Energy and Buildings最新文献

{"title":"Frost resilient energy recovery ventilation system for dwellings in Canada’s North and Arctic: A comparative study between a regenerative dual core and conventional single core systems","authors":"Boualem Ouazia,&nbsp;Chantal Arsenault,&nbsp;Patrique Tardif,&nbsp;Sador Brhane,&nbsp;Daniel Lefebvre,&nbsp;Sandra Mancini","doi":"10.1016/j.enbuild.2025.115477","DOIUrl":"10.1016/j.enbuild.2025.115477","url":null,"abstract":"<div><div>In the Canadian Northern climate, the winter outdoor temperatures may fall below −40 °C. With an average indoor temperature of 20 °C, a 60 °C increase in temperature applied to the incoming outdoor air represents a significant heating load. One problem faced by exhaust air heat/energy recovery systems in winter is the build-up of frost on the heat exchanger surfaces. The accumulation of frost in the core slows the transfer of heat/energy between the two airstreams and can impede ventilation and the ineffectiveness of HRVs/ERVs can lead to even poorer IAQ. This paper presents a novel air-to-air regenerative energy recovery ventilation system that employs a cycling heat exchanger as a defrost strategy to ensure a continuous delivery of outdoor air to the house. The dual core ERV system was assessed in real-world environment through a field trial, using the Canadian Centre of Housing technology (CCHT) twin houses. The side-by-side evaluation compared the winter performance of a dual core regenerative ERV and conventional single core ERV in term of ventilation rate, thermal performance that includes sensible and total heat transfer effectiveness and temperature of air supplied to the house. The obtained results showed no sign of frost problems and the dual core ERV provided continuous delivery of outdoor air without stopping to defrost, unlike the conventional single core ERV which spent up to 7.5 h per day defrosting. The <em>Test House</em> with the dual core ERV had higher ventilation rate by 23 % than the Reference Housee with single core ERV, was capable of providing air at the supply outlet at up to 3 °C higher temperature than the air supplied by a single core ERV, and had lower whole house energy consumption by ∼ 5 %.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"333 ","pages":"Article 115477"},"PeriodicalIF":6.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143427836","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 real-time visualization of urban heat islands using mean radiant temperature for urban design","authors":"Zahra Rashtian ,&nbsp;Mohammad Tabatabaei Manesh ,&nbsp;Mohammad Tahsildoost ,&nbsp;Zahra Sadat Zomorodian","doi":"10.1016/j.enbuild.2025.115470","DOIUrl":"10.1016/j.enbuild.2025.115470","url":null,"abstract":"<div><div>The Mean Radiant Temperature (T_mrt), is a critical indicator for understanding urban thermal comfort and microclimate conditions, particularly in urban areas experiencing higher temperatures compared to rural surroundings. T_mrt is heavily influenced by urban morphology, including building layout, street design, and green spaces which alter airflow, shading, and heat retention. Evaluating geometry alternatives during the early design stages in urban neighborhoods is challenging due to lengthy simulations and the need for extensive expertise in physical models. Recent studies have employed data-driven methods for quick design comparisons and new urban layout evaluations, successfully predicting Thermal indicators of Urban heat Island phenomenon but often limited by the diversity of urban configurations inputs used in training datasets. To address these limitations, this study proposes a novel framework that uses machine learning models to predict T_mrt as the primary indicator. A comprehensive training dataset of 200 cases was generated in Rhino7 using Grasshopper, Ladybug, and Dragonfly plugins. Sensitivity analysis was conducted to assess the impact of input uncertainties on model predictions, and the model’s performance was validated against unseen configurations. Among six machine learning algorithms tested, the CatBoost Regressor achieved the highest accuracy, predicting T_mrt with an R² = 0.93, RMSE = 4.30 °C, and MAE = 2.34 °C. Validation using 20 additional cases showed an accuracy of R² = 0.71, RMSE = 3.34 °C, and MAE = 2.27 °C in predicting T_mrt heat maps for new urban configurations. This framework successfully enables real-time T_mrt heat map analysis in simplified cubic neighborhoods within a 3D environment. Additionally, it enhances the temporal and spatial resolution of thermal patterns predictions, offering rapid and detailed insights into various urban design alternatives.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"333 ","pages":"Article 115470"},"PeriodicalIF":6.