skin temperature, we conducted a field study (n = 99), combining individual thermal monitoring with ERA5-Land reanalysis data.<!--> <!-->Linear regression and mixed-effects models were used to evaluate the environmental and human factors influencing thermal exposure and physiological responses. Participants (average age 53.1 years) spent over 80 % of their time indoors. Outdoor temperature and relative humidity (RH) were positively correlated with individual exposure parameters (<em>r</em> = 0.84 and <em>r</em> = 0.64 for temperature and RH, <em>p</em> &lt; 0.01), but had limited explanatory power, especially in summer. Significant inter-individual differences existed in individual exposure temperature and RH. Age was significantly positively correlated with individual exposure temperature (<em>p</em> = 0.024), and sex had a significant influence on individual exposure RH (<em>p</em> &lt; 0.01). Chest skin temperature also showed high variability relative to outdoor and personal exposure temperatures. These results indicate that relying solely on outdoor thermal environment parameters to characterize individual thermal exposure has limited validity and is insufficient. To enhance the accuracy of thermal exposure assessments, there is a need to integrate individual-level monitoring data and individual-specific characteristics into evaluation frameworks. This study contributes to advancing the understanding of thermal exposure dynamics in the Chinese population, expanding existing datasets, and highlights the indispensable role of indoor thermal environments in environmental health related studies and policy-making aimed at improving residents’ well-being.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"349 ","pages":"Article 116544"},"PeriodicalIF":7.1,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145263909","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":"Cold island effect in air-source heat pump arrays: experimental characterization and analysis of influencing factors","authors":"Zhihao Wan ,&nbsp;Guowei Lv ,&nbsp;Pengxiang Li ,&nbsp;Sujie Liu ,&nbsp;Zhaoying Wang ,&nbsp;Xianwang Fan ,&nbsp;Jiaxuan Pu ,&nbsp;Huan Zhang ,&nbsp;Tianzhuo Huo ,&nbsp;Jakub Jurasz ,&nbsp;Wandong Zheng","doi":"10.1016/j.enbuild.2025.116538","DOIUrl":"10.1016/j.enbuild.2025.116538","url":null,"abstract":"<div><div>To support the low-carbon transformation of heating systems, deploying arrays of efficient and eco-friendly air-source heat pump (ASHP) units represents a promising strategy. However, ambient wind may be insufficient to carry away the discharged cold air when ASHP units are closely arranged. This can lead to cold air recirculation, inducing a cold island effect (CIE) that deteriorates units’ heating performance. Despite its relevance, CIE has not been thoroughly characterized, and the influence of contributing factors remains unquantified. Considering above, the objective of this paper is to investigate the intensity and spatial extent of CIE through a set of field experiments. Using multiple regression analysis, the impact levels of environmental and operational parameters on CIE were determined. The negative effects of CIE were then comprehensively evaluated. Results show that the CIE’s center tended to be located near the center of turned-on units. Compared to the cold island intensity of units (CII-U) that is calculated as the temperature difference between ambient air and the inlet air of individual units, the cold island intensity of array (CII-A), which reflects the temperature difference between ambient air and air within the array, was more appropriate to characterize the CIE across the entire ASHP array. The CII-A increased with rising ambient temperature and turned-on ratio of units, with the ambient temperature exerting a greater influence. However, no significant correlation was observed between ambient humidity and CII-A. An increase in CII-A from 0.14 ℃ to 7.67 ℃ corresponded to an expansion of the CIE’s spatial extent from 0.63 m to 3.22 m. When the CII-U increased from 0 ℃ to 7 ℃, the ASHP unit’s heating capacity was reduced by 72.17 kW, and the COP diminished by 57 %. Additionally, Unit 2 at the center of array had a 27 % lower defrost frequency than Unit 1 at the periphery, which experienced a more severe CIE.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"349 ","pages":"Article 116538"},"PeriodicalIF":7.1,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145219222","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 GIS-Informed deep learning framework to assess the effect of urban morphology on energy demand","authors":"Haolin Wang ,&nbsp;Zhi Wu ,&nbsp;Wei Gu ,&nbsp;Pengxiang Liu ,&nbsp;Qirun Sun ,&nbsp;Wei Wang","doi":"10.1016/j.enbuild.2025.116535","DOIUrl":"10.1016/j.enbuild.2025.116535","url":null,"abstract":"<div><div>Assessing the impact of urban morphology on building energy demand is essential for sustainable city development. Traditional approaches often oversimplify complex urban geographic data or rely on extensive calculations of urban morphological factors (UMFs), leading to significant uncertainties. Advances in geographic information systems (GIS) and deep learning offer solutions to these challenges. As a consequence, this study introduces a GIS-informed deep learning framework, employing high-resolution 3D point cloud data to process UMFs. It outputs 3D vectors representing cooling, heating, and electricity demand via a multi-channel convolutional neural network (CNN). The network classifies building age and function type using satellite imagery and point-of-interest (POI) data, generating twelve-dimensional feature vectors by integrating building orientation coordinates and normal vectors as CNN inputs. Applied to Xinwu District in Wuxi City, the model significantly enhances prediction accuracy over traditional multiple linear regression (MLR) based on morphological parameters across twelve urban forms, achieving an R² improvement exceeding 50%. These results demonstrate capability of proposed model to capture complex nonlinear relationships between detailed morphology and energy efficiency in various urban configurations. This study offers a scalable and reliable tool for designing and planning energy-efficient cities.