Energy and Buildings最新文献

{"title":"Sex- and age-related differences in thermal sensitivity to radiant cooling in sleeping pods","authors":"Omar A. Ismail ,&nbsp;Sally Hussain ,&nbsp;Ahmed M. Ali ,&nbsp;Mohamed W. Tawfik ,&nbsp;Federica Fiacco ,&nbsp;Muhammed A. Hassan ,&nbsp;Mohamad T. Araji","doi":"10.1016/j.enbuild.2025.116185","DOIUrl":"10.1016/j.enbuild.2025.116185","url":null,"abstract":"<div><div>Sleeping pods are used for short-term rest or affordable accommodation. While their market is expanding rapidly, research on their thermal comfort conditions is scarce. This study explores thermal comfort and cooling performance in such pods when integrated with radiant cooling panels at different temperature levels while focusing on occupants’ sex and age. A computational model is developed and validated, then used to assess global and local thermal comfort, cooling capacity, and condensation risks. The results show that panel temperatures of 23–25 °C are sufficient to maintain thermal comfort for all occupants when no internal devices are active. Males experience higher operative temperatures than females due to their greater metabolic heat dissipation, with temperature differences up to 1.3 °C. Thermal comfort declines marginally with age, especially at lower panel temperatures. Radiant cooling is more effective in achieving thermal comfort for females at panel temperatures as high as 25 °C, with males requiring up to 25 % more cooling than females due to their larger body area and metabolic rate. Heat dissipation from small appliances shifts the preferred panel temperature from 23–25 °C to 19 °C for females, whereas, for males, a supplementary cold air stream is required to achieve thermal comfort. These remarks emphasize the importance of occupant-specific settings in such compact enclosures.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"346 ","pages":"Article 116185"},"PeriodicalIF":6.6,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714236","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":"Data-driven model for predicting power consumption of heat-pump-driven liquid-desiccant systems in building applications","authors":"Jae-Hee Lee ,&nbsp;Soo-Jin Lee ,&nbsp;Hansol Lim ,&nbsp;Ki-Hyung Yu ,&nbsp;Jae-Weon Jeong","doi":"10.1016/j.enbuild.2025.116191","DOIUrl":"10.1016/j.enbuild.2025.116191","url":null,"abstract":"<div><div>With the growing emphasis on indoor humidity control in energy-efficient buildings, heat-pump-driven liquid-desiccant (HPLD) systems have emerged for their ability to independently control air temperature and humidity. Previous studies have estimated their power consumption using theoretical models, which are often limited by structural complexity and challenges in physical interpretation. Additionally, theoretical models yield prediction inaccuracies when applied to buildings because they lack sensitivity to dynamic environmental variations typically observed in real-building conditions. This study develops a simplified data-driven model using real-building measurements to predict power consumption, capturing partial-load compressor performance under variable outdoor conditions and indoor thermal loads during the summer season. A polynomial regression method is used to develop the model in a simplified equation-based form. The developed model achieves R-squared, root mean squared error, and mean absolute percentage error (MAPE) values of 0.9583, 0.0668, and 8.37 %, respectively, in predicting the partial-load compressor power. Moreover, the model predicts the compressor energy consumption during summer operations with a percentage error of 0.36 %. Its adaptability is further validated against previous studies on HPLD systems with diverse features and specifications, within an acceptable error bound of ±20 % and a MAPE of 11.1 %. These results highlight the exceptional prediction accuracy and practical utility of the model developed in this study, supporting its adoption in various building application scenarios and replacement of theoretical models.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"346 ","pages":"Article 116191"},"PeriodicalIF":6.6,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144714235","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":"Comprehensive assessment of dynamic shading devices for daylighting and energy management in Saudi Arabian hot-arid buildings","authors":"Omar Humaidan ,&nbsp;Khaled Almazam ,&nbsp;Faizah Mohammed Bashir ,&nbsp;Mohammed J. Alshayeb ,&nbsp;Nedhal Al-Tamimi ,&nbsp;Yakubu Aminu Dodo","doi":"10.1016/j.enbuild.2025.116180","DOIUrl":"10.1016/j.enbuild.2025.116180","url":null,"abstract":"<div><div>Dynamic shading devices offer promising solutions for balancing daylighting and solar heat gain in hot-arid buildings, yet their operational effectiveness requires systematic evaluation.<!--> <!-->Filling a critical gap in comprehensive performance assessment, this study uniquely integrates long-term field measurements, calibrated building energy simulations, detailed occupant feedback, advanced control strategies, and techno-economic analysis to evaluate various dynamic shading systems within the challenging climate context of Saudi Arabia.<!--> <!