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143455133","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":"Improving urban-scale building occupancy and energy use estimation using a transportation-informed building occupancy estimation framework","authors":"Shayan Nejadshamsi ,&nbsp;Ursula Eicker ,&nbsp;Jamal Bentahar ,&nbsp;Chun Wang","doi":"10.1016/j.enbuild.2025.115468","DOIUrl":"10.1016/j.enbuild.2025.115468","url":null,"abstract":"<div><div>Buildings consume a significant portion of global energy, highlighting the importance of accurately estimating building energy use for effective urban energy management. Building occupancy profiles are a key factor in physics-based building energy estimation. Traditional Urban Building Energy Modeling (UBEM) tools often rely on deterministic standard schedules, such as those provided by ASHRAE, which fail to account for spatial and temporal diversity in building occupancy and use a single profile for all buildings within the same use type, leading to inaccuracies in energy estimation. While novel data sources like WiFi and Bluetooth can generate building occupancy profiles, these methods are typically suitable only for generating typical occupancy profiles for a limited number of buildings, ignoring the impact of building geographical location on occupancy patterns. This paper introduces a novel approach, the Transportation-Informed Building Occupancy (TIBO) model, which leverages urban-scale transportation data—including metro, bus, bike-sharing, and vehicle flow data—to generate individualized building occupancy profiles. Our approach addresses the limitations of existing methods by incorporating extensive real-world spatial transportation data. We applied the TIBO model to estimate building occupancy profiles across different districts in Montreal and compared these profiles with ground truth data and those derived from ASHRAE models. Our results show that the TIBO model improves occupancy profile accuracy by 55.29–62.52 % compared to ASHRAE profiles in our case study. Additionally, integrating these more realistic TIBO profiles into UBEM improved electricity use demand estimation by an average of 2.03–78.66 % across various city zones relative to using ASHRAE profiles. This study introduces a novel integrated urban system that connects building energy modeling with transportation systems, facilitating cross-sector analysis. It further highlights how transportation and mobility patterns are effective in refining the accuracy of building energy use models.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"333 ","pages":"Article 115468"},"PeriodicalIF":6.6,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic life cycle environmental impact assessment for urban built environment based on BIM and GIS","authors":"Luqi Wang,&nbsp;Zhiming Wu,&nbsp;Wenjie Zhang,&nbsp;Xiaoxia Wang,&nbsp;Weimin Feng","doi":"10.1016/j.enbuild.2025.115445","DOIUrl":"10.1016/j.enbuild.2025.115445","url":null,"abstract":"<div><div>Cities play a key role in mitigating climate change and promoting sustainable development. Dynamic Life Cycle Assessment (DLCA) has garnered increasing attention, but existing studies often focus on either temporal or spatial factors and overlook their integration. This study proposes an innovative DLCA model that integrates Building Information Modeling (BIM) and Geographic Information System (GIS) to assess the environmental impacts of urban built environments in both spatial and temporal dimensions. The model is structured around five key components: goal and scope definition, dynamic elementary flows, dynamic inventory analysis, environmental impact assessment, and interpretation. BIM provides detailed building-level data, while GIS integrates spatial and temporal data for regional analysis. A campus case study demonstrates the model’s applicability, showing that the BIM-GIS DLCA model results in 7.27% higher recycling rates, an 18.66% variation in energy structure assessments, a 2.72% difference in energy consumption, a 0.53% adjustment in green space carbon sequestration, and a −0.49% change in vehicle fuel economy/type and component lifespan compared to static assessments. These differences highlight how dynamic data integration enhances the comprehensiveness of environmental assessments and provides valuable insights for sustainable urban development.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"333 ","pages":"Article 115445"},"PeriodicalIF":6.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437685","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":"Analyzing and predicting residential electricity consumption using smart meter data: A copula-based approach","authors":"Waleed Softah ,&nbsp;Laleh Tafakori ,&nbsp;Hui Song","doi":"10.1016/j.enbuild.2025.115432","DOIUrl":"10.1016/j.enbuild.2025.115432","url":null,"abstract":"<div><div>Accurate demand prediction is essential for smart grid applications, and its precision can be significantly improved by accounting for individual consumption patterns in smart meter data. As nations and corporations increasingly strive for environmental sustainability, integrating clustering methodologies with forecasting models enables the identification of consumption trends and enhances predictive accuracy. Unlike existing prediction methods focusing on point estimates, we propose a novel clustering-based D-Vine Copula Quantile Regression (DVQR) framework for smart meter demand forecasting, which can capture the distribution of consumption behaviors about external factors such as weather conditions and time of day. The K-means are used to group the residential energy data into different groups. By integrating segmentation techniques with predictive models, DVQR leverages clustering to uncover complex and latent patterns in the data. Furthermore, DVQR extends beyond traditional forecasting by using quantile regression to capture variability, heteroscedasticity, and dependencies in consumption patterns, providing more comprehensive insights into the drivers of electricity demand. Our proposed approach is validated on the Melbourne household's dataset and compared with six models to demonstrate its superior performance. The results show that DVQR offers more accurate and flexible quantile predictions, especially when capturing consumption variability under different conditions.