Energy and Buildings最新文献

{"title":"Building function, ownership, and space heating: Exploring adaptive reuse pathways in Swedish building stock","authors":"Ilia Iarkov ,&nbsp;Victor Fransson ,&nbsp;Dennis Johansson ,&nbsp;Ulla Janson ,&nbsp;Henrik Davidsson","doi":"10.1016/j.enbuild.2025.116108","DOIUrl":"10.1016/j.enbuild.2025.116108","url":null,"abstract":"<div><div>Adaptive reuse, the conversion of existing buildings to new functions, offers a sustainable alternative to demolition and new construction by reducing environmental impact, conserving materials, and minimising costs. This study presents the first large-scale, systematic analysis of adaptive reuse in Sweden, using Energy Performance Certificates (EPCs) from 141 778 buildings issued between 2007 and 2023. EPCs provide measured data on building function, conditioned floor area, construction year, and space heating energy use—a dominant contributor to operational emissions in cold-climate regions. The study identifies common conversion pathways and examines how building characteristics and ownership influence reuse patterns. Conversions were most frequent in buildings sized 1 000–5 000  m² and constructed between the 1930s and 1970s. Office–to–residential conversions were most common in absolute terms, but normalised data revealed frequent reuse of care facilities and retail spaces. Ownership analysis showed that corporate and public actors are the primary initiators of reuse, while private and cooperative owners are underrepresented. Energy performance analysis revealed that 82 % of converted buildings were associated with reductions in space heating energy use, and 54 % outperformed their non-converted counterparts. The average reduction for converted buildings was 9.6  kWh/m²·year, compared to 9.3  kWh/m²·year for non-converted buildings; office–to–residential conversions achieved mean savings of up to 19  kWh/m²·year. However, sign tests indicated that statistically significant trends were present in only a subset of conversion pairs, suggesting that the direction of energy use change is not uniformly robust. These differences likely reflect a combination of changes in building use intensity and renovation measures introduced during conversion. The findings demonstrate that adaptive reuse is physically feasible, broadly applicable, and, in some cases, associated with measurable energy efficiency gains. Although national in scope, the methodology is transferable to other regions with structured building energy datasets, and the results are relevant for countries with similar climatic conditions and ageing building stocks. This study provides an empirical basis for cautiously integrating adaptive reuse into energy efficiency policy, housing strategy, and long-term decarbonisation planning.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"345 ","pages":"Article 116108"},"PeriodicalIF":6.6,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563001","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":"Development and evaluation of cooling tower performance prediction using physics-informed neural networks","authors":"Zehongyu Kang ,&nbsp;Xin Zhou ,&nbsp;Da Yan ,&nbsp;Jingjing An","doi":"10.1016/j.enbuild.2025.116101","DOIUrl":"10.1016/j.enbuild.2025.116101","url":null,"abstract":"<div><div>Accurate prediction of outlet water temperature in cooling towers is crucial for implementing energy-efficient control strategies in air-conditioning systems, ultimately leading to reduced energy consumption in building operations. This study investigates the application of physics-informed neural networks (PINN) to leverage the strengths of both physical and data-driven models for predicting cooling tower performance. To achieve this, four experiments were conducted, resulting in the training of 2700 PINN models and 1900 artificial neural network (ANN) models. The predictive performance of the PINN models, developed through various methodologies, was thoroughly evaluated. The findings revealed that incorporating physical constraints into ANN models to form PINN models significantly decreased prediction errors and reduced model training costs. Based on the experimental results, this study proposes a systematic approach for constructing PINN models for cooling tower performance prediction. Key recommendations include: setting the proportion of physical constraints to 0.1, utilizing training data that constitutes 20 % of the dataset in both quantity and domain length, and ensuring that the distribution of the training data is centered within the prediction dataset. In the case analysis, the mean relative error (MRE) and root mean square error (RMSE) of the PINN model developed using the suggested methodology were 2.070 % and 0.309 °C, respectively. In comparison to the ANN model, the MRE improved by 4.712 %, and the RMSE decreased by 0.626 °C. These results demonstrate that PINN models offer significant advantages and considerable potential for predicting cooling tower performance, especially in scenarios with limited training data and narrowly distributed datasets.