Energy and Buildings最新文献

{"title":"Advanced building utility systems: Utilizing a thermal wheel and a photovoltaic/thermal system equipped with a nanofluid/nano-enhanced PCM-based spectral splitter","authors":"Amin Shahsavar ,&nbsp;Jaber Aboueian ,&nbsp;Hamid Reza Askarifard Jahromi","doi":"10.1016/j.enbuild.2025.115669","DOIUrl":"10.1016/j.enbuild.2025.115669","url":null,"abstract":"<div><div>The aim of this research is to evaluate the effectiveness of a combined system, consisting of a photovoltaic/thermal system and a thermal wheel, in supplying hot air, cold air, electricity, and hot water for a building. To enhance the performance of the photovoltaic/thermal system, a spectral splitter containing a nanofluid and a nano-enhanced phase change material is implemented. During the cold months, cold outside air is first heated by passing it through the thermal wheel and then through the photovoltaic/thermal system. In the hot months, cooling of the outside hot air is achieved by transferring heat to the air exiting the building. Throughout the year, the electricity generated by the photovoltaic system is used to power the building, while the heated nanofluid in the splitter provides hot water through a heat exchanger. The study examines the impact of the length of photovoltaic/thermal system, thickness of nanofluid, phase change material, and air layers, the mass flow rate of nanofluid and air flow, the concentration ratio, and the diameter and length of thermal wheel on the annual average functionality of the system form energy and exergy viewpoints.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115669"},"PeriodicalIF":6.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760405","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":"Research on low-carbon district heating system planning approach based on thermal energy grade and integrated demand response","authors":"Quanyi Lin ,&nbsp;Shilei Lu ,&nbsp;Lu Yue ,&nbsp;Boyang Li ,&nbsp;Hongcheng Zhu","doi":"10.1016/j.enbuild.2025.115660","DOIUrl":"10.1016/j.enbuild.2025.115660","url":null,"abstract":"<div><div>Low-carbon district heating systems are the result of the improvement of existing fossil-based district heating systems, where various environmentally friendly heat sources are integrated to decarbonize the heating sector. Existing research has the problem of emphasizing “quantity” over “quality” in the planning stage of the district heating system and does not consider the load elasticity of the demand side, resulting in redundancy in the equipment capacity. Therefore, a source–load–storage collaborative planning approach for a low-carbon district heating system is proposed, which comprehensively considers the grade equivalence of thermal energy supply and demand, and integrated electricity-heat demand response to achieve the optimal system configuration. Additionally, we establish a refined mathematical model of latent heat thermal energy storage based on engineering-measured data to address the issue that a general energy storage planning model cannot accurately describe the differentiated charging–discharging characteristics of multi-energy storage systems. A case study is considered, and the results are thoroughly analyzed. Compared to the conventional planning method, the proposed method can effectively reduce the capacity of system equipment by up to 35.0% and reduce the annual investment cost by approximately 5.31%. The outcomes can be referred to for district heating system planning decisions.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115660"},"PeriodicalIF":6.6,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760404","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":"Selection and integration strategies of PCMs in traditional bricks for thermal comfort and energy efficiency: A comprehensive review","authors":"N. Ruiz-Marín","doi":"10.1016/j.enbuild.2025.115663","DOIUrl":"10.1016/j.enbuild.2025.115663","url":null,"abstract":"<div><div>Traditional bricks, while being cost-effective and durable construction materials, exhibit limited thermal performance under extreme weather conditions, which can lead to uncomfortable indoor environments and increased energy consumption for heating and cooling. To address this issue, the integration of phase change materials (PCMs) into bricks has emerged as an area of interest in sustainable construction. PCMs have the ability to store and release large amounts of latent heat during phase transitions, acting as a thermal buffer that regulates indoor temperature. However, a comprehensive review on how PCM integration impacts thermal performance, energy savings, environmental effects, and costs—particularly in bricks—has not yet been published. This review examines strategies for the selection and integration of PCMs in traditional bricks, analyzing the different types of PCMs, their properties, and the most common integration techniques. The influence of brick design on thermal performance is discussed, along with the importance of building orientation to maximize system efficiency. Several studies demonstrate that the integration of PCMs into bricks significantly reduces indoor temperature fluctuations, heat flow, and cooling demand, thereby improving thermal comfort and energy efficiency in buildings. However, challenges remain, such as improving thermal conductivity, reducing costs, and ensuring the safety of PCMs. Future research is needed to optimize integration techniques, develop PCMs with enhanced properties, and establish safety guidelines, paving the way for the widespread adoption of this technology in the construction of more sustainable buildings.