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"332 ","pages":"Article 115432"},"PeriodicalIF":6.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421560","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lifetime financial analysis of a model predictive control retrofit for integrated PV-battery systems in commercial buildings","authors":"Max Bird ,&nbsp;Reewa Andraos ,&nbsp;Salvador Acha ,&nbsp;Nilay Shah","doi":"10.1016/j.enbuild.2025.115459","DOIUrl":"10.1016/j.enbuild.2025.115459","url":null,"abstract":"<div><div>As electrical grids decarbonise, the need for flexible, real-time energy management systems becomes crucial to handle the variability of renewable sources. This paper investigates the lifetime performance of a commercial PV-battery system under three potential control approaches. Two rule-based controllers and one economic MPC approach are simulated over the lifetime of the battery, considering both the upfront capital and ongoing operational costs. Under the nominal rule-based control, installing the battery system saves 2.9% in operational costs per year. An informed rule-based schedule was then created, based on observing the typical PV and building loads and electricity price dynamics, increasing savings to 4.3%. These additional savings can be realised without any additional capital or operational investment. A supervisory MPC approach is integrated with the existing system control, requiring an upfront investment of $13.7k, combined with additional operational costs of $5.89k/yr. Accounting for these additional costs, net operational savings increase to 6% compared to the baseline operation without a battery system, while also reducing carbon emissions by 9.8%. MPC savings increase to 13.2% when considering the volatile electricity prices seen during the 2022 energy crisis. Despite these encouraging savings, current battery systems remain financially unattractive due to their high upfront cost, and all three control scenarios result in a negative NPV. A sensitivity analysis demonstrates that optimal sizing of batteries and reductions in their cost are the most significant factors when evaluating the lifetime performance of PV-battery systems.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"332 ","pages":"Article 115459"},"PeriodicalIF":6.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing new comfort band for adaptive model in Japanese residential building","authors":"Naja Aqilah ,&nbsp;Hom Bahadur Rijal ,&nbsp;Kazui Yoshida ,&nbsp;Fergus Nicol","doi":"10.1016/j.enbuild.2025.115469","DOIUrl":"10.1016/j.enbuild.2025.115469","url":null,"abstract":"<div><div>An environmentally sustainable society can be created by reducing building energy usage and achieving carbon neutrality. Adaptive thermal comfort models can aid with the establishment of national guidelines and standards on building indoor thermal conditions. However, many researchers focus on developing adaptive models without concerning the comfort band. A field measurement was conducted for 2 years in 64 selected houses with measured indoor environments and collected 32,988 votes from the thermal comfort survey. The findings indicate that majority of the residents expressed satisfaction with their thermal environments. The monthly mean comfort temperature (<em>T_c</em>) by Griffiths’ method ranged from 19 to 27 °C. In developing the comfort band for the adaptive model, the data from indoor temperature (<em>T_in</em>) and the difference between indoor and comfort temperature (<em>ΔT = T_in-T_c</em>) were analysed using quadratic regression and probit analysis. A wider width of the comfort band was found when using the data of <em>T_in</em>. By using the data of <em>ΔT</em>, both quadratic and probit analysis produced a similar comfort band. Thus, an appropriate comfort band was found as ±1.5 K and ±2.0 K for 90 % and 80 % limits of the adaptive model. Developing these methods for comfort bands of the adaptive models is crucial when creating a reliable standard and guidelines for building design or indoor environmental quality assessment.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"335 ","pages":"Article 115469"},"PeriodicalIF":6.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143534634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Day-ahead demand response potential prediction in residential buildings with HITSKAN: A fusion of Kolmogorov-Arnold networks and N-HiTS","authors":"Ali Muqtadir ,&nbsp;Bin Li ,&nbsp;Zhou Ying ,&nbsp;Chen Songsong ,&nbsp;Sadia Nishat Kazmi","doi":"10.1016/j.enbuild.2025.115455","DOIUrl":"10.1016/j.enbuild.2025.115455","url":null,"abstract":"<div><div>Accurate forecasting of Demand Response (DR) is vital for optimizing resource allocation in power systems, especially in markets where Load Aggregators (LAs) bid based on predicted DR potential. Traditional models struggle to capture the nonlinear dependencies of consumer behavior and the temporal patterns in energy consumption. This study aims to overcome these limitations by introducing HITSKAN, a hybrid approach which is a fusion of Kolmogorov-Arnold Networks (KANs) and Neural Hierarchical Interpolation (N-HiTS) to improve day-ahead DR potential forecasting. HITSKAN is able to solve the challenges faced by LAs by integrating the ability of KANs to model complex multivariate functions for nonlinearity together with the strength of N-HiTS in handling temporal dependencies. The methodology employs real-world residential load data from 114 apartments to capture historical demand response potential through thermal response modeling, which does not require appliance-level data and then applies the HITSKAN forecasting model to predict day-ahead DR potential. The performance of model is evaluated on all key metrics which include Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and systematic Mean Absolute Percentage Error (sMAPE) along with variance, standard deviation and computation time. Results demonstrate that HITSKAN outperforms state-of-the-art forecasting models in both winter and summer seasons. By incorporating KANs into a time series forecasting framework, HITSKAN offers a scalable and effective solution for DR potential forecasting, significantly enhancing grid management and resource optimization in residential settings.