Energy and Buildings最新文献

{"title":"Flexibility-centric sizing and optimal operation of building-thermal energy storage systems: A systematic modelling, optimization and validation approach","authors":"Yangzhe Chen ,&nbsp;Thomas Ohlson Timoudas ,&nbsp;Qian Wang","doi":"10.1016/j.enbuild.2025.115722","DOIUrl":"10.1016/j.enbuild.2025.115722","url":null,"abstract":"<div><div>The increasing integration of renewable energy sources (RES) and the transition towards a decarbonized energy sector present significant challenges, particularly in demand-side management. Thermal energy storage (TES) systems offer a cost-effective solution for enhancing energy flexibility in building heating systems. However, improper sizing and operation of TES systems can lead to increased investment costs and energy losses. To bridge this gap, this study proposes a novel, optimization-based framework for the systematic sizing and operation of TES systems. The methodology encompasses two key components: (1) an innovative TES sizing framework that integrates system modelling and optimization-based sizing leveraging historical thermal load data; (2) validation and performance evaluation of the sizing outputs through building energy simulations across three diverse building types and climatic conditions. Key findings demonstrate the framework’s ability to adapt to various scenarios, achieving operational cost reductions of up to 35 % and significantly enhancing the energy flexibility in terms of flexibility factor by up to 1.03. Furthermore, the proposed framework is shown to effectively optimize TES capacities to unique building load patterns. These results highlight the framework’s potential as a robust tool for optimizing TES in buildings, contributing to flexible and cost-efficient energy systems.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"338 ","pages":"Article 115722"},"PeriodicalIF":6.6,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821230","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 investigation of radiative cooling protective coatings for building materials","authors":"Lanxin Wang ,&nbsp;Yuncheng Wang ,&nbsp;Yingshuo Li ,&nbsp;Zhiyong Liu ,&nbsp;Jinyang Jiang","doi":"10.1016/j.enbuild.2025.115718","DOIUrl":"10.1016/j.enbuild.2025.115718","url":null,"abstract":"<div><div>The application of radiative cooling protective coatings on building roofs and exterior walls represents a new strategy for energy saving and emission reduction. This approach not only decreases reliance on air conditioning by improving thermal management but also enhances the durability of concrete structures, contributing to reduced carbon emissions in the cement production process. This study proposed a straightforward and facile method for fabricating radiative cooling coatings by incorporating vacuum ceramic microspheres (VCM) modified with polycatecholamine (PCA) into polyurethane (PU) matrix. VCM@PCA significantly enhanced the mechanical properties and thermal stability of PU composite coating, reduced the thermal conductivity, and simultaneously increased its infrared emissivity. The radiative cooling performance of this coating was systematically evaluated. In the xenon lamp radiation experiments, the PU/[email protected] coating demonstrated a remarkable cooling performance, with its bottom surface temperature showing a reduction of 17.1 °C compared to the ambient temperature. Infrared thermography further confirmed that this coating consistently exhibited the lowest temperature among the tested samples. In outdoor experiments, the model house coated with PU/[email protected] exhibited a maximum temperature reduction of 8.8 °C compared to the external ambient temperature. Additionally, this new kind of coating demonstrated excellent hydrophobicity (130.3°) and excellent resistance to chloride ion penetration (the rapid chloride ion migration (RCM) value of the cement specimen was reduced to 1.75 × 10^-12 m²/s). These findings underscored the potential of PU/VCM@PCA as a promising coating material for improving energy saving efficiency and durability in building materials, thereby paving the way for innovative applications in sustainable construction.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"338 ","pages":"Article 115718"},"PeriodicalIF":6.6,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823679","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":"Deploying advanced supervisory control strategies to small- and medium-sized commercial buildings: Case study and lessons learned","authors":"Mingyue Guo ,&nbsp;Boming Liu ,&nbsp;Sen Huang ,&nbsp;Jin Dong ,&nbsp;Jianming Lian ,&nbsp;Zheng O’Neill","doi":"10.1016/j.enbuild.2025.115710","DOIUrl":"10.1016/j.enbuild.2025.115710","url":null,"abstract":"<div><div>Deploying advanced supervisory control strategies (ASCSs) in small- and medium-sized commercial buildings (SMBs) is vital but faces two issues: (1) a lack of building control and communication infrastructure (BCCI) in SMBs and (2) the significant engineering efforts required to implement and configurate ASCSs. Despite hindering the large-scale adoption of ASCSs in SMBs, these issues have not been adequately explored in the literature, which tends to focus more on feasibility than scalability. This paper provides a comprehensive evaluation of these two issues through a case study of an occupied SMB in eastern Tennessee of the United States. Specifically, we design and implement a BCCI for the studied building with commercial off-the-shelf products to accommodate the needs for deploying ASCSs. We then deploy two ASCSs—a rule-based setback control and a model predictive control (MPC)—with the BCCI and evaluate their performance throughout the summer of 2024. This study reveals that the main bottlenecks in establishing BCCIs for SMBs are the high initial cost (∼$56/m²) and communication delays (up to 9 min). Additionally, the assessment of the two ASCSs indicates that the majority (∼75 %) of the engineering effort required for implementation and configuration is spent on model identification, debugging, and tuning.