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"349 ","pages":"Article 116535"},"PeriodicalIF":7.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270027","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":"Urban building age prediction using machine learning and its application to carbon sink estimation","authors":"Peifeng Zhang ,&nbsp;Yudi Fu ,&nbsp;Beibei Jia ,&nbsp;Mohamed Al-Hussein ,&nbsp;Zheng Cheng","doi":"10.1016/j.enbuild.2025.116534","DOIUrl":"10.1016/j.enbuild.2025.116534","url":null,"abstract":"<div><div>Building age is significant for urban planning, energy demand estimation and climate change mitigation. This paper utilized building shape metrics, spatial indicators, and explanatory distance predictors of point of interest (POI) data to predict building age, based on 32,826 sample buildings in Qingdao City. The spatial cross-validation (SP-CV) method was used for the evaluation of predictors’ contribution to building age in Random Forest model and the prediction of building age in Support Vector Regression model. The study analyzed the building age characteristics and estimated the carbon sequestration of concrete buildings. The results showed that the distance to 8 adjacent buildings and the POIs, building height and coordinates were significant predictors of building age. The coefficient of determination (R²) exceeded 0.8, and both the mean absolute error (MAE) and root mean square error (RMSE) were less than one year. More than 57 % of the buildings in Qingdao City were over 30 years old. Low-rise buildings and residential buildings have the highest average ages, at 25.7 and 24.7 years, respectively. The average building age increased with distance from the city and district centers. The total carbon sequestration of concrete buildings was estimated at 1.86 million tons in Qingdao City. These findings are critical for urban-scale building energy estimation, urban risk assessment, and climate change mitigation, and they provide valuable insights for urban planning and low-carbon urban renewal.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"349 ","pages":"Article 116534"},"PeriodicalIF":7.1,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218657","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":"Bridging the gap: can low-carbon city policies reduce carbon inequality? Evidence from China’s pilot program","authors":"Ye Luo,&nbsp;Haifeng Xiao","doi":"10.1016/j.enbuild.2025.116530","DOIUrl":"10.1016/j.enbuild.2025.116530","url":null,"abstract":"<div><div>To advance urban low-carbon transitions, prioritizing carbon emission regulation and enhancing carbon equity is critical. This paper evaluates the impact of China’s Low-Carbon City Pilot (LCCP) policy on carbon inequality (CI) using data from 276 cities (2006–2021). We employ a staggered difference-in-differences design, treating LCCP rollout as a quasi-natural experiment, and conduct robustness checks to validate the results. Our findings show that the LCCP policy significantly reduces CI by 2% in pilot cities compared to non-pilot cities. This reduction is driven by improved energy efficiency, optimized industrial structures, and reduced income inequality. Regionally, the policy exhibits heterogeneous effects, mitigating CI more effectively in non-resource-based, mature-type and regenerative-type resource-based, multi-center structure and eastern cities. Furthermore, the Innovative City Pilot policy markedly strengthened the effect of the LCCP policy in reducing CI, demonstrating a synergistic interaction. In contrast, combining the Carbon Emission Trading Pilot with LCCP policies widens CI. These results highlight the importance of integrating equity concerns with efficiency-oriented climate strategies. Dual-focused policies that balance environmental and social goals can accelerate progress toward China’s “dual carbon” targets.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"349 ","pages":"Article 116530"},"PeriodicalIF":7.