Energy and Buildings最新文献

{"title":"A review of passive design strategies and their effect on thermal resilience in low-income households","authors":"Tejas Kokatnur ,&nbsp;Shane Ferreira ,&nbsp;Büşra Karadeniz Akkoç ,&nbsp;Elin Markarian ,&nbsp;Pedram Nojedehi ,&nbsp;Seif Qiblawi ,&nbsp;Kala Sewraj ,&nbsp;Burak Gunay ,&nbsp;William O’Brien ,&nbsp;Maya Papineau ,&nbsp;Marcel Schweiker ,&nbsp;Gülsu Ulukavak Harputlugil ,&nbsp;Elie Azar","doi":"10.1016/j.enbuild.2025.116508","DOIUrl":"10.1016/j.enbuild.2025.116508","url":null,"abstract":"<div><div>Climate change is causing more frequent and extreme weather events (e.g., heat waves and ice storms) that disproportionately impact low-income households with poor building conditions. Passive design strategies offer a promising solution to enhance building thermal resilience; however, little is known about their actual application and effectiveness in low-income households under different climate conditions. This paper presents a scoping literature review of 123 articles that evaluated passive design strategies applied to low-income housing contexts. The detailed article review shows that wall- and roof-related passive design strategies (e.g., insulation, sealing) are the most frequently studied and effective strategies, increasing comfort hours and energy savings with a wide variation of up to 24 % and 67 %, respectively. Combined strategies often outperformed individual strategies. In contrast, some studies reported unintended consequences following the adoption of passive design strategies, such as increases in energy demand reaching as high as 50 %. Such findings shed light on the need for design processes to consider multiple performance metrics, as well as behavioural and socio-economic factors that require more in-depth investigation, such as energy poverty and rebound effects. Detailed recommendations are finally provided to guide future research and applications on the topic, covering the need to (i) explore and combine underrepresented thermal resilience strategies and metrics, (ii) follow more standardized reporting practices, (iii) quantify costs and identify implementation barriers, and (iv) integrate participatory research methods to support technical assessments with contextual knowledge of the studied low-income households and communities.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"348 ","pages":"Article 116508"},"PeriodicalIF":7.1,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181287","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":"Framework for integrating energy-efficient mortgage into environmentally sustainable housing","authors":"Afua Ampofowaa Ennin ,&nbsp;Joshua Ayarkwa ,&nbsp;Dickson Osei-Asibey ,&nbsp;Peter Amoah ,&nbsp;Benjamin Baah ,&nbsp;Renas Atanga","doi":"10.1016/j.enbuild.2025.116504","DOIUrl":"10.1016/j.enbuild.2025.116504","url":null,"abstract":"<div><div>As global concerns over climate change and environmental degradation intensify, the housing sector has come under increased scrutiny for its environmental impact, and energy-efficient mortgages have emerged as a strategic financial tool to promote energy efficiency in housing and reduce carbon emissions. However, their success depends significantly on the involvement of key stakeholders, including governments, financial institutions, developers, and homebuyers. This paper explores the role of stakeholders in promoting green mortgages and how these financial instruments contribute to environmental sustainability in the housing sector. This study adopts a qualitative approach by conducting a systematic literature review (SLR) guided by the PSALSAR framework. Thirty (30) relevant articles were retrieved from Scopus, Web of Science, Science Direct, and Google Scholar, and the proposed framework was further validated through interviews. The data were organised into two broad thematic categories aligned with the study’s objectives, culminating in the development of a stakeholder-driven framework for sustainable housing. The findings reveal that isolated efforts are inadequate to address the intricate relationships between housing, energy and carbon emission, and climate objectives. Instead, coordinated, multi-stakeholder collaboration across the housing, financial, policy, and technical ecosystems is essential to scale and sustain energy-efficient housing solutions. The framework emphasises that energy-efficient mortgages are not merely financial tools but powerful catalysts for systemic change by effectively tackling the dual challenges of climate change and housing affordability. The study further aligns its contributions with the United Nations Sustainable Development Goals (SDGs), particularly SDG 7, SDG 11, SDG 13, and other global policies on climate change by bridging local housing finance strategies with global climate and sustainability agendas.