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"345 ","pages":"Article 116101"},"PeriodicalIF":6.6,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563526","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":"An extensive examination of uses of machine learning and artificial intelligence in the construction industry’s project life cycle","authors":"Nervana Osama Hanafy, Nourhan Osama Hanafy","doi":"10.1016/j.enbuild.2025.116094","DOIUrl":"https://doi.org/10.1016/j.enbuild.2025.116094","url":null,"abstract":"The integration of Artificial Intelligence (AI) and Machine Learning (ML) in the construction industry has gained increasing momentum over the past decade. This study presents a systematic literature review aimed at evaluating the deployment of AI/ML technologies across the five key phases of the construction project life cycle: planning, design, construction, operation and maintenance, and demolition. The review follows PRISMA guidelines and employs a three-stage filtering process to analyze publications from 2013 to 2023. Findings indicate that the planning and construction phases feature the most extensive and mature applications, particularly in cost estimation, risk analysis, safety management, and scheduling optimization. In contrast, adoption in demolition and post-occupancy phases remains limited. The study also identifies major challenges including data quality, integration barriers, and ethical considerations. By mapping AI/ML use across lifecycle stages, this paper provides a structured foundation for further academic inquiry and practical implementation of intelligent technologies in construction","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"29 1","pages":"116094"},"PeriodicalIF":6.7,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144566062","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":"Energy efficiency analysis and life cycle assessment of a biosafety level 4 laboratory","authors":"Ruiwen Zou ,&nbsp;Wei Zhang ,&nbsp;Jiahong Guo ,&nbsp;Xiding Zeng ,&nbsp;Kun Yang ,&nbsp;Zhangyu Li ,&nbsp;Xuhong Wang","doi":"10.1016/j.enbuild.2025.116096","DOIUrl":"10.1016/j.enbuild.2025.116096","url":null,"abstract":"<div><div>In recent years, the global threat of infectious diseases has prompted countries to increase the construction of high-level biosafety laboratories (BSLs). However, the high energy consumption in the operation of biosafety laboratories leads to their low utilization rate. In this study, a biosafety level 4 (BSL-4) laboratory in Southwest China was used for energy optimization while ensuring safety. It was found that the heating, ventilation, and air conditioning (HVAC) system in a BSL-4 laboratory consumes 4.3 times more energy than in a standard office. Energy savings of 36 % and 35 % could be achieved by reducing the number of air changes and performing heat recovery of exhaust gases, respectively. In addition, a complete Life Cycle Assessment (LCA) model was developed and it was found that the operational stage had the highest environmental impact of 48 %. As a result, it was proposed to perform a high-low interaction strategy that saves 25 %–28 % of energy and reduces carbon emissions by 24–27 tons per year; after optimizing renewable energy sources, the energy payback time and greenhouse gas payback time of the BSL-4 laboratory are 9.2 and 6.1 years, respectively. The study’s life cycle assessment modeling and recommended energy savings scenarios contribute to efficient energy design and operation at BSL.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"345 ","pages":"Article 116096"},"PeriodicalIF":6.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548800","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 cooling load assessment in solar-intensive climates: A ΔT-based solar-air temperature approach for enhanced free cooling modeling","authors":"Ali Keçebaş ,&nbsp;Mustafa Ertürk","doi":"10.1016/j.enbuild.2025.116095","DOIUrl":"10.1016/j.enbuild.2025.116095","url":null,"abstract":"<div><div>This study introduces a novel solar-air temperature (T_sa)-driven framework to enhance the conventional Cooling Degree Hour (CDH) method for accurately assessing cooling demand and free cooling potential in high solar irradiance regions. Unlike standard CDH models that rely solely on ambient temperature and thus underestimate cooling loads, the proposed model incorporates T_sa, a composite parameter that integrates solar radiation, sky temperature, surface emissivity, and absorptivity, capturing both convective and radiative effects. A ΔT-based sky classification algorithm was developed using 31 years of hourly meteorological data for Muğla, Turkey, to dynamically estimate sky temperatures under varying cloud conditions. The study systematically investigates three indoor setpoint temperatures (18 °C, 22 °C, 26 °C) and multiple emissivity scenarios to quantify mechanical and free cooling demands. The results reveal that using T_sa instead of ambient temperature can increase the calculated CDH by up to 12 °C during peak summer midday, exposing the limitations of traditional models. At a setpoint of 26 °C and emissivity ε = 0.9, mechanical cooling demand drops by over 30 %, and annual free cooling hours exceed 61,000 °C·h. Even at lower setpoints (18 °C, 22 °C), considerable nighttime and shoulder-season free cooling potential was observed. This hybrid approach bridges empirical simplicity and physical realism, offering a scalable, low-data solution for early design decisions and policy applications. By addressing a key research gap, the exclusion of radiative gains in simplified cooling models, this study provides a climate-responsive methodology to advance sustainable building cooling strategies, especially in Mediterranean and solar-intensive climates.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"345 ","pages":"Article 116095"},"PeriodicalIF":6.