-->The research methodology employed field measurements in eight identical test rooms equipped with different dynamic shading technologies, including electrochromic glazing, automated venetian blinds, and motorized roller shades. Continuous monitoring of interior illuminance, energy consumption, and occupant response was conducted over 14 months. Calibrated building energy simulations extended the analysis to additional climate contexts. Results revealed that automated external venetian blinds provided the best overall performance, reducing cooling energy by 32 % while maintaining useful daylight illuminance (300–3000 lux) for 68 % of occupied hours. Electrochromic glazing demonstrated superior glare control but reduced daylight availability by 27 % during cloudy conditions. The study concludes that dynamic shading systems must be carefully selected based on building type, orientation, and climate zone, with properly commissioned automation algorithms being crucial for optimizing the balance between daylighting and thermal performance. Integration with building management systems can further enhance performance by responding to both environmental conditions and occupant preferences. Theoretically, the work extends façade-performance modelling by delivering the first long-duration, empirically calibrated dataset for hot-arid contexts and by formalizing an optimized multi-parameter control algorithm that can be generalized to comparable climates.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"346 ","pages":"Article 116180"},"PeriodicalIF":6.6,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711683","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":"Regional building energy modelling: A residential building stock model for a Swedish island","authors":"Lukas Dahlström ,&nbsp;Fatemeh Johari ,&nbsp;Joakim Widén","doi":"10.1016/j.enbuild.2025.116178","DOIUrl":"10.1016/j.enbuild.2025.116178","url":null,"abstract":"<div><div>This study presents the development and validation of a regional urban building energy model (UBEM) for the island of Gotland, Sweden, using openly available national datasets. The aim is to capture the diversity of the residential building stock - including both urban and rural areas - and provide a robust tool for large-scale energy planning and decarbonisation strategies. The model integrates building geometry, national construction data, and energy performance certificates (EPCs) with probabilistic approaches for infiltration and stochastic occupancy simulation. Building geometry is calibrated against EPC data to assure optimal agreement to real-world circumstances. A novel archetype methodology, based on clustering analysis, is employed to represent the heterogeneous building stock accurately with 15 archetypes for two residential building use types. Implemented with an EnergyPlus-based simulation core, the model achieves high computational efficiency. Validation of aggregated annual results against regional energy use statistics and EPC data demonstrates strong agreement on the aggregate level: for single-family buildings, the annual energy use difference is 3.3 %, with a weighted mean difference in energy performance of 0.2 %, while multi-family houses show a modest overestimation. These results confirm that combining open data with advanced probabilistic methods allows reliably simulating building energy dynamics at a regional scale. The framework is easily transferable and adaptable to new case studies, cities, or regions, making it a valuable resource for policymakers and urban planners aiming to enhance energy efficiency and reduce greenhouse gas emissions.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"346 ","pages":"Article 116178"},"PeriodicalIF":6.6,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711682","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":"Revealing the role of Fe particle size in soft magnetic geopolymer for enhancing energy conversion in airport pavement induction heating","authors":"Gonghui Gu ,&nbsp;Xinchi Xu ,&nbsp;Jinsheng Han ,&nbsp;Lin Wan-Wendner ,&nbsp;Zhiji Gao ,&nbsp;Rongze Fu ,&nbsp;Chuanqing Fu ,&nbsp;Tao Ma","doi":"10.1016/j.enbuild.2025.116182","DOIUrl":"10.1016/j.enbuild.2025.116182","url":null,"abstract":"<div><div>In this study, a soft magnetic geopolymer (SMG) co-modified with nanomagnetic fluid and spherical micron-sized Fe powders was developed for enhancing the energy conversion in airport pavement induction heating. The effects of Fe particle size on the microstructure, electromagnetic behavior, and mechanical performance of SMG were systematically investigated, with a focus on the size-dependent coupling mechanisms. Results show that Fe powder with a particle size no less than 150 μm provides pore-filling and skeletal reinforcement, leading to improved compressive strength and stable magnetic permeability. In contrast, finer particles significantly increase the specific surface area, which intensifies internal demagnetizing fields and magnetic flux pinning, thereby reducing saturation magnetization and coercivity. Simultaneously, excessive water demand caused by fine particles suppresses geopolymer gel formation, resulting in increased porosity and decreased mechanical strength. COMSOL simulations confirm the development of localized demagnetizing fields around smaller Fe particles. Indoor induction heating tests further reveal that the incorporation of SMG improved the energy conversion efficiency during induction heating by 19.4 % when the Fe powder particle size was not less than 150 μm, demonstrating its potential for energy-responsive, structure-integrated infrastructure.