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"332 ","pages":"Article 115455"},"PeriodicalIF":6.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-model real-time energy consumption anomaly detection for office buildings based on circuit classification","authors":"Kuixing Liu,&nbsp;Jiale Tang,&nbsp;Lixin Xue","doi":"10.1016/j.enbuild.2025.115406","DOIUrl":"10.1016/j.enbuild.2025.115406","url":null,"abstract":"<div><div>With the widespread adoption of office building electricity consumption monitoring platforms, ample data are available for diagnosing energy anomalies, increasing interest in data-driven approaches. However, whole-building energy evaluation often fails to identify anomalies in specific sub-circuits. Additionally, the complexity of building energy systems has led research to focus mainly on data-driven methods, with limited exploration of individual sub-circuit characteristics. To address these issues, this study proposes a classification procedure based on physical attributes and data features of office building power circuits, categorizing energy-consumption circuits into four types. Subsequently, a multi-model real-time diagnostic framework was developed, which utilizes anomaly detection models tailored to specific circuits for precise identification of anomalies. The framework was experimentally validated using real-world data from a commercial office building in Haidian District, Beijing. The results demonstrated that the proposed method effectively performed hourly monitoring of energy consumption in lighting, chiller, and cooling tower circuits, and successfully identified multiple time periods during which energy consumption deviated from the normal range due to improper operations by facility management personnel. These findings highlight the benefit of integrating sub-metering with data mining, providing building operators with a novel approach to swiftly detect circuit-level abnormalities and optimize energy management strategies.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"332 ","pages":"Article 115406"},"PeriodicalIF":6.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421618","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":"Specific metrics for direct adiabatic cooling of industrial buildings and climate adaptation","authors":"Antoine Breteau ,&nbsp;Emmanuel Bozonnet ,&nbsp;Patrick Salagnac ,&nbsp;Jean-Marie Caous","doi":"10.1016/j.enbuild.2025.115472","DOIUrl":"10.1016/j.enbuild.2025.115472","url":null,"abstract":"<div><div>This paper presents an analysis of the performance of a direct evaporative cooling system incorporated into an industrial building, evaluated in various climates and weather conditions. This system is a simple and economical cooling solution widely used in industrial buildings that combines ventilation and water evaporation cooling. We characterized the system operation through the development of a coupled numerical model of the system and a typical industrial building, in a Mediterranean climate, in the mid-term horizon of 2050. A comparison without any system showed a 74 % reduction in degree-hours of thermal discomfort. Analysis of the building operation shows a predominance of nighttime free cooling, while the adiabatic operates during the occupancy hours. We compared the performance in four different locations, taking into account future weather and heatwaves. The system performed better in hot and dry climates if we consider only the thermal discomfort based on degree-hours, with a 48 % reduction in Abu Dhabi, compared to 41 % in Singapore. However, we observed very different tendencies with water consumption and cooling efficiency: with a cooling efficiency ratio to water use of 22.46 °Ch/m³ in the equatorial climate, which is almost double that obtained in the dry and arid climate. Arid climates were the most appropriate in terms of energy consumption. In Abu Dhabi, the performance (0.24 °Ch/kWh) was 13 % higher than in an equatorial climate such as Singapore. The results also show that the system performs better under future weather conditions for all the locations studied. Under future conditions, the cooling gain per unit of water consumed rose to 1.48 °Ch/m³, while the thermal escalation factor decreased by 0.054 points. These results highlight the ability of the system to effectively reduce thermal discomfort, while revealing trade-offs between thermal efficiency, energy consumption and use of water resources. This analysis underlines the relevance of the system to current and future climate challenges.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"332 ","pages":"Article 115472"},"PeriodicalIF":6.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143421732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}