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557163","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":"User-friendly AI-driven automation for rapid building energy model generation","authors":"Mo ElSayed,&nbsp;Justin Shultz,&nbsp;Jill Kurtz","doi":"10.1016/j.enbuild.2025.116092","DOIUrl":"10.1016/j.enbuild.2025.116092","url":null,"abstract":"<div><div>With the ever-increasing energy efficiency and decarbonization targets mandated by building energy codes, the frequent use of building energy models (BEMs) has become essential. These models must iterate in parallel with the design development process, analyzing various variables to inform decisions and achieve optimal results and meet project goals. However, the complexity, expertise, and time-intensive nature of traditional BEMs often fail to match the fast pace of design development, which is often supercharged by computational design tools and value engineering. This study proposes an innovative framework that leverages artificial intelligence (AI) to automate EnergyPlus and Radiance energy and daylight modeling tasks, seamlessly integrating them with existing parametric design workflows. This integration enables rapid iteration without incurring time penalties. The framework introduces precise preconditioning of an affordable general use pre-trained large language model (LLM) to translate natural language descriptions or images (text-to-text and image-to-text) of buildings into corresponding model parameters—such as geometry, function, loads, and materials, etc.—leveraging ASHRAE 90.1/ IECC libraries. These models are then processed using the Honeybee/Ladybug open-source tools. The framework’s robustness is validated through a series of tests involving various prompts and images, achieving a 100% convergence rate. It reduces the time spent by expert energy modelers and helps address key challenges in AI integration, such as data quality, interpretability, code compliance, and scalability by realizing rapid batch processing and urban-scale building energy modeling.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"345 ","pages":"Article 116092"},"PeriodicalIF":6.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563525","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":"Validation of energy simulations for multi-family residences with photovoltaic power and gas cogeneration: Case study on balancing energy efficiency and resident health","authors":"Hikari Harasaki ,&nbsp;Kan Shindo ,&nbsp;Shin-ichi Tanabe ,&nbsp;Toru Shiba ,&nbsp;Shun Kawakubo ,&nbsp;Takashi Akimoto ,&nbsp;Toshiharu Ikaga","doi":"10.1016/j.enbuild.2025.116088","DOIUrl":"10.1016/j.enbuild.2025.116088","url":null,"abstract":"<div><div>In Japan, the energy consumption of the residential sector is significantly high. Therefore, insulation retrofitting and behavioral changes have become critical to enhance energy efficiency. Although several studies have examined these factors, they have focused on standard dwelling units and generalized lifestyle patterns.</div><div>This study investigated, through simulations, the impacts of thermal insulation performance and lifestyle behavior on energy consumption, health, and economic efficiency of an actual Zero Energy House (ZEH)-certified dwelling installed with a dual-generation system combining photovoltaic panels and a solid oxide fuel cell. The simulation model was validated using energy consumption and indoor temperature data from the winter of 2024; resident interviews were also incorporated to reflect their behavior.</div><div>Results showed that the primary energy consumption exceeded the ZEH design values, mainly owing to higher-than-expected lighting and gas consumption. Lighting was used in unoccupied rooms, and water heating was frequently used. Operating floor heating systems for only approximately 3 h per day was sufficient to maintain indoor temperatures above 18 °C, the WHO-recommended minimum in winter. Regarding the building envelope performance, residents’ morning blood pressure was estimated at 127.5 mmHg; however, a lower thermal insulation performance resulted in an increase in blood pressure to 137.6 mmHg, exceeding the 135 mmHg threshold, indicating potential health risks.</div><div>Behavioral optimizations, such as controlling the use of unnecessary lighting, high-efficiency appliances, and water heating systems, reduce energy consumption by 15% and improved economic efficiency by 33%, without compromising health. Moreover, even when operating 24-hr floor heating, these behavioral optimizations contribute toward reducing energy consumption by 11% and improving economic efficiency by 25%.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"345 ","pages":"Article 116088"},"PeriodicalIF":6.6,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548801","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 study on thermal sensation and physiological parameter changes under the interaction of environmental temperature and taste","authors":"Li Tong,&nbsp;Mingzhi Zhang,&nbsp;Songtao Hu,&nbsp;Xiaoxia Zhang","doi":"10.1016/j.enbuild.2025.116093","DOIUrl":"10.1016/j.enbuild.2025.116093","url":null,"abstract":"<div><div>The thermal environment is a crucial part of the indoor environment and can exert influences on people’s thermal sensation as well as emotional changes. Taste, being a significant physiological sensation of the human body, also has an impact on human physiology and psychology. However, there is no clear conclusion on whether different tastes have different effects on thermal sensation at different environmental temperatures. This study aims to investigate the interaction between various environmental temperatures and tastes which has an impact on the changes in human thermal sensation and physiological parameters. Thirty subjects were selected for the experiment, where data such as subjective questionnaires, average skin temperature, core temperature