Energy and Buildings最新文献

{"title":"A multi-domain simulation approach for enhanced indoor environmental quality in offices: Challenges and opportunities","authors":"Brenda da Costa Loeser,&nbsp;Mateus Bavaresco,&nbsp;Ana Paula Melo","doi":"10.1016/j.enbuild.2025.115829","DOIUrl":"10.1016/j.enbuild.2025.115829","url":null,"abstract":"<div><div>As occupants experience simultaneous stimuli from multiple environmental domains, integrating multi-domain simulation practices can significantly enhance decision-making in the building design phase. This study introduces a multi-domain simulation workflow, merging thermal, visual, acoustic, and air quality domains to evaluate IEQ in offices. A case study uncovered significant challenges in integrating the various domains, underscoring the need for better interoperability between simulation tools. Systems, boundaries, and fluid dynamics shape the interplay between thermal, acoustic, and air quality. Besides, the thermal and visual domains emphasise the need for a holistic approach that also considers the influence of outdoor climate on indoor environments. This comprehensive approach supports initial design processes while highlighting the relevance of detailed multi-domain interactions.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115829"},"PeriodicalIF":6.6,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931368","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":"Assessing the performance gap in energy rating systems in Australian commercial office buildings","authors":"H. Bell ,&nbsp;V. Bunster ,&nbsp;T. Sartori ,&nbsp;D.W. Maxwell ,&nbsp;D. Rooney","doi":"10.1016/j.enbuild.2025.115813","DOIUrl":"10.1016/j.enbuild.2025.115813","url":null,"abstract":"<div><div>The energy performance gap, defined as the difference between expected and actual building energy performance, has been recognized as a significant obstacle to meeting energy targets in the building sector. In Australia, various policies and private-sector initiatives have been introduced to assist the construction industry in reaching its environmental impact reduction potential. The National Australian Built Environment Rating System (NABERS) and Green Star, two widely adopted building certification systems, have targeted this gap, with Green Star mandating design verification during construction and NABERS monitoring operational performance. This study aims to assess the effectiveness of these certification systems in reducing the performance gap in Australian office buildings by quantifying the predicted and actual performance of certified assets. Predicted energy usage data was collected from Green Star submissions of 176 office buildings and matched and compared with NABERS Energy ratings reporting performance during operation. The results were discussed with a group of building certification specialists, who provided insights on these outcomes and approaches towards narrowing the remaining performance gap. Overall, 65% of the cases achieved or exceeded their predicted NABERS Energy performance targets. The cases increased to 75% when considering the buildings’ highest NABERS Energy rating during the period of analysis and to 94% in cases that included NABERS Commitment Agreements (CA)—where developers are held accountable by contract to meet a specific performance target. These results suggest that the use of certification systems, such as the ones evaluated in this study, can contribute to closing the energy performance gap in Australia by setting buildings up for high-energy performance through design and operation.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115813"},"PeriodicalIF":6.6,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931370","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 the performance of human thermal stress models in the outdoors against observations","authors":"Roberta Jacoby Cureau ,&nbsp;Ilaria Pigliautile ,&nbsp;Ioannis Kousis ,&nbsp;Xinjie Huang ,&nbsp;Elie Bou-Zeid ,&nbsp;Anna Laura Pisello","doi":"10.1016/j.enbuild.2025.115837","DOIUrl":"10.1016/j.enbuild.2025.115837","url":null,"abstract":"<div><div>Urban overheating significantly impacts human well-being, requiring outdoor thermal comfort assessments. This research aims to assess the performance of models to calculate mean skin temperature in outdoor environments against measurements. The study compares a Lumped Human Energy Budget (LHEB) model, which is a simplified representation of the human body thermal state developed in an Urban Canopy Model (UCM), and the more sophisticated thermoregulation model JOS-3. Skin temperatures computed with the LHEB one-node model were significantly higher than observed values (RMSE: 1.71 – 2.76 °C). This variation, however, cannot be attributed to differences between simulated and monitored environmental data. It probably results from simplifications assumed in the human energy balance, such as disregarding dynamic thermoregulation processes, which considerably impact outdoor human comfort. The JOS-3 model, more advanced in terms of heat exchange and thermoregulation processes, performed better (RMSE: 0.21 – 1.62 °C, considering sessions when sensors were stabilized). Since all JOS-3 meteorological inputs can be provided by a UCM, the model can be directly integrated into UCMs for investigating the effects of urban overheating and mitigation strategies on human thermal stress. Therefore, it can serve as the human thermal comfort module for UCMs. The collected dataset is the first to combine measurements of the biophysical energy model outputs and the required UCM inputs, and to evaluate the reliability of the LHEB and the JOS-3 models outdoors, contributing to improve thermal stress modeling in inter-building urban contexts, which is an important approach for designing effective human-centric heat reduction measures and improving cities’ livability.