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115663"},"PeriodicalIF":6.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739821","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":"Outdoor thermal benchmarks for the elderly and its application to the outdoor spaces design of nursing homes in Shanghai","authors":"Pengfei Zhou ,&nbsp;Dexuan Song ,&nbsp;Chi Zhang","doi":"10.1016/j.enbuild.2025.115654","DOIUrl":"10.1016/j.enbuild.2025.115654","url":null,"abstract":"<div><div>With China’s increasing aging, many older people in cities choose to live in nursing homes.<!--> <!-->However, previous research on nursing homes has concentrated mainly on the study of elderly-friendly design and indoor thermal comfort, paying little attention to the elderly’s thermal comfort in outdoor spaces.<!--> <!-->This study investigated older people’s thermal perceptions in six typical outdoor spaces at Q nursing home in Shanghai, aiming to establish a healthy and comfortable outdoor thermal environment for older people in nursing homes. The investigation utilized physical measurements, physiological<!--> <!-->measurements, and questionnaires to explore the correlations between outdoor thermal perceptions, thermal environment factors, and space enclosing degree. The Physiological Equivalent Temperature (PET) was chosen to assess the elderly’s outdoor thermal benchmarks in Shanghai. Based on the thermal benchmarks, optimal design strategies for nursing homes’ outdoor thermal environments were proposed.<!--> <!-->Results<!--> <!-->demonstrated that: 1) The elderly’s outdoor thermal sensation and thermal comfort were closely related. Air temperature and wind speed are the major influences on the elderly’s thermal perceptions. 2) The neutral PET, neutral PET range, acceptable PET range, and preferred PET of the elderly in Shanghai were identified as 18.3℃, 11.24 °C-25.26℃, 9.48 °C-28.4℃<!--> <!-->and 20.9℃, respectively.<!--> <!-->3) The elderly’s outdoor thermal sensation and the mean skin temperature are closely correlated with the building space’s enclosing degree and opening direction.<!--> <!-->Semi-open spaces are the most comfortable, and open spaces have the lowest comfort. 4) Optimum design strategies<!--> <!-->were proposed based on the natural and built environments, and the results provide design strategies for the<!--> <!-->outdoor thermal environments of nursing homes in regions of hot<!--> <!-->summer and cold<!--> <!-->winter.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115654"},"PeriodicalIF":6.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739826","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":"In-depth sensitivity analysis of heating demand and overheating in Dutch terraced houses using interpretable machine learning","authors":"Alexis Cvetkov-Iliev ,&nbsp;Vasilis Soulios ,&nbsp;Luyi Xu ,&nbsp;Günsu Merin Abbas ,&nbsp;Evangelos Kyrou ,&nbsp;Lisanne Havinga ,&nbsp;Pieter Jan Hoes ,&nbsp;Roel Loonen ,&nbsp;Joaquin Vanschoren","doi":"10.1016/j.enbuild.2025.115611","DOIUrl":"10.1016/j.enbuild.2025.115611","url":null,"abstract":"<div><div>Sensitivity analyses are often performed to facilitate the design or modeling of complex building systems by focusing on the most influential parameters. However, the insights they produce are generally limited to a ranking of the parameters’ impact. Instead, many applications could benefit from more advanced insights, such as how these impacts vary across different parameter values and scenarios. In addition, sensitivity analysis results should be easily interpretable to facilitate subsequent decision making. With these goals in mind, this paper introduces a novel sensitivity analysis method based on partial dependence (PD) plots. PD plots are further combined with dictionary learning, advanced visualizations, and surrogate models to facilitate their analysis and reduce their computational cost. Using this method, the effect of 26 parameters on heating demand and overheating in Dutch terraced houses is investigated. Two surrogate models are trained on 66,000 EnergyPlus simulations to predict the annual heating demand and percentage of overheating hours with excellent precision (<span><math><mrow><mo>≈</mo><mn>3</mn></mrow></math></span>–4 % of percentage error). The benefits of our approach are demonstrated through 3 use cases: 1) a comparison of the impact and energy-overheating trade-off of insulation measures across various scenarios, 2) improving the design of parametric simulations by eliminating redundant parameter values, and 3) uncovering complex behaviors in simulation or surrogate models, to build trust in them and diagnose potential modeling or training errors. Finally, our results suggest that surrogate models can be trained on much less data (1000–3000) without compromising sensitivity analysis results.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115611"},"PeriodicalIF":6.