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"346 ","pages":"Article 116182"},"PeriodicalIF":6.6,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685710","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":"Silica aerogel composites with excellent thermal insulation for building applications","authors":"Song He ,&nbsp;Hongcheng Li ,&nbsp;Yuling Zhang ,&nbsp;Hongliang Sheng ,&nbsp;Yajun Huang","doi":"10.1016/j.enbuild.2025.116169","DOIUrl":"10.1016/j.enbuild.2025.116169","url":null,"abstract":"<div><div>In high altitude regions with low air pressure, the thermal insulation performance and thermal stability of building insulation materials are more demanding. Developing effective thermal insulation materials for buildings is crucial for achieving energy efficiency and reducing emissions. In this study, a modified glass fiber reinforced aerogel (GFRA) composite was successfully synthesized using inexpensive water glass as the raw material. Experimental results show that GFRA is significantly hydrophobic with water contact angle of 146° and low thermal conductivity of 0.018 W/(m·K). The complex network structure within the fiber mat and the high loading ratio of the aerogel contribute to the excellent thermal insulation properties of GFRA. Measurements indicate that 2.25 mm thick GFRA can maintain insulation temperature of 38.3 °C when exposed to 90 °C. Under simulated low pressure conditions in environmental chamber, with pressure of 0.06 MPa, the GFRA’s insulation temperature increased significantly, reaching 227.3 °C at approximately 400 °C. The thermogravimetric analysis confirmed the material’s excellent thermal stability, with maximum total mass loss limited to 7 % while maintaining significant hydrophobicity with water contact angle of 120.3°<!--> <!-->in high-temperature environments. GFRA’s properties demonstrate its exceptional thermal insulation capabilities, making it highly suitable for building applications.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"346 ","pages":"Article 116169"},"PeriodicalIF":6.6,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685789","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":"Domain-invariant representation learning for generalizable chiller model: a real-world case study","authors":"Jin Hong Kim ,&nbsp;Young Sub Kim ,&nbsp;Hyeong Gon Jo ,&nbsp;Jeeye Mun ,&nbsp;Cheol Soo Park","doi":"10.1016/j.enbuild.2025.116168","DOIUrl":"10.1016/j.enbuild.2025.116168","url":null,"abstract":"<div><div>Data-driven models have been widely adopted due to their ease of modeling and good prediction accuracy. However, they face a challenge in ensuring predictive performance on <em>unseen</em> datasets, especially when the training dataset is imbalanced. For this reason, Transfer learning (TL), that leverages source domain datasets, has attracted attention. However, TL is often limited by its <em>one-way</em> learning process, which transfers knowledge from a data-rich (<em>source</em>) domain to a data-scarce (<em>target</em>) domain. In this regard, this study proposes a Domain-Invariant Representation Learning (DIRL) modeling approach that extracts generalizable knowledge through a <em>bidirectional</em> learning framework. To realize it, the authors develop a DIRL chiller model using two real-life chillers’ datasets where hidden layers are shared within an artificial neural network (ANN). Relevant data were collected at the sampling time of one hour between April 2020 and December 2023.</div><div>For comparison, the following five simulation models of the two-chillers were cross-compared in terms of model accuracy and extrapolation ability: (1) an individual ANN model, (2) an ANN model developed by combined data from two chillers, (3) a transfer learning model developed by the other chiller data, (4) a transfer learning model developed by a physics-based model, and (5) a DIRL model. The predictive performance of all five models was satisfactory for the target chiller by achieving a mean average error (MAE) = 0.41–0.49 and a coefficient of the variation of the root mean square error (cvRMSE) = 7.0–8.3 % for the coefficient of performance (COP). In contrast, the combined-data ANN and DIRL presented superior predictive performance by achieving an MAE = 0.10–0.13 and a cvRMSE = 2.0–3.1 %. The DIRL model demonstrated best superior extrapolation ability with an MAE = 0.36 and a cvRMSE = 8.5 %. As a result, the DIRL model achieved improvements of 0.81 of MAE and 16.4 % of cvRMSE for chiller COP prediction, and 0.83 in MAE and 17.4 % in cvRMSE for extrapolation ability, compared to the individual ANN model. By leveraging bidirectional learning with combined datasets and a shared feature extractor, the DIRL chiller model can infer general chiller knowledge while maintaining a consistent predictive performance in terms of both accuracy and extrapolation ability.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"346 ","pages":"Article 116168"},"PeriodicalIF":6.6,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685791","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":"Modeling for coupled thermal and humidity environments in spaces with impinging jet ventilation using zonal model theory","authors":"Peng Du ,&nbsp;Xiao Ye ,&nbsp;Wentao Xi ,&nbsp;Yanming Kang ,&nbsp;Ke Zhong","doi":"10.1016/j.enbuild.2025.116171","DOIUrl":"10.1016/j.enbuild.2025.116171","url":null,"abstract":"<div><div>Thermal stratification, due to the coupling of heat and moisture transfer, also results in humidity stratification. While many models address thermal stratification in impinging jet ventilation (IJV) systems, few consider its humidity distribution. This study develops a zonal model that simultaneously