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"338 ","pages":"Article 115710"},"PeriodicalIF":6.6,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143850566","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":"Assessing energy efficiency in the built environment: A quantile regression analysis of CO2 emissions from buildings and manufacturing sector","authors":"Ashar Awan ,&nbsp;Mustafa Kocoglu ,&nbsp;Mohammad Subhan ,&nbsp;Kansulu Utepkaliyeva ,&nbsp;Nora Yusma bte Mohamed Yusoff ,&nbsp;Md. Emran Hossain","doi":"10.1016/j.enbuild.2025.115733","DOIUrl":"10.1016/j.enbuild.2025.115733","url":null,"abstract":"<div><div>Achieving sustainable development has emerged as a global emergency in response to environmental challenges, including the need to mitigate carbon dioxide (CO₂) emissions. The built environment, encompassing both buildings and manufacturing activities, is critical to achieving net zero, given its contribution of approximately 37% to global energy-related CO₂ emissions. While earlier research explored CO₂ emissions broadly, we lack a clear picture of how built environments respond to various environmental and economic factors, especially as economies modernize their infrastructure and cities. This research provides insights into the sustainable transformation of the built environment by analyzing the impact of energy efficiency, urbanization, industrial development, and non-renewable energy on building and manufacturing-based CO₂ emissions in newly industrialized countries during 1990–2020. To this end, the study employed the Method of Moments Quantile Regression (MMQR) that enables analysis of heterogeneous effects across the conditional distribution of emissions, extending beyond conventional mean-based estimations. The findings from the MMQR show that improvement in energy efficiency mitigates CO₂ emissions from the built environment. Specifically, a 1% improvement in energy efficiency results in a reduction in CO₂ emissions ranging from 0.913% to 0.835% across all quantiles. The “Environmental Kuznets Curve (EKC)” holds for built environment-based CO₂ emissions in sample countries. The results indicate that non-renewable energy consumption and industrialization significantly increase emissions from the built environment. Policy efforts should prioritize renewable energy sources, energy-efficient designs, and a balanced growth path to steer the building sector toward a lower carbon footprint for long-term environmental sustainability<em>.</em></div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"338 ","pages":"Article 115733"},"PeriodicalIF":6.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829490","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":"Evaluating urban form influence on solar exposure and corresponding building energy demands","authors":"Marwa Alfouly,&nbsp;Smajil Halilovic,&nbsp;Thomas Hamacher","doi":"10.1016/j.enbuild.2025.115708","DOIUrl":"10.1016/j.enbuild.2025.115708","url":null,"abstract":"<div><div>Solar radiation is a key factor in the energy balance of urban areas, with urban form significantly affecting solar exposure. The design and arrangement of buildings determine how they interact with solar radiation, influencing factors such as the performance of building-integrated photovoltaic systems, solar heat gains, and heating and cooling demands. Urban form indicators, including orientation, roof slope, and shading from surrounding structures, are crucial for determining solar exposure, which has significant implications for optimizing energy efficiency and guiding sustainable urban planning decisions. This paper presents a new Python-based framework that analyzes urban geometry to enable shadow analysis and the calculation of solar radiation, solar heat gain, and the heating/cooling load for any selected building(s) given 3D models. The framework is developed using CityJSON, facilitating the processing of 3D city models within the Python environment. Its performance is demonstrated through a case study of a representative building in the city of Munich. The results show that it effectively evaluates the thermal behavior of buildings in response to solar radiation and analyzes how the urban form influences solar shading. This provides potential methods for optimizing urban form and improving the energy systems of buildings. This framework is the first to use CityJSON in a unified environment to jointly analyze publicly available 3D building models, incorporate windows, and perform solar radiation and shadow analysis, as well as heating and cooling demand calculations.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"338 ","pages":"Article 115708"},"PeriodicalIF":6.