Energy and Buildings最新文献

{"title":"Research on the coupling relationship between energy consumption and architectural form design of university dormitory buildings under the target of low energy consumption","authors":"Qibo Liu ,&nbsp;Siqing Ma ,&nbsp;Yuyan Guo ,&nbsp;Yuheng Yan ,&nbsp;Xin Xu","doi":"10.1016/j.enbuild.2025.115664","DOIUrl":"10.1016/j.enbuild.2025.115664","url":null,"abstract":"<div><div>In response to the challenges of global climate change, promoting low-carbon and green sustainable development has become a key direction for urban construction. With advancements in technology and methods, research on building energy consumption has gradually expanded from individual buildings to urban and block scales. This study regards dormitory areas as functionally unified blocks. From the perspective of block scale, taking Xi ’an, a cold area in China, as an example, through typical case study, field investigation and Rhino-UMI (Urban Modeling Interface) tool, energy consumption simulation of dormitory buildings was carried out to analyze the impact of building layout, spacing and orientation on energy consumption. Based on the target of low energy consumption, architectural form design strategies were proposed, and their effectiveness was verified through case optimization and energy consumption simulation. The coupling relationship between the two was discussed. The results show that for high-rise dormitory areas, enclosed layout is more energy-efficient; in a parallel layout, the gable spacing is positively correlated with energy consumption, with 20-meter spacing selected for design, cooling, heating and total energy consumption are the lowest; for a vertical layout, the most energy-efficient gable spacing is approximately 24 m; moderate increases in building height can reduce energy consumption, in the case of meeting the relevant control indicators, high-rise dormitory area height in the 11–18 floors, a higher building height can be selected on the basis of the design; the optimal direction of the parallel arrangement layout is due north and south; the optimal orientation of the enclosing type is 15° south by west.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115664"},"PeriodicalIF":6.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143768823","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":"Multi-stage calibration framework for a digital twin model in building operations: Cold chain logistics centers case study","authors":"Rongrui Lin,&nbsp;Sanghyeob Kwon,&nbsp;Sungwoo Bae","doi":"10.1016/j.enbuild.2025.115662","DOIUrl":"10.1016/j.enbuild.2025.115662","url":null,"abstract":"<div><div>This paper presents a multi-stage calibration framework for digital twins in building operations for cold chain logistics centers, focusing on key aspects such as temperature dynamics, cooling loads, and power consumption during such building operations. The rapid expansion of cold chain logistics centers has introduced significant challenges in ensuring product quality, optimizing energy consumption, and reducing operational costs. Digital twin-enabled building operations offer a potential solution to address these challenges. The proposed building digital twin, developed using EnergyPlus and Python, integrates sensor data with particle swarm optimization (PSO) algorithms to systematically calibrate key parameters such as internal thermal mass, air infiltration, and HVAC performance. Calibration is performed with a time step of one-minute, improving model accuracy by capturing transient dynamics that often overlooked by conventional hourly calibration methods. A real-world building was used to validate the proposed building digital twin structure and calibration framework. Experimental results demonstrated the ability of the digital twin to predict building operating temperatures and energy consumption with high accuracy. The study highlights the benefits of using temperature and power sensor data as the primary inputs for model calibration, showing the potential on reducing reliance on more complex and intrusive measurement techniques. Furthermore, a multi-objective particle swarm optimization (MOPSO) algorithm was implemented to further verify the theoretical feasibility of the proposed multi-stage calibration framework</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115662"},"PeriodicalIF":6.6,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783133","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":"Selection and integration strategies of PCMs in traditional bricks for thermal comfort and energy efficiency: A comprehensive review","authors":"N. Ruiz-Marín","doi":"10.1016/j.enbuild.2025.115663","DOIUrl":"10.1016/j.enbuild.2025.115663","url":null,"abstract":"<div><div>Traditional bricks, while being