predicts vertical temperature and humidity profiles for IJV. The space is vertically divided into sub-zones, with mass and energy balance equations established for each. A critical parameter in the model is the inter-zonal mass flow rate (<em>m</em>), which governs the exchange of air between adjacent zones and thus determines the transport of heat and moisture. To quantify this parameter, CFD simulations are performed under various operating conditions, and a height-dependent function is derived for <em>m</em>. The model’s accuracy is validated by comparing the predicted temperature and humidity distributions with numerical results. The results show that the proposed model achieves a mean relative error of 6.95 % for temperature prediction and of 1.41 % for humidity prediction. Besides, the proposed model is compared with original zonal model considering only heat transfer. It reveals that the maximum temperature prediction discrepancy for the original model reaches up to 5.03 °C, whereas the proposed zonal model shows a discrepancy of only 1.99 °C. This underscores the importance of considering the coupling of air temperature and moisture in indoor environmental studies. The current model not only provides a more accurate description of temperature distributions for IJV but also enables the prediction of indoor humidity distribution—a capability that the original zonal model lacks.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"346 ","pages":"Article 116171"},"PeriodicalIF":6.6,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144679980","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 fault diagnosis strategy for refrigerant leakage of the air conditioning system in high-efficiency internet data centers","authors":"Chuang Yang,&nbsp;Shikai Tan,&nbsp;Huanxin Chen","doi":"10.1016/j.enbuild.2025.116176","DOIUrl":"10.1016/j.enbuild.2025.116176","url":null,"abstract":"<div><div>Internet data centers (IDCs) are large energy consumers and the IDCs air conditioning system will inevitably experience refrigerant leakage due to long-term and non-stop operation, which increases the risk of computer services’ health and leads to unnecessary energy waste. Therefore, this paper presents a fault diagnosis strategy for refrigerant leakage of the IDCs air conditioning systems based on deep neural network (DNN). The <em>Gini</em> coefficient is utilized to choose important feature variables. Then a fault diagnosis model was developed based on the DNN. On-the-spot experiments of an IDCs air conditioning system are conducted to collect practical operational data to validate this strategy. Refrigerant charge under normal conditions and five various leakage levels are investigated. The offline data of IDCs air conditioning system are collected to train the DNN models, testing results show that the proposed DNN model has good classification performance and generalization ability. The accuracy, geometric mean accuracy (GMA), false alarm rate (FAR), missing alarm rate (MAR) reach to 99.99%, 99.92%, 0%, 0%, respectively. A small amount of online data was used to update the model, the classification performance of the model will be greatly improved, which shows that the proposed DNN model has great potential for online data classification. Accuracy increase by 26.62%, from 73.66% to 93.27%, FAR decrease from 32.82% to 0%.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"346 ","pages":"Article 116176"},"PeriodicalIF":6.6,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711700","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":"Towards energy flexible commercial buildings: Machine learning approaches, implementation aspects, and future research directions","authors":"M.M.A.L.N. Maheepala ,&nbsp;Hangxin Li ,&nbsp;Dilan Robert ,&nbsp;Lasantha Meegahapola ,&nbsp;Shengwei Wang","doi":"10.1016/j.enbuild.2025.116170","DOIUrl":"10.1016/j.enbuild.2025.116170","url":null,"abstract":"<div><div>Commercial buildings encounter considerable challenges in predicting and managing energy flexibility, arising from the complexity of their energy systems and the interdependencies among system components and building thermal mass. Nonetheless, the emergence of “smarter buildings” creates significant opportunities for applying machine learning (ML) techniques in energy flexibility. These methods provide significant benefits to commercial building owners, with multiple states integrating energy flexibility provisions for commercial buildings into their regulatory frameworks. This paper provides a systematic review of the role of commercial buildings in energy flexibility studies, with a particular emphasis on ML techniques used in the characterisation, optimisation, and forecasting of energy flexibility. Furthermore, it examines the direct monetary and non-direct monetary benefits and practical challenges associated with integrating flexibility concepts into commercial buildings, as well as the policy and regulatory frameworks that facilitate flexibility implementations. A comprehensive understanding of these aspects will be beneficial for developing robust frameworks that enhance the adaptive capacity of commercial buildings, thus enabling their seamless integration into dynamic energy markets while supporting grid stability and sustainability objectives.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"346 ","pages":"Article 116170"},"PeriodicalIF":6.6,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144685711","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}