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143829492","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":"Predictive study of elderly adaptive behaviour under climate change and implications on future design of residential buildings in Hong Kong","authors":"Yilin Lee ,&nbsp;Kai Gao ,&nbsp;Edward Ng","doi":"10.1016/j.enbuild.2025.115730","DOIUrl":"10.1016/j.enbuild.2025.115730","url":null,"abstract":"<div><div>This study is motivated by the need to improve building design to cope with future climatic conditions and changing occupant lifestyles. The research objectives are to forecast changing lifestyle patterns in response to climate change, particularly given the absence of future behavioural data, and to provide a set of building design recommendations based on predicted behavioural changes and future climate scenarios. The study investigates adaptive behavioural patterns under extreme heat conditions and their implications for future residential building design in Hong Kong, analysing data from 348 elderly participants during intense heat periods in 2021–2022. Using this data as a proxy for future climate scenarios, the research employs two predictive models: an Ordinary Logistic Regression Model (OLRM) to examine relationships between adaptive behaviours and thermal comfort, and a Binary Logistic Regression Model (BLRM) to analyse thermal acceptability of outdoor temperatures. Results reveal significant associations between living conditions and thermal comfort: participants with larger living area per person (b = 0.04, p = 0.035) and those in village houses (b = 1.402, p = 0.041) reported higher thermal comfort levels compared to housing estate residents. Conversely, tenement building residents were 22 times more likely to experience thermal unacceptability (b = -3.111, p = 0.018). Analysis of architectural typologies indicates that these outcomes correlate with ventilation potential, presence of outdoor spaces, and urban heat island effects. The study predicts increased frequency of specific adaptive behaviours under future climate scenarios, particularly window operation, outdoor space utilization, and movement between indoor areas. Based on these findings, the research proposes key recommendations for future residential design: maintaining adequate per-person living area, avoiding studio layouts, incorporating balconies or verandas, maximizing natural ventilation through strategic façade design, and providing designated cooling zones. These insights contribute to climate-adaptive architectural design strategies for high-density urban environments facing increasing thermal stress.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"338 ","pages":"Article 115730"},"PeriodicalIF":6.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844423","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":"Learning from other cities: Transfer learning based multimodal residential energy prediction for cities with limited existing data","authors":"Yulan Sheng ,&nbsp;Hadi Arbabi ,&nbsp;Wil Oc Ward ,&nbsp;Martin Mayfield","doi":"10.1016/j.enbuild.2025.115723","DOIUrl":"10.1016/j.enbuild.2025.115723","url":null,"abstract":"<div><div>Reliable prediction of residential energy consumption is essential for informing energy efficiency policies and retrofit strategies. However, traditional data-driven approaches are often constrained by the availability and quality of data. This study presents a novel approach combining multimodal neural networks with a transfer learning framework, leveraging both tabular and visual data to enhance prediction accuracy and enable knowledge transfer from data-rich to data-poor regions. Case studies conducted in Barnsley, Doncaster, and Merthyr Tydfil demonstrated that the proposed approach outperforms traditional mono-modal models. The multimodal model improved prediction accuracy significantly, achieving a MAPE reduction from 1.15 (with only visual data) and 0.86 (with only tabular data) to 0.43 (with both visual and tabular data), while the inclusion of transfer learning offers further performance improvements in data-scarce regions, with up to 63.6 % error reduction. Explainable AI is utilised to validate the model’s interpretability, confirming key features such as floor and wall insulation conditions as pivotal in energy consumption predictions. This integrated framework offers actionable insights for policymakers, facilitating data-driven decisions to enhance energy efficiency in diverse urban settings.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"338 ","pages":"Article 115723"},"PeriodicalIF":6.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821229","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":"Enhancing grid stability with machine learning: A smart predictive approach to residential energy management","authors":"Mattew A. Olawumi ,&nbsp;B.I. Oladapo","doi":"10.1016/j.enbuild.2025.115729","DOIUrl":"10.1016/j.enbuild.2025.115729","url":null,"abstract":"<div><div>This research focuses on enhancing energy efficiency and grid stability in residential buildings by developing and evaluating advanced demand response (DR) strategies, explicitly comparing a Rule-Based model with a Predictive model leveraging machine learning. The Predictive Model utilised a neural network with ReLU activation functions, optimised using grid search and cross-validation, and incorporated real-time data from