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115837"},"PeriodicalIF":6.6,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143929380","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":"The carbon emissions of prefabricated building in urban renewal: Assessment and emission reduction path","authors":"Shuyan Zhao,&nbsp;Xinru Qu,&nbsp;Xiaojing Zhao","doi":"10.1016/j.enbuild.2025.115830","DOIUrl":"10.1016/j.enbuild.2025.115830","url":null,"abstract":"<div><div>Prefabricated buildings have been vigorously promoted for benefits in energy saving, environmental protection, and industrialization. However, few studies explore the impact of prefabricated buildings on carbon emissions in urban renewal. This paper thus aims to assess the carbon emissions of prefabricated buildings and ascertain the path for carbon emission reduction from prefabricated buildings in urban renewal. Firstly, the influencing factors and mechanisms of carbon emissions for prefabricated buildings were developed using <em>meta</em>-analysis and structural equation modelling (SEM). Secondly, an assessment model for carbon emissions from prefabricated buildings in urban renewal was developed using the system dynamics (SD) method. Thirdly, taking Beijing as a case, the carbon emissions from prefabricated buildings in urban renewal from 2023 to 2035 were predicted, determining the optimal carbon reduction path under 15 scenarios. During the forecast period, the carbon emissions from prefabricated buildings in urban renewal in Beijing are expected to increase by 18.10%. In 2035, the total carbon emission from building operations and prefabricated building fabrication in the system will account for 74.59% of the total predicted carbon emission. The optimal carbon reduction path considers four aspects: low-carbon policy regulation by enterprises and the public, technological progress, building area regulation, and energy structure optimization.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115830"},"PeriodicalIF":6.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143907776","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":"Carbon emission estimation at the urban functional zone scale: Integrating multi-source data and machine learning approach","authors":"Cui Wang ,&nbsp;Liuchang Xu ,&nbsp;Xinyu Zheng ,&nbsp;Yiming Hua ,&nbsp;Xingyu Xue","doi":"10.1016/j.enbuild.2025.115832","DOIUrl":"10.1016/j.enbuild.2025.115832","url":null,"abstract":"<div><div>With the acceleration of urbanization, urban carbon emissions have become a major contributor to global warming. Although the spatial distribution of carbon emissions has been studied, more accurate fine-scale results are needed. This study utilized Open Street Map (OSM) and point-of-interest (POI) data to identify urban functional zones. Carbon emissions of sample zones were calculated from electricity and natural gas data as target variables. The identified functional zones were combined with multi-source remote sensing data as features to construct a machine learning-based carbon emission model, predicting the spatial distribution of emissions at the urban functional zone scale. The study area was divided into 3,861 functional zones with total emissions of 51,124,900 tons, where residential, commercial, and industrial zones were the main sources. Multifunctional urban green space plazas also contributed significantly to emissions, a factor often overlooked in previous studies. Compared to conventional methods relying on nighttime light data, multi-source data significantly improved accuracy and spatial resolution, especially in industrial and green space zones. This study confirms that analyzing carbon emissions based on urban functional zones is effective and supports low-carbon city construction and management.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115832"},"PeriodicalIF":6.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917669","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":"Sustainable construction practices in urban areas: innovative materials, technologies, and policies to address environmental challenges","authors":"Md. Alhaz Uddin ,&nbsp;M. Shahabuddin ,&nbsp;M. Jameel ,&nbsp;Mizanoor Rahman ,&nbsp;Akter Hosen ,&nbsp;Fayez Alanazi ,&nbsp;Mohamed AbdelMongy ,&nbsp;Mahmoud S. El-kady","doi":"10.1016/j.enbuild.2025.115831","DOIUrl":"10.1016/j.enbuild.2025.115831","url":null,"abstract":"<div><div>The rapid population growth has increased the demand for construction activities, depleting the raw material reserves and increasing energy consumption. This review discusses sustainable construction materials, energy-efficient architectural designs and state-of-the-art technologies like green buildings and net-zero energy policy that will