and heart rate were collected. Three environmental temperatures (18°C, 22°C, 26°C) and five tastes (tasteless, salty, sweet, sour and bitter) were set. The results indicated that sweet taste enhanced human thermal sensation in cooler and neutral environments, whereas sour taste diminished it in warmer and neutral environments. Salty and bitter tastes did not affect thermal sensation. In the case of different environmental temperatures, the core temperature of the same taste decreased, the average skin temperature and heart rate increased as the environmental temperature rose. There was basically no variation in each objective parameter of different tastes at the same environmental temperature. This study not only complements the existing research in the related field concerning the influence of taste on human thermal sensation, but also furnishes a scientific foundation for creating a comfortable and healthy environment for humans.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"345 ","pages":"Article 116093"},"PeriodicalIF":6.6,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144557162","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":"RCSGAN: Residual feature cycle semi-generative adversarial network for chillers fault diagnosis under extremely scarce labeled data","authors":"Xuejin Gao ,&nbsp;Zhiyuan Zhang ,&nbsp;Huayun Han ,&nbsp;Huihui Gao ,&nbsp;Yongsheng Qi","doi":"10.1016/j.enbuild.2025.116085","DOIUrl":"10.1016/j.enbuild.2025.116085","url":null,"abstract":"<div><div>The fault diagnosis of chillers is of significant importance for equipment maintenance and energy saving. Semi-supervised learning based methods alleviate the model’s rigid dependence on labeled data by learning from the information contained in a large amount of unlabeled data. However, the dynamic coupling characteristics and multi-fault severity levels of chillers result in poor inter-class separability of their data in low dimensional space, making existing models struggle to extract sufficient information from unlabeled data, and their performance tends to rely heavily on the number of labeled samples. Therefore, a fault diagnosis method based on residual feature cycle semi-generative adversarial network (RCSGAN) is proposed. This method enhances the inter-class separability by extracting residual features, allowing the model to mine more effective information from unlabeled data. Additionally, a cyclic training strategy combined with a novel pseudo-label selection method is proposed to further reduce the model’s reliance on the quantity of labeled data. Experimental results on the ASHRAE Research Project 1043 (RP-1043) dataset and real datasets show that the proposed method still achieves good fault diagnosis performance even in scenarios with extremely scarce labeled data. With only one labeled sample per category, RCSGAN improves the fault diagnosis accuracy by 26.16% compared with the current advanced methods on the RP-1043 dataset.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"345 ","pages":"Article 116085"},"PeriodicalIF":6.6,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144536046","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":"Benchmarking Danish, Estonian and Finnish NZEB requirements with European Commission recommendations in residential and office buildings","authors":"Raimo Simson ,&nbsp;Kirsten Engelund Thomsen ,&nbsp;Kim Bjarne Wittchen ,&nbsp;Jarek Kurnitski","doi":"10.1016/j.enbuild.2025.116086","DOIUrl":"10.1016/j.enbuild.2025.116086","url":null,"abstract":"<div><div>Comparing building energy performance across countries is challenging due to varying climatic conditions, calculation methods, primary energy (PE) factors, and input data discrepancies. This study systematically compares nearly zero-energy building (NZEB) requirements and energy calculation methodologies between Denmark, Finland, and Estonia, using a new office building, an apartment building, and a single-family house—each designed to comply with the respective national NZEB requirements. To account for climatic differences, a heating-degree-days correction factor was applied to thermal transmittance values of building envelope components. NZEB requirements of each country were then compared against the European Commission (EC) recommended values (EU 2016/1318) through normalization and benchmarking, employing detailed dynamic simulations. Both national and standardized (EN 16798–1:2019) input data was used with country-specific climate. In the benchmarking analysis, simulated primary energy performances were evaluated against EC NZEB thresholds, and the on-site renewable energy generation required to meet these targets was quantified. Results show that Estonian NZEB align with EC recommendations for Nordic climates, but EC Oceanic benchmarks were unmet even when Estonian configuration were applied to Danish climate indicating that the Oceanic benchmark is more ambitious than Nordic. Danish and Estonian national NZEB PE thresholds for office buildings showed a gap of 7% and 23%, respectively, to meet EC recommendations. However, the case study office building, surpassing Estonian NZEB standards, met both Nordic and Oceanic EC benchmarks. Finnish NZEB requirements were readily met by the case study buildings, highlighting that Finnish NZEB thresholds are currently less stringent compared to those of Denmark, Estonia, and the EC recommendations.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"345 ","pages":"Article 116086"},"PeriodicalIF":6.6,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144548799","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}