cost-effective and durable construction materials, exhibit limited thermal performance under extreme weather conditions, which can lead to uncomfortable indoor environments and increased energy consumption for heating and cooling. To address this issue, the integration of phase change materials (PCMs) into bricks has emerged as an area of interest in sustainable construction. PCMs have the ability to store and release large amounts of latent heat during phase transitions, acting as a thermal buffer that regulates indoor temperature. However, a comprehensive review on how PCM integration impacts thermal performance, energy savings, environmental effects, and costs—particularly in bricks—has not yet been published. This review examines strategies for the selection and integration of PCMs in traditional bricks, analyzing the different types of PCMs, their properties, and the most common integration techniques. The influence of brick design on thermal performance is discussed, along with the importance of building orientation to maximize system efficiency. Several studies demonstrate that the integration of PCMs into bricks significantly reduces indoor temperature fluctuations, heat flow, and cooling demand, thereby improving thermal comfort and energy efficiency in buildings. However, challenges remain, such as improving thermal conductivity, reducing costs, and ensuring the safety of PCMs. Future research is needed to optimize integration techniques, develop PCMs with enhanced properties, and establish safety guidelines, paving the way for the widespread adoption of this technology in the construction of more sustainable buildings.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115663"},"PeriodicalIF":6.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739821","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":"Outdoor thermal benchmarks for the elderly and its application to the outdoor spaces design of nursing homes in Shanghai","authors":"Pengfei Zhou ,&nbsp;Dexuan Song ,&nbsp;Chi Zhang","doi":"10.1016/j.enbuild.2025.115654","DOIUrl":"10.1016/j.enbuild.2025.115654","url":null,"abstract":"<div><div>With China’s increasing aging, many older people in cities choose to live in nursing homes.<!--> <!-->However, previous research on nursing homes has concentrated mainly on the study of elderly-friendly design and indoor thermal comfort, paying little attention to the elderly’s thermal comfort in outdoor spaces.<!--> <!-->This study investigated older people’s thermal perceptions in six typical outdoor spaces at Q nursing home in Shanghai, aiming to establish a healthy and comfortable outdoor thermal environment for older people in nursing homes. The investigation utilized physical measurements, physiological<!--> <!-->measurements, and questionnaires to explore the correlations between outdoor thermal perceptions, thermal environment factors, and space enclosing degree. The Physiological Equivalent Temperature (PET) was chosen to assess the elderly’s outdoor thermal benchmarks in Shanghai. Based on the thermal benchmarks, optimal design strategies for nursing homes’ outdoor thermal environments were proposed.<!--> <!-->Results<!--> <!-->demonstrated that: 1) The elderly’s outdoor thermal sensation and thermal comfort were closely related. Air temperature and wind speed are the major influences on the elderly’s thermal perceptions. 2) The neutral PET, neutral PET range, acceptable PET range, and preferred PET of the elderly in Shanghai were identified as 18.3℃, 11.24 °C-25.26℃, 9.48 °C-28.4℃<!--> <!-->and 20.9℃, respectively.<!--> <!-->3) The elderly’s outdoor thermal sensation and the mean skin temperature are closely correlated with the building space’s enclosing degree and opening direction.<!--> <!-->Semi-open spaces are the most comfortable, and open spaces have the lowest comfort. 4) Optimum design strategies<!--> <!-->were proposed based on the natural and built environments, and the results provide design strategies for the<!--> <!-->outdoor thermal environments of nursing homes in regions of hot<!--> <!-->summer and cold<!--> <!-->winter.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115654"},"PeriodicalIF":6.