Energy and Buildings最新文献

{"title":"Experimental study of the influence of the vents on the thermal performance of a Trombe wall","authors":"Afef Laribi, Sylvie Bégot, Dominique Surdyk, Yacine Ait-Oumeziane, Valérie Lepiller, Philippe Désévaux, Nour El Zein, Ana Ribeiro De Carvalho","doi":"10.1016/j.enbuild.2024.115176","DOIUrl":"https://doi.org/10.1016/j.enbuild.2024.115176","url":null,"abstract":"This article presents an experimental study of a Trombe wall made of cellular concrete, associated with different vent configurations. The experiments were conducted in the laboratory using a Trombe wall connected to an adjacent room, under conditions of an intermediate season. The measurements focus on temperature and heat flux measured in various areas of the facility. Five configurations are compared: one with the vents fully open, three with reduced vent surface areas and different positions, and one with the vents fully closed. The impact of the number and position of the vents on the thermal behavior and efficiency of the Trombe wall is measured and analyzed. The results indicate that only the configuration with the vents fully closed exhibits a very different thermal behavior compared to the other configurations. In this configuration, the insulating properties of cellular concrete result in a minimal temperature increase in the room, making it an appropriate solution for maintaining occupant comfort even during summer conditions. Configurations with partially closed vents show generally similar behavior, except when there are significant reductions in vent surface. To effectively limit convective transfer with partial vent closure, a substantial reduction in vent surface (at least 60%) is required.","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"23 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841472","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":"Optimizing lighting design in educational settings for enhanced cognitive performance: A literature review","authors":"Meriç Çelik, Altuğ Didikoğlu, Tuğçe Kazanasmaz","doi":"10.1016/j.enbuild.2024.115180","DOIUrl":"https://doi.org/10.1016/j.enbuild.2024.115180","url":null,"abstract":"Lighting has more functions than simply illuminating spaces. For humans, light is the main signal that aligns our body’s internal clock, regulating circadian rhythms. This process instructs our bodies to wake up in the morning, become alert during the day, and feel sleepy at night. Disruption of these rhythms can impact neurological and psychiatric health, including cognitive performance. We can utilize light for mood improvements and