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218651","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":"Experimental evaluation of radiant ceiling panels in office building perimeter zones","authors":"Hung Q. Do ,&nbsp;Mark B. Luther ,&nbsp;Jane Matthews ,&nbsp;Igor Martek","doi":"10.1016/j.enbuild.2025.116531","DOIUrl":"10.1016/j.enbuild.2025.116531","url":null,"abstract":"<div><div>Lightweight and modular radiant ceiling panels are attracting increasing interest from researchers, project owners, and designers. However, their thermal performance and capacity to deliver comfort in perimeter office zones remain underexplored. This study evaluates the performance of a prototype lightweight radiant ceiling panel system in a test chamber constructed in Victoria, Australia, to emulate a north-facing perimeter office space. The system, designed for both heating and cooling, was assessed through two cooling test runs and one heating test run. Results show that the panels are highly responsive, thermally effective, and capable of maintaining thermal comfort, particularly under cooling conditions. The system achieved average capacities of 95 W/m² in cooling and 85 W/m² in heating. In cooling mode, it successfully countered perimeter solar heat gains while maintaining appropriate comfort levels. Heating, though viable, responded more slowly and produced significant vertical air temperature gradients, potentially causing local discomfort. To mitigate this, supplementary mechanical ventilation is recommended. While broadly consistent with prior research, the study highlights the need for at least three test runs in both heating and cooling to improve generalizability. Future experiments with an upgraded heat pump and improved storage tank are planned to validate and extend these findings.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"349 ","pages":"Article 116531"},"PeriodicalIF":7.1,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145229011","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 direct adaptive control architecture for buildings thermal comfort and energy efficiency optimization using multilayer perceptron neural networks and model reference learning","authors":"Ahmed Ouaret ,&nbsp;Soundouss Ismahane Talantikite ,&nbsp;Hocine Lehouche ,&nbsp;Hervé Guéguen ,&nbsp;Youcef Belkhier ,&nbsp;Mohamed Benbouzid","doi":"10.1016/j.enbuild.2025.116528","DOIUrl":"10.1016/j.enbuild.2025.116528","url":null,"abstract":"<div><div>This paper presents an original direct adaptive control strategy applied to a building heating system, using an intelligent control approach based on Multi-Layer Perceptron (MLP) neural networks. The main contribution of this work is the integration of a newly developed climate database, representing the coldest regions of Algeria, into the Simbad simulation environment. This enhancement significantly increases the realism and robustness of the simulation, allowing for a more accurate assessment of control performance under extreme and challenging climate conditions. The proposed control architecture follows a model reference structure in which an MLP-based adaptive controller ensures real-time adjustment to varying thermal demands and external disturbances. Unlike conventional fixed-parameter controllers, the system learns and adapts online to maintain thermal comfort while optimizing energy usage. Simulation results demonstrate the effectiveness of the proposed method in achieving precise temperature regulation and substantial energy savings. This study highlights the importance of coupling intelligent control techniques with realistic environmental data to develop energy-efficient solutions tailored to diverse climatic contexts.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"349 ","pages":"Article 116528"},"PeriodicalIF":7.1,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145218654","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":"Quantifying the cooling demand of Manhattan using aerial images","authors":"Florian Barth ,&nbsp;Kathrin Menberg ,&nbsp;Matthias Sulzer ,&nbsp;Philipp Blum","doi":"10.1016/j.enbuild.2025.116526","DOIUrl":"10.1016/j.enbuild.2025.116526","url":null,"abstract":"<div><div>Cooling demand accounts for a significant share of worldwide electricity consumption, with space cooling being responsible for about 10 % of global electricity consumption, and process cooling making up for 4 % of electricity consumption in the European Union. To implement more efficient district-scale solutions for coupled heating, cooling and thermal storage, spatial information on cooling demand is essential. In this study, a method to quantify installed cooling capacities and cooling demands on building, district and city scale is presented and applied to Manhattan, New York City (NYC). Therefore, heat rejection units of cooling systems are detected in aerial images using deep learning. The cooling capacity of the systems is quantified using a regression model based on visual characteristics of the heat rejection units. The annual operating time is estimated based on building type to calculate the cooling demand. Furthermore, the cooling-related electricity consumption is quantified using energy efficiency ratios and through Monte Carlo simulation to assess the uncertainties of all parameters on different scales. In Manhattan, a total installed cooling capacity of 10.6 ± 0.2<!--> <!-->GW, a cooling demand of 10.0 ± 0.2<!--> <!-->TWh/a and a cooling-related electricity consumption of 2.82 ± 0.05<!--> <!-->TWh/a is quantified. Most of the cooling demand is concentrated in Midtown and the Financial District, with the largest cold consumers being public buildings such as universities and hospitals. Midtown and the Financial District’s cooling demands are 171 % and 175 % of the estimated heating demands, respectively. This indicates a high potential for coupled heating and cooling, for example, in the form of seasonal thermal energy storage, especially in and around the named areas in Manhattan.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"349 ","pages":"Article 116526"},"PeriodicalIF":7.1,"publicationDate":"2025-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145263963","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}