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739826","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":"In-depth sensitivity analysis of heating demand and overheating in Dutch terraced houses using interpretable machine learning","authors":"Alexis Cvetkov-Iliev ,&nbsp;Vasilis Soulios ,&nbsp;Luyi Xu ,&nbsp;Günsu Merin Abbas ,&nbsp;Evangelos Kyrou ,&nbsp;Lisanne Havinga ,&nbsp;Pieter Jan Hoes ,&nbsp;Roel Loonen ,&nbsp;Joaquin Vanschoren","doi":"10.1016/j.enbuild.2025.115611","DOIUrl":"10.1016/j.enbuild.2025.115611","url":null,"abstract":"<div><div>Sensitivity analyses are often performed to facilitate the design or modeling of complex building systems by focusing on the most influential parameters. However, the insights they produce are generally limited to a ranking of the parameters’ impact. Instead, many applications could benefit from more advanced insights, such as how these impacts vary across different parameter values and scenarios. In addition, sensitivity analysis results should be easily interpretable to facilitate subsequent decision making. With these goals in mind, this paper introduces a novel sensitivity analysis method based on partial dependence (PD) plots. PD plots are further combined with dictionary learning, advanced visualizations, and surrogate models to facilitate their analysis and reduce their computational cost. Using this method, the effect of 26 parameters on heating demand and overheating in Dutch terraced houses is investigated. Two surrogate models are trained on 66,000 EnergyPlus simulations to predict the annual heating demand and percentage of overheating hours with excellent precision (<span><math><mrow><mo>≈</mo><mn>3</mn></mrow></math></span>–4 % of percentage error). The benefits of our approach are demonstrated through 3 use cases: 1) a comparison of the impact and energy-overheating trade-off of insulation measures across various scenarios, 2) improving the design of parametric simulations by eliminating redundant parameter values, and 3) uncovering complex behaviors in simulation or surrogate models, to build trust in them and diagnose potential modeling or training errors. Finally, our results suggest that surrogate models can be trained on much less data (1000–3000) without compromising sensitivity analysis results.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115611"},"PeriodicalIF":6.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725623","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":"Feasibility analysis of integrating solar thermal technologies into district heating network with urban building energy modeling","authors":"Y. Usta,&nbsp;A. Montazeri,&nbsp;G. Mutani","doi":"10.1016/j.enbuild.2025.115661","DOIUrl":"10.1016/j.enbuild.2025.115661","url":null,"abstract":"<div><div>The aim of this work is to study the effects of utilizing cleaner technologies in district heating networks and assess their contribution to the energy transition within densely populated urban areas. In this context, this study presents a methodology using Urban Building Energy Modeling (UBEM) with a place-based approach to assess the potential of integrating solar thermal collectors for space heating and hot water production services. Moreover, it compares their feasibility with photovoltaic panels. The proposed methodology can be applied to various urban contexts with different climate conditions using an open-source tool and available databases. The methodology adopts a bottom-up approach with a building as the territorial unit, and it takes into account site specific climate condition, building characteristics, urban features, and local constraints. The key step presented in this work is a detailed roof segmentation method used to evaluate the available areas on different roof orientations. The results show an increase in self-consumption and self-sufficiency levels when solar production is utilized for multiple energy services compared to a single service. This increase is three-fold in self-consumption index when hot water is added to the space heating service (a rise from 10% to 31%), and double for self-efficiency index, that is, from 12 to 24%. By using energetic, economic and social indicators, this study contributes to defining target indicators and indexes, while considering local constrains, to achieve the overarching goal of sustainability in energy system. This is aligned with the efforts that are being made to create sustainable cities through collective actions.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"338 ","pages":"Article 115661"},"PeriodicalIF":6.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825754","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":"Explaining building energy efficiency prediction through architectural and engineering solutions considering environmental impacts using a hybrid model","authors":"Semi Emrah Aslay","doi":"10.1016/j.enbuild.2025.115614","DOIUrl":"10.1016/j.enbuild.2025.115614","url":null,"abstract":"&lt;div&gt;&lt;div&gt;The aim of this study is to investigate building energy efficiency by integrating hybrid modelling approaches and interpretable models into architectural design processes and engineering solutions while considering environmental impacts. At the same time, it is aimed to achieve the most efficient building design possible. Separate analyses were carried out using a total of 6,913 data from