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"348 ","pages":"Article 116504"},"PeriodicalIF":7.1,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156819","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":"Station-aware heating, ventilation, and air conditioning energy prediction in urban rail transit via mixture of experts","authors":"Wei Zhang ,&nbsp;Hui Fang ,&nbsp;Fan Yang ,&nbsp;Chaoqun Wang ,&nbsp;Tongle Wu ,&nbsp;Libo Xu ,&nbsp;Jiajun Bu ,&nbsp;Qiming Zhong ,&nbsp;Yueyao Yu","doi":"10.1016/j.enbuild.2025.116493","DOIUrl":"10.1016/j.enbuild.2025.116493","url":null,"abstract":"<div><div>Heating, Ventilation, and Air Conditioning (HVAC) systems account for a significant portion (about 25-35 %) of energy consumption in urban rail transit operations, making accurate modeling essential for enhancing energy efficiency and supporting sustainability. However, this task remains challenging due to differences in station layout, station type, environmental conditions, etc., leading to large variations in energy consumption patterns across stations. Most existing data-driven approaches rely on a limited set of features which fails to fully capture the complexity of HVAC systems. To address this limitation, we construct a real-world dataset comprising about 150 features by integrating operational records, environmental sensor data, and detailed equipment status. Moreover, a generic energy modeling framework based on the Mixture of Experts (MoE) architecture is developed to automatically capture the complex relationships among features, while adaptively learning the characteristics of different stations. The experimental results from 3 stations in Ningbo Rail Transit demonstrate that the MoE model achieves the best performance in predicting HVAC energy consumption at 30 min intervals compared to the other baseline models, delivering a root mean square error (RMSE) of 1.579 kWh and a mean absolute percentage error (MAPE) of 2.4 % on the test set. The results show that the proposed approach is capable of adapting to various station environments, enabling a unified and scalable solution for HVAC energy modeling in urban rail systems. In addition, the methodology provides a potential pathway for developing integrated energy models for other electromechanical systems within urban rail transit stations.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"348 ","pages":"Article 116493"},"PeriodicalIF":7.1,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145181294","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":"Integrated evaluation of full-scale and hybrid operating rooms for energy-efficient and resilient ventilation design","authors":"Indra Permana ,&nbsp;Zulvi Alfiqri Hidayatulloh ,&nbsp;Alya Penta Agharid ,&nbsp;Yung-Chieh Cheng ,&nbsp;Fujen Wang","doi":"10.1016/j.enbuild.2025.116507","DOIUrl":"10.1016/j.enbuild.2025.116507","url":null,"abstract":"<div><div>Efficient ventilation and contamination control in operating rooms (ORs) are essential for ensuring patient safety and minimizing energy consumption in healthcare facilities. This study presents an integrated evaluation of full-scale and hybrid operating rooms aimed at achieving energy-efficient and resilient ventilation design in healthcare environments. A full-scale surgical suite containing 12 operating rooms—including a high-performance hybrid OR (Class 100)—was analyzed through a combination of in-situ field measurements, Testing, Adjusting, and Balancing (TAB), and validated Computational Fluid Dynamics (CFD) simulations. Environmental parameters, including air velocity, pressure differentials, particle concentration, temperature, and humidity, were measured across all rooms to assess compliance with ISO cleanroom and healthcare ventilation standards. TAB procedures revealed substantial overdesign, with airflow rates exceeding 130 % of the specifications in several rooms. Rebalancing led to energy savings of up to 45 % per Air Handling Unit (AHU) without compromising air quality. CFD models, calibrated with TAB data and validated against field measurements (with &lt; 10 % error), simulated airflow distribution, pressurization cascades, and contaminant transport across the OR center. Hybrid OR simulations demonstrated that Class 100 conditions (144 ACH) can be achieved with a minimum of 100 ACH, enabling energy savings of 20.37 %. Meanwhile, Class 1000 conditions are feasible at a minimum of 65 ACH, resulting in up to 58.45 % energy reduction. The hybrid OR demonstrated not only high ventilation performance but also operational flexibility—adapting to varying cleanliness requirements without sacrificing control. This flexibility supports the resilience of surgical environments in response to procedural risk, occupancy changes, and sustainability goals.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"348 ","pages":"Article 116507"},"PeriodicalIF":7.1,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156361","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":"Economic implications of smart meter deployment in enhancing energy efficiency and demand flexibility in the residential smart economy","authors":"Aiguo Bolin","doi":"10.1016/j.enbuild.2025.116496","DOIUrl":"10.1016/j.enbuild.2025.116496","url":null,"abstract":"<div><div>This study looks at how smart meters help make home Renewable Energy Communities (RECs) with photovoltaic (PV) systems more energy-efficient and flexible in their energy use. The study employs high-resolution thermal modeling to predict how much energy single-family homes with different levels of insulation consume for heating and cooling under both baseline and centralized Demand Response (DR) scenarios. Smart meters let you see how much energy you’re using in real time. This enables you to change loads, utilize more of your own solar energy, and use distributed renewable energy more efficiently. The results demonstrate that flexible DR techniques can move up to 54 % of cooling loads and 33 % of heating loads, depending on the kind of structure, the amount of PV, and the time of year. Buildings with a lot of insulation were more energy-independent, and an economic study showed that self-consumption was more important than community sharing for shorter payback periods. But adding more users to the REC made it less flexible overall since there was more demand. The results show that smart meters not only improve energy use in buildings, but they also make it possible for energy communities to work together in a way that is both cost-effective and environmentally friendly. This study supports the creation of intelligent building systems that are in line with long-term goals to lower emissions, enhance indoor air quality, and make decentralized energy systems more resilient.