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explaining building energy efficiency prediction through architectural and engineering solutions considering environmental impacts using a hybrid model","authors":"Semi Emrah Aslay","doi":"10.1016/j.enbuild.2025.115614","DOIUrl":"10.1016/j.enbuild.2025.115614","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The aim of this study is to investigate building energy efficiency by integrating hybrid modelling approaches and interpretable models into architectural design processes and engineering solutions while considering environmental impacts. At the same time, it is aimed to achieve the most efficient building design possible. Separate analyses were carried out using a total of 6,913 data from Rutland (185) and Salford (6718) cities. The data were grouped into carbon emission information, architectural information, lighting information, personal heating information, and main heating system information to form a dataset. Light Gradient Boosting Machine (LightGBM) was preferred as the base model and Particle Swarm Optimisation (PSO) method was applied for hyperparameter optimisation. The hybrid model created in this way is called PSO-LightGBM. The optimization process was carried out using software in both R Studio and Python environments, utilizing seven different hyperparameters. Apart from the hybrid model used as a method, 2 different SHAP analyses, neural network based, and tree based, were performed to clearly explain the parameter relationships. The PSO-LightGBM hybrid model provided more successful predictions compared to the basic LightGBM model. While R&lt;sup&gt;2&lt;/sup&gt; values improved between 0.82 and 0.90 in the Rutland dataset, this value increased from 0.8687 to 0.8901 for test data and from 0.8538 to 0.9091 for training data in the Salford dataset. R&lt;sup&gt;2&lt;/sup&gt; values show an improvement of 7% in the Rutland dataset and maximum 6% in the Salford dataset. When the reduction in error rates is evaluated, it is found that the greatest improvement is in the Mean Squared Error (MSE) metric. MSE decreased by 17% in the Rutland dataset and by 4% in the Salford dataset. According to the SHAP Analysis results, CO&lt;sub&gt;2&lt;/sub&gt; emissions have the largest impact on energy consumption, while primary fuel types, number heated rooms and individual heating systems are other important parameters. While the tree-based SHAP model is more sensitive to physical parameters, the neural network model is more sensitive to indirect relationships. In both analyses, the communal heating system type has the lowest impact. In order to improve building energy efficiency, high efficiency individual boiler systems should be preferred, architectural approaches that optimise the number of heated rooms and smart heating solutions should be used, and central heating systems should be modernised. The results highlight the effectiveness of hybrid modelling approaches with SHAP analyses based on different baselines to ensure the integration of environmental impacts, architectural design processes and engineering solutions in terms of building energy efficiency. Furthermore, the findings contribute to the importance of interdisciplinary work in buildings to improve energy efficiency. Future studies can focus on the development of building energy performance prediction models","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115614"},"PeriodicalIF":6.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714516","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":"On-Site temperature and irradiance forecast tuning for improved load prediction in buildings","authors":"Amine Jarraya ,&nbsp;Tim Diller ,&nbsp;Himanshu Nagpal ,&nbsp;Anton Soppelsa ,&nbsp;Federico Trentin ,&nbsp;Gregor Henze ,&nbsp;Roberto Fedrizzi","doi":"10.1016/j.enbuild.2025.115642","DOIUrl":"10.1016/j.enbuild.2025.115642","url":null,"abstract":"<div><div>Building management systems (BMSs) with predictive control strategies rely on accurate weather forecasts to optimise heating and cooling operations. These strategies depend on precise climatic inputs to adjust system operations dynamically. Typically, weather forecast data is sourced from the internet and is generated by numerical weather prediction (NWP) models using advanced mathematical simulations. However, these models fail to account for localised nano-climatic variations, such as significant temperature and irradiance differences between the north and south sides of a building or the actual environmental conditions around the on-site sensors. These nano-climatic effects directly influence the calculation of the future thermal load of the building, which is crucial for predictive control approaches. To address this challenge, we propose a hybrid methodology that integrates NWP forecasts with local measurements from on-site sensors, improving NWP forecast accuracy. Our approach employs Inverse Distance Weighting (IDW) to interpolate NWP outputs to a specific geographical position and applies exponential smoothing for further finetuning by using historical error patterns. This methodology enhances the predictive accuracy of temperature and irradiance forecasts, achieving reductions of up to 60% to 80% in temperature errors and up to 20% to 30% in irradiance errors. Based on the finetuned weather forecast, the accuracy of building’s thermal load prediction is improved up to 86% compared to the predictions with IDW weather forecast.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115642"},"PeriodicalIF":6.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is it necessary to calibrate existing decision-making results based on real option analysis during the process of climate change? A case study from Xiamen, China","authors":"Wenqiang Li ,&nbsp;Xin Jin ,&nbsp;Pei Peng ,&nbsp;Zaiyi Liao ,&nbsp;Min Wu ,&nbsp;Huahua Xu ,&nbsp;Jiashun Feng","doi":"10.1016/j.enbuild.2025.115658","DOIUrl":"10.1016/j.enbuild.2025.115658","url":null,"abstract":"<div><div>Previous studies have rarely examined how well Representative Concentration Pathways (RCP) scenarios fit with real weather data making climate change related decision-makings. This paper proposes a real option analysis (ROA) decision-making calibration method based on RCP (learning) scenarios uncertainty caused by the gap between the simulated and real weather conditions. Firstly, EnergyPlus is used to predict