assist in addressing the environmental challenges. Utilizing recyclable, reusable, and biodegradable materials can significantly reduce the carbon footprint of buildings, promoting sustainable development. Material science, specifically the development of new materials and technologies, drives innovation in sustainable construction. The paper also discusses several policies and strategies on sustainability in the construction industry, including the environmental, economic, and social benefits derived from green construction methods. The environmental benefits achieved by adopting sustainable construction methods are significant; such practices reduce the ecological footprint of the built environment. To promote sustainability, it is essential to implement more robust regulations governing sustainable construction practices and to incentivise stakeholders by supporting research initiatives, legislative development, and regulatory frameworks. Furthermore, this study suggest for strengthening collaborations, developing targeted programs, leveraging digital technologies, and advancing the circular economy to optimise resource utilisation and minimise waste.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115831"},"PeriodicalIF":6.6,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143917654","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-objective optimization based PV-battery storage system sizing with judicious excess energy transfer framework fostering net-zero energy buildings","authors":"Raghuraman Ramakrishnan,&nbsp;Vijayakumar Krishnasamy,&nbsp;B. Chitti Babu","doi":"10.1016/j.enbuild.2025.115807","DOIUrl":"10.1016/j.enbuild.2025.115807","url":null,"abstract":"<div><div>Adopting net-zero energy buildings (NZEBs) is a key global strategy for attaining international climatic goals. Integrating onsite renewables is vital for NZEBs, and solar photovoltaic (PV) is widely adopted for its geographical flexibility. However, it demands a battery energy storage system (BESS) or grid support to tackle its natural intermittency, which invokes an affordability-reliability trade-off. Therefore, multi-objective grey wolf optimization (MOGWO) with net-zero focus has been employed in this study to optimally size the PV-BESS for different buildings with levelized cost of energy (LCOE), loss of power supply probability (LPSP), and excess energy index (EEI) as minimization objectives. Despite attaining a competitive compromise solution, EEI and LPSP often become inevitable. To address this issue, an excess energy transfer framework between the DC buses is facilitated and analyzed. The benefits achieved from the proposed framework motivated the optimal sizing of an energy-intensive building while utilizing excess energy transfer from neighboring buildings. This collaborative energy transfer approach cuts BESS requirements by 41 %, lowers LCOE by 37 %, reduces LPSP by 8 %, and eases grid burden by 6 % annually for the considered building compared to the scenario without excess energy transfer. Further, attempting simultaneous optimal sizing of all considered buildings ensures net-zero attainment, while providing similar cost and energy benefits. Hence, incorporating excess energy transfer from the planning stage enables urban high-rise buildings, even with limited rooftop space for PV installation, to become NZEBs economically, thus fostering sustainable power generation across urban environments.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115807"},"PeriodicalIF":6.6,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924325","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":"Analysis of the impact of supply air temperature on outdoor unit power consumption in EHP-AHU systems and development of a DNN-Based predictive model","authors":"Da-Sung Jung ,&nbsp;Ho-Won Byun ,&nbsp;Il-Hwan Choi ,&nbsp;Je-Hyeon Lee","doi":"10.1016/j.enbuild.2025.115812","DOIUrl":"10.1016/j.enbuild.2025.115812","url":null,"abstract":"<div><div>Recent studies on optimizing heating, ventilation, and air conditioning (HVAC) system control using data-driven predictive models are making significant strides toward achieving the decarbonization goals in the building sector. However, previous research often faced challenges in acquiring measured data that reflected control setpoint variations, leading to the use of simulation-derived data as training data for predictive models. Additionally, performance data provided by manufacturers for the simulation modeling of direct expansion air handling unit (AHU) systems like electric heat pumps (EHP)-AHU was based on experimental data using variable refrigerant flow systems connected to cassette-type and duct-type indoor units. This approach inadequately captured the heating and cooling performance and energy consumption characteristics of EHP-AHU systems. This study was conducted as a foundational investigation aimed at addressing the limitations of previous research and supporting the development of an optimal control algorithm for the EHP-AHU system. To this end, a multi-calorimeter experiment was carried out to quantitatively evaluate the cooling performance under varying supply air temperature conditions and to assess the feasibility of algorithm development through regression analysis. In addition, to develop a prediction-based