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"349 ","pages":"Article 116496"},"PeriodicalIF":7.1,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264043","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":"Beyond steady-state: How human body responds to thermal environment step changes across indoor and outdoor spaces","authors":"Qi Zhu ,&nbsp;Peixian Li ,&nbsp;Richard de Dear ,&nbsp;Xing Shi ,&nbsp;Feng Yang","doi":"10.1016/j.enbuild.2025.116499","DOIUrl":"10.1016/j.enbuild.2025.116499","url":null,"abstract":"<div><div>Prolonged exposure to thermally neutral environments may compromise comfort and health, underscoring the need to synthesize knowledge on human responses to dynamic thermal environments. This study systematically reviews 92 articles (2014–2025) on thermal environment step changes, selected through rigorous literature screening and full-text evaluation. A multi-layered review methodology was applied: literature quality assessment and bibliometric analysis to evaluate research quality, trends, and thematic distribution; quantitative content analysis and qualitative review to compare experimental designs, predictors, and analytical strategies across indoor, outdoor, and transitional contexts; and <em>meta</em>-analysis to integrate quantitative findings on thermal sensation and physiological responses. Findings reveal lag, asymmetry, and overshoot in thermal sensation, varying acclimation times in physiological responses, and the influence of exposure sequence and duration on thermal pleasure. By consolidating fragmented methods and dispersed findings, this review builds a comprehensive reference for advancing dynamic thermal comfort research, supporting climate-responsive design and the development of comfort models that capture temporal and spatial complexity.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"348 ","pages":"Article 116499"},"PeriodicalIF":7.1,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156812","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":"Reliability of lookup table methods for estimating clothing insulation: Experimental verification using typical garments","authors":"Shengkai Zhao ,&nbsp;Liu Yang ,&nbsp;Haiyan Yan ,&nbsp;Yong Liu ,&nbsp;Siru Gao ,&nbsp;Yongchao Zhai","doi":"10.1016/j.enbuild.2025.116500","DOIUrl":"10.1016/j.enbuild.2025.116500","url":null,"abstract":"<div><div>Accurate quantification of clothing insulation is essential for reliable assessment of human thermal comfort and energy-efficient building operations. However, prevailing methods that are heavily dependent on standardized lookup tables may introduce significant biases. This study employs a combined approach of expert surveys and thermal manikin experiments to systematically evaluate discrepancies between estimated and measured clothing insulation values. A total of 34 garments (20 upper-body and 14 lower-body) were configured into 16 representative ensembles. Clothing insulation values were estimated by 120 thermal comfort researchers based on standard references, and precisely measured using a thermal manikin under controlled conditions (20–21 °C, 50% RH). Results reveal considerable variability in researchers’ estimates based on standard references, with errors escalating alongside insulation values. These errors reached critical maxima of 69% for single garments and 33% for ensembles. The widely adopted McCullough equation was found to systematically underestimate insulation values for typical Chinese adult clothing ensembles by 0% to 35%. Further analysis using thermal comfort models demonstrated that such input discrepancies can lead to significant predictive deviations: up to 0.59 units on the PMV scale, 5 °C on the SET scale, and an average skin temperature error of approximately 1.74 °C. Such deviations could substantially influence HVAC control strategies and occupant comfort evaluations. This study contributes a refined insulation dataset for clothing ensembles and underscores the need for improved estimation methods. It highlights the importance of accurate insulation input for reliable thermal comfort predictions and provides empirical evidence to support future revisions of clothing insulation standards.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"348 ","pages":"Article 116500"},"PeriodicalIF":7.1,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145217871","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 energy utilization characteristics and the potential for demand response in urban residential buildings in beijing","authors":"Xinyue Wang ,&nbsp;Ying Ji ,&nbsp;Menghan Niu ,&nbsp;Jiefan Gu ,&nbsp;Jingchao Xie","doi":"10.1016/j.enbuild.2025.116491","DOIUrl":"10.1016/j.enbuild.2025.116491","url":null,"abstract":"<div><div>Residential buildings energy consumption account for a significant portion of total energy use, particularly during summer high temperature periods, emphasizing the importance of optimizing energy usage. Demand response (DR) is an effective strategy to alleviate power demand. In this study a comprehensive framework is proposed by combining questionnaire survey, on-site measurement and EnergyPlus-Python co-simulation for optimizing DR strategies and implemented in Beijing urban