Rutland (185) and Salford (6718) cities. The data were grouped into carbon emission information, architectural information, lighting information, personal heating information, and main heating system information to form a dataset. Light Gradient Boosting Machine (LightGBM) was preferred as the base model and Particle Swarm Optimisation (PSO) method was applied for hyperparameter optimisation. The hybrid model created in this way is called PSO-LightGBM. The optimization process was carried out using software in both R Studio and Python environments, utilizing seven different hyperparameters. Apart from the hybrid model used as a method, 2 different SHAP analyses, neural network based, and tree based, were performed to clearly explain the parameter relationships. The PSO-LightGBM hybrid model provided more successful predictions compared to the basic LightGBM model. While R&lt;sup&gt;2&lt;/sup&gt; values improved between 0.82 and 0.90 in the Rutland dataset, this value increased from 0.8687 to 0.8901 for test data and from 0.8538 to 0.9091 for training data in the Salford dataset. R&lt;sup&gt;2&lt;/sup&gt; values show an improvement of 7% in the Rutland dataset and maximum 6% in the Salford dataset. When the reduction in error rates is evaluated, it is found that the greatest improvement is in the Mean Squared Error (MSE) metric. MSE decreased by 17% in the Rutland dataset and by 4% in the Salford dataset. According to the SHAP Analysis results, CO&lt;sub&gt;2&lt;/sub&gt; emissions have the largest impact on energy consumption, while primary fuel types, number heated rooms and individual heating systems are other important parameters. While the tree-based SHAP model is more sensitive to physical parameters, the neural network model is more sensitive to indirect relationships. In both analyses, the communal heating system type has the lowest impact. In order to improve building energy efficiency, high efficiency individual boiler systems should be preferred, architectural approaches that optimise the number of heated rooms and smart heating solutions should be used, and central heating systems should be modernised. The results highlight the effectiveness of hybrid modelling approaches with SHAP analyses based on different baselines to ensure the integration of environmental impacts, architectural design processes and engineering solutions in terms of building energy efficiency. Furthermore, the findings contribute to the importance of interdisciplinary work in buildings to improve energy efficiency. Future studies can focus on the development of building energy performance prediction models","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115614"},"PeriodicalIF":6.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714516","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":"On-Site temperature and irradiance forecast tuning for improved load prediction in buildings","authors":"Amine Jarraya ,&nbsp;Tim Diller ,&nbsp;Himanshu Nagpal ,&nbsp;Anton Soppelsa ,&nbsp;Federico Trentin ,&nbsp;Gregor Henze ,&nbsp;Roberto Fedrizzi","doi":"10.1016/j.enbuild.2025.115642","DOIUrl":"10.1016/j.enbuild.2025.115642","url":null,"abstract":"<div><div>Building management systems (BMSs) with predictive control strategies rely on accurate weather forecasts to optimise heating and cooling operations. These strategies depend on precise climatic inputs to adjust system operations dynamically. Typically, weather forecast data is sourced from the internet and is generated by numerical weather prediction (NWP) models using advanced mathematical simulations. However, these models fail to account for localised nano-climatic variations, such as significant temperature and irradiance differences between the north and south sides of a building or the actual environmental conditions around the on-site sensors. These nano-climatic effects directly influence the calculation of the future thermal load of the building, which is crucial for predictive control approaches. To address this challenge, we propose a hybrid methodology that integrates NWP forecasts with local measurements from on-site sensors, improving NWP forecast accuracy. Our approach employs Inverse Distance Weighting (IDW) to interpolate NWP outputs to a specific geographical position and applies exponential smoothing for further finetuning by using historical error patterns. This methodology enhances the predictive accuracy of temperature and irradiance forecasts, achieving reductions of up to 60% to 80% in temperature errors and up to 20% to 30% in irradiance errors. Based on the finetuned weather forecast, the accuracy of building’s thermal load prediction is improved up to 86% compared to the predictions with IDW weather forecast.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115642"},"PeriodicalIF":6.