optimal control algorithm, this study aimed to construct a deep neural network (DNN)-based prediction model capable of accurately capturing the dynamic variations in operating conditions, including outdoor air temperature, thermal load, and humidity, under real-world environments. Experimental results demonstrate that as the supply air temperature increases, the low pressure of the EHP-AHU system rises, leading to a reduction in the power consumption of the outdoor unit. Depending on outdoor conditions, power consumption reduction rates vary. For example, changing the supply air temperature from 12 °C to 14 °C and 16 °C resulted in maximum reduction rates of 20.7 % and 32.3 %, respectively. Thus, supply air temperature setpoints were identified as critical control variables for effectively reducing the power consumption of outdoor units. Using multi-calorimeter experimental data reflecting EHP-AHU system characteristics, a DNN-based power consumption prediction model was developed. Additionally, hyperparameter optimization achieved a highly accurate predictive model with an R² score of 0.87 and a coefficient of variation of the root mean square error of 16.3 %.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115812"},"PeriodicalIF":6.6,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934622","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 integrated evaluation framework for evaluating renewable-based building sector implementation in remote/off-grid areas with varying climatic zones","authors":"Rida Maryam,&nbsp;Syed Ali Abbas Kazmi,&nbsp;Muhammad Hassan,&nbsp;Mustafa Anwar","doi":"10.1016/j.enbuild.2025.115803","DOIUrl":"10.1016/j.enbuild.2025.115803","url":null,"abstract":"<div><div>The world population is growing and so is the demand for energy mainly, for industrialization and urbanization thus calling for the utilization of renewable energy sources. This paper involves techno-economic and environmental assessment aligned with sustainable development goals achieved using HOMER software to determine the best hybrid system for the considered off-grid areas in Pakistan. The study finds that the most suitable solutions include: Technical, economic, and environmental analyses of decentralized renewable energy systems in Punjab ($755,510 NPC, 0.153 $/kWh LCOE), Sindh (PV-DG-W-BESS $74,887 NPC, 0.141 $/kWh LCOE), Balochistan (PV-DG-BESS $80,891 NPC, 0.173 $/kWh LCOE), Kashmir (PV-DG-BESS $85,875 NPC, LCOE $0.161). Sensitivity analysis on the discount rates with the ranges of 9.75%, 15%, 21%, 27%, shows considerable effects on the costs like NPC and the LCOE. The proposed systems provide energy efficiency ranging from 92% to 98% and renewable energy fractions along with a mitigation effect of up to 87% in greenhouse gas emissions. Cost analysis further established high IRR, ROI, and low payback period affirming the cost-effectiveness of hybrid microgrids.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"341 ","pages":"Article 115803"},"PeriodicalIF":6.6,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143913020","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":"Machine learning prediction of heating and cooling loads based on Athenian residential buildings’ simulation dataset","authors":"Lei Zhang ,&nbsp;Mengying Cao ,&nbsp;Ning Li ,&nbsp;Lin Luo ,&nbsp;Yalan Chen ,&nbsp;Zhimin Li","doi":"10.1016/j.enbuild.2025.115808","DOIUrl":"10.1016/j.enbuild.2025.115808","url":null,"abstract":"<div><div>Energy is a critical utility and infrastructure of today that powers development in society. With increasing demand, particularly for heating and cooling, there are increasingly more worries over energy shortages and pollution. Accurate prediction of heating and cooling energy usage is the most important method to achieve the highest level of energy efficiency in building design. Machine learning models are promising tools for predicting. The main objective of this study is to use machine learning models to effectively predict the Heating and Cooling Load of buildings. The novelty of this study lies in using a broad dataset with diverse building structures and strong influence factors along with a novel prediction method. This includes the use of both stochastic-based (Stochastic Forest and Stochastic Gradient Boosting) and non-probabilistic-based models (Random Forest and Extreme Gradient Boosting), as well as the application of advanced optimization algorithms and ensemble prediction techniques. In addition, the study develops interpretable machine learning models using SHAP and FAST analyses to enable greater interpretability. From the obtained results, the SGEH, which combines prediction results by two Stochastic Gradient Boosting-based hybrid models, achieved an excellent performance in the prediction of Heating and Cooling Load with R2 values greater than 98% and 99%, respectively. The current book contributes to current research by utilising new and explainable prediction techniques and more precise predictions for energy consumption and, thus, filling the gap emerging in the energy-efficient building management.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"342 ","pages":"Article 115808"},"PeriodicalIF":6.6,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068670","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}