residential buildings. Through the investigation and statistical analysis of 273 valid questionnaires, 4 kind of typical households and some appliances with DR potential such as air-conditioners (ACs), fans, washing machines and electric water heaters were extracted. Field measurements were conducted in 11 selected households throughout the summer. The energy usage patterns and load flexibility are quantitatively featured and discussed. Based on above statistical data, the DR co-simulation model is established and calibrated. Three optimization objectives (electricity cost saving, peak-valley reduction and DR capacity) are selected and the achieved effects are 24.9%, 31.2% and 61.9% respectively, when assuming 18:00–22:00 as the DR period. This study clarifies the energy usage characteristics of urban residential buildings in Beijing and establishes a model for optimizing DR strategies, providing technical supports for the government, integrators, and residents participating in the DR events.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"348 ","pages":"Article 116491"},"PeriodicalIF":7.1,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156359","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":"GUIDE: A prescriptive hybrid AI framework for energy-efficient appliances usage through behavioral modeling and LLM guidance","authors":"Alexios Papaioannou,&nbsp;Asimina Dimara,&nbsp;Stelios Krinidis","doi":"10.1016/j.enbuild.2025.116463","DOIUrl":"10.1016/j.enbuild.2025.116463","url":null,"abstract":"<div><div>Improving energy efficiency in residential environments reduces electricity consumption and cost, eases stress on the electricity grid, and promotes sustainability. However, realizing these benefits at scale requires accurate understanding of how energy is consumed within households. The precise interpreting of household energy consumption and providing personalized advice is still proving to be a hurdle due to overlapping appliance usage patterns and diverse user behaviors. This paper introduces Generative Usage-based Insights for Device Efficiency (GUIDE), a hybrid AI framework that integrates advanced device detection, behavioral modeling, and personalized natural language generation using fine-tuned large language models (LLMs). The system first disaggregates appliance-level consumption data utilizing statistical and temporal features, and then profiles household routines to enhance device classification accuracy. These insights are transformed into user-specific energy-saving advices through a domain-adapted LLM trained on structured behavioral prompts and contextual usage summaries. By combining time-series analysis, machine learning, and scalable LLM fine-tuning techniques (LoRA/QLoRA), GUIDE delivers fluent, context-aware and personalized recommendations that adapt to each household’s evolving energy patterns. Experimental results on real-world datasets demonstrate that GUIDE achieves high accuracy in device classification and generates actionable, human-readable advice bridging the gap between signal-level analysis and personalized energy engagement.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"348 ","pages":"Article 116463"},"PeriodicalIF":7.1,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145156814","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 Bayesian–physics integrated optimization framework for flexible building cooling under heatwave conditions","authors":"Xinyi Liu ,&nbsp;Xue Liu ,&nbsp;Yang Geng ,&nbsp;Da Yan ,&nbsp;Shan Hu ,&nbsp;Hao Tang","doi":"10.1016/j.enbuild.2025.116466","DOIUrl":"10.1016/j.enbuild.2025.116466","url":null,"abstract":"<div><div>The increasing frequency and severity of heatwaves driven by climate change have caused a sharp rise in air conditioning (AC) demand in buildings. This surge imposes significant stress on electricity grids, escalating the risks of peak load strain and power outages. Existing studies have shown that precooling and AC setpoint adjustments are promising strategies to unlock the energy flexibility. However, their combined effectiveness has rarely been examined specifically in the context of heatwaves. To address this gap, the study proposes an optimization-based control strategy that combines off-peak precooling with AC setpoint adjustments during peak hours. A multi-objective optimization framework is developed to balance energy cost, peak demand, and thermal comfort, and is implemented by coupling EnergyPlus with a Python-based distributed Bayesian optimization algorithm. Results from an office building case study show that the optimized control strategy significantly outperforms both the baseline and rule-based control strategies. Specifically, it reduces energy cost by 31.85 % and 6.57 %, and peak demand by 91.69 % and 77.32 %, compared to the baseline and rule-based strategies, respectively. Furthermore, the impact of the number of decision variables, weights, and internal thermal mass on the optimization performance is investigated. Key findings show that the Two-hourly strategy with six decision variables outperforms the Hourly and Period-based strategies under reasonable computational constraints. Moreover, buildings with high internal thermal mass can achieve lower precooling setpoints and higher AC setpoints during peak periods, significantly eliminating peak electricity demand and saving energy cost while maintaining thermal comfort.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"348 ","pages":"Article 116466"},"PeriodicalIF":7.1,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109689","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}