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714515","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":"Is it necessary to calibrate existing decision-making results based on real option analysis during the process of climate change? A case study from Xiamen, China","authors":"Wenqiang Li ,&nbsp;Xin Jin ,&nbsp;Pei Peng ,&nbsp;Zaiyi Liao ,&nbsp;Min Wu ,&nbsp;Huahua Xu ,&nbsp;Jiashun Feng","doi":"10.1016/j.enbuild.2025.115658","DOIUrl":"10.1016/j.enbuild.2025.115658","url":null,"abstract":"<div><div>Previous studies have rarely examined how well Representative Concentration Pathways (RCP) scenarios fit with real weather data making climate change related decision-makings. This paper proposes a real option analysis (ROA) decision-making calibration method based on RCP (learning) scenarios uncertainty caused by the gap between the simulated and real weather conditions. Firstly, EnergyPlus is used to predict the energy consumption of a case building based on future climatic conditions, and the learning scenarios uncertainty is quantified. Secondly, a ROA decision-making calibration model (ROACM) is established through optimization to minimize the learning scenarios uncertainty. Finally, the ROACM is applied to a case study of an office building located in Xiamen, China, aiming to calibrate individual and sequential investment decisions for three retrofitting measures (<span><math><msub><mi>R</mi><mn>1</mn></msub></math></span>: horizontal shading, <span><math><msub><mi>R</mi><mn>2</mn></msub></math></span>: low-e windows, <span><math><msub><mi>R</mi><mn>3</mn></msub></math></span>: improving COP of chiller). The accuracy of ROACM is assessed through a sensitivity analysis. The results indicate that the learning scenarios uncertainty leads to annual energy consumption differences of approximately 8,451 kW <span><math><mo>∙</mo></math></span>h to 17,545 kW<span><math><mo>∙</mo></math></span>h in the case study. After calibration, the optimal timing for both individual and sequential investment has advanced by at least 4 years, significantly boosting returns. The optimal individual investment <span><math><msub><mi>R</mi><mn>3</mn></msub></math></span> generated an additional return of up to $52,744, while the sequential investment <span><math><mrow><msub><mi>R</mi><mn>1</mn></msub><mo>+</mo><msub><mi>R</mi><mn>13</mn></msub></mrow></math></span> (shading first, and improving COP of chiller later), increased by $175,017. The proposed ROACM demonstrated high performance during model validation in a case study, and could also aid governments or investors in ROA-based mitigation strategies analysis and increasing the reliability of the results.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115658"},"PeriodicalIF":6.6,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739822","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":"Unsupervised domain adaptation for HVAC fault diagnosis using contrastive adaptation network","authors":"Naghmeh Ghalamsiah ,&nbsp;Jin Wen ,&nbsp;K.Selcuk Candan ,&nbsp;Teresa Wu ,&nbsp;Zheng O’Neill ,&nbsp;Asra Aghaei","doi":"10.1016/j.enbuild.2025.115659","DOIUrl":"10.1016/j.enbuild.2025.115659","url":null,"abstract":"<div><div>Data-driven methods have shown great promise for heating, ventilation, and air conditioning (HVAC) systems’ fault diagnosis, but their reliance on well-labeled datasets poses challenges in real-world applications where such data may not be readily available. Meanwhile, well-labeled data might exist from virtual testbeds or laboratory systems. Domain adaptation could provide a solution to utilize labeled data from a source domain (such as a virtual or laboratory testbed) to diagnose faults in an unlabeled target domain, such as faults in a real building system. This paper utilizes the contrastive adaptation network (CAN) algorithm, originally successful in image classification, to overcome the specific challenges faced by current domain adaptation algorithms in HVAC systems. Furthermore, temporal causal discovery framework (TCDF), a causality-based framework for discovering causal relationships in time series data, is implemented in the data processing step to meet the requirements of convolutional networks, where spatially closer features are more likely to be correlated. The results on air handling unit (AHU) datasets demonstrate that the CAN algorithm effectively facilitates domain adaptation in the absence of target labels and that the feature reordering process reduces the training time and the number of loops required for convergence.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"337 ","pages":"Article 115659"},"PeriodicalIF":6.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714513","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}