the energy consumption of a case building based on future climatic conditions, and the learning scenarios uncertainty is quantified. Secondly, a ROA decision-making calibration model (ROACM) is established through optimization to minimize the learning scenarios uncertainty. Finally, the ROACM is applied to a case study of an office building located in Xiamen, China, aiming to calibrate individual and sequential investment decisions for three retrofitting measures (<span><math><msub><mi>R</mi><mn>1</mn></msub></math></span>: horizontal shading, <span><math><msub><mi>R</mi><mn>2</mn></msub></math></span>: low-e windows, <span><math><msub><mi>R</mi><mn>3</mn></msub></math></span>: improving COP of chiller). The accuracy of ROACM is assessed through a sensitivity analysis. The results indicate that the learning scenarios uncertainty leads to annual energy consumption differences of approximately 8,451 kW <span><math><mo>∙</mo></math></span>h to 17,545 kW<span><math><mo>∙</mo></math></span>h in the case study. After calibration, the optimal timing for both individual and sequential investment has advanced by at least 4 years, significantly boosting returns. The optimal individual investment <span><math><msub><mi>R</mi><mn>3</mn></msub></math></span> generated an additional return of up to $52,744, while the sequential investment <span><math><mrow><msub><mi>R</mi><mn>1</mn></msub><mo>+</mo><msub><mi>R</mi><mn>13</mn></msub></mrow></math></span> (shading first, and improving COP of chiller later), increased by $175,017. The proposed ROACM demonstrated high performance during model validation in a case study, and could also aid governments or investors in ROA-based mitigation strategies analysis and increasing the reliability of the results.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115658"},"PeriodicalIF":6.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739822","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":"Influence of social and economic aspects on end-use energy consumption in Chinese urban households","authors":"Tian Wang ,&nbsp;Qinfeng Zhao ,&nbsp;Weijun Gao ,&nbsp;Jialu Dai ,&nbsp;Mengyuan Zhou ,&nbsp;Yi Yu","doi":"10.1016/j.enbuild.2025.115645","DOIUrl":"10.1016/j.enbuild.2025.115645","url":null,"abstract":"<div><div>Reducing household energy consumption is essential for China’s implementation of its “dual-carbon” commitment. This study established an accounting framework to analyze urban Household End-use Energy Consumption (HEEC) across 30 Chinese provinces. The framework links five types of household activities to five energy sources through appliance functionality. The Logarithmic Mean Divisia Index (LMDI) method was used to quantify the social and economic factors affecting HEEC. The results show that HEEC increased by approximately 30% from 2010 to 2019, with provincial disparities decreasing. Kitchen/hot water and heating were the primary drivers of this growth, contributing 38.5% and 33.8%, respectively. Meanwhile, energy consumption for cooling and power grew rapidly at annual rates of 5.6% and 4.8%, respectively. Decomposition analysis revealed that income growth and urban expansion were major drivers of HEEC growth. The rise in single-person households, which reduces economies of scale in household activities, has also increased HEEC. However, energy consumption intensity and behavioral habits played key roles in curbing the HEEC growth. These insights underline the significant impact of social and economic development on HEEC and underscore the need for continued appliance upgrades and energy reforms in China.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115645"},"PeriodicalIF":6.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725343","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":"Unsupervised domain adaptation for HVAC fault diagnosis using contrastive adaptation network","authors":"Naghmeh Ghalamsiah ,&nbsp;Jin Wen ,&nbsp;K.Selcuk Candan ,&nbsp;Teresa Wu ,&nbsp;Zheng O’Neill ,&nbsp;Asra Aghaei","doi":"10.1016/j.enbuild.2025.115659","DOIUrl":"10.1016/j.enbuild.2025.115659","url":null,"abstract":"<div><div>Data-driven methods have shown great promise for heating, ventilation, and air conditioning (HVAC) systems’ fault diagnosis, but their reliance on well-labeled datasets poses challenges in real-world applications where such data may not be readily available. Meanwhile, well-labeled data might exist from virtual testbeds or laboratory systems. Domain adaptation could provide a solution to utilize labeled data from a source domain (such as a virtual or laboratory testbed) to diagnose faults in an unlabeled target domain, such as faults in a real building system. This paper utilizes the contrastive adaptation network (CAN) algorithm, originally successful in image classification, to overcome the specific challenges faced by current domain adaptation algorithms in HVAC systems. Furthermore, temporal causal discovery framework (TCDF), a causality-based framework for discovering causal relationships in time series data, is implemented in the data processing step to meet the requirements of convolutional networks, where spatially closer features are more likely to be correlated. The results on air handling unit (AHU) datasets demonstrate that the CAN algorithm effectively facilitates domain adaptation in the absence of target labels and that the feature reordering process reduces the training time and the number of loops required for convergence.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115659"},"PeriodicalIF":6.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714513","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}