smart meters and environmental sensors. Evaluation metrics demonstrated that the Predictive Model outperformed the Rule-Based Model, achieving a 15% reduction in electricity costs, a 20% improvement in energy efficiency, and a 15% reduction in peak load demands while maintaining a high predictive accuracy of 0.95%. However, these benefits came with increased computational complexity and resource requirements. The Rule-Based Model, while more straightforward and less resource-intensive, was less effective in dynamic environments. This study underscores the potential of integrating machine learning with real-time data for optimising residential energy management, offering significant cost savings and contributing to sustainable energy practices. The findings suggest that, despite higher computational demands, the Predictive Model provides superior adaptability and accuracy, making it a valuable tool for future smart grid applications.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"338 ","pages":"Article 115729"},"PeriodicalIF":6.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816064","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":"Impact of outdoor air temperature during hostel-classroom commute on student’s classroom performance","authors":"Shashikant Das ,&nbsp;Asit Kumar Mishra ,&nbsp;Sudhakar Subudhi","doi":"10.1016/j.enbuild.2025.115716","DOIUrl":"10.1016/j.enbuild.2025.115716","url":null,"abstract":"<div><div>This study investigated the performance of students attending class, but before attending class, they were at different climatic and metabolic conditions. We investigated the physiological and cognitive impacts of the commute of the students to reach their first lecture of the day. The commute was simulated in one room of a climate chamber, using a bicycle ergometer, at three different temperatures: 30 °C, 35 °C, and 40 °C. Attending the lecture was simulated in a different room of the same climate chamber, at a constant temperature of 26 °C. The participant’s skin and tympanic temperatures, pulse, and blood pressure were recorded at pre-specified time points during the study. The participants performed cognitive tasks, targeting working memory, task switching, and inhibition. Subjective thermal sensation and thermal comfort votes were also collected. The different commute temperatures did not significantly impact task accuracy (<em>p</em> &gt; 0.05). However, the reaction time in all three tests was found to significantly increase for higher commute temperatures (<em>p</em> &lt; 0.05). It was found that Stage 1 (i.e., when about to start cycling) and Stage 3 (i.e., entry of classroom) had a significant difference in tympanic and skin temperature (p &lt; 0.05) while Stage 5 (i.e., end of 1 h of class) showed no significant difference (p &gt; 0.05). A similar outcome was seen for thermal sensation votes. Overall, the results point to the outdoor air temperature during summer commutes impacting performance, subjective thermal sensation, and objective physiological measures of thermal comfort during the class hour, immediately following the commute.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"338 ","pages":"Article 115716"},"PeriodicalIF":6.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825751","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":"Advancing urban solar assessment: A deep learning and atmospheric modelling framework for quantifying PV yield and carbon reduction","authors":"Muhammad Kamran Lodhi ,&nbsp;Yumin Tan ,&nbsp;Xiaolu Wang ,&nbsp;Agus Suprijanto ,&nbsp;Muhammad Imran","doi":"10.1016/j.enbuild.2025.115717","DOIUrl":"10.1016/j.enbuild.2025.115717","url":null,"abstract":"<div><div>This study investigates the potential of rooftop photovoltaic (PV) systems to contribute to sustainable urban energy transitions in Lahore, Pakistan. While solar energy offers a significant opportunity to reduce urban carbon emissions, accurately estimating rooftop PV potential is challenged by complex atmospheric conditions and limitations in existing solar irradiance models. To address these challenges, this study proposes a novel framework combining atmospheric radiative transfer modelling with deep learning. High-resolution satellite imagery and optimized deep learning models are leveraged to precisely delineate existing solar panel installations across Lahore. Solar insolation on existing and potential rooftop PV installations is estimated by incorporating diurnal cloud fraction, cloud optical depth, and aerosol optical depth data to refine transmittance and diffuse fraction calculations, moving beyond simplified atmospheric parameterizations. Our analysis reveals that existing PV systems in Lahore generated 373 GWh of electricity in 2023, mitigating 0.23 Mt CO2e. However, by harnessing the full rooftop PV potential, estimated at 10,877 GWh, Lahore could reduce its carbon emissions by 6.74 Mt CO2e annually. This study identifies key areas within Lahore with the highest potential for rooftop PV development, providing valuable insights for urban planners and policymakers seeking to integrate solar energy into building infrastructure and advance towards net-zero goals.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"338 ","pages":"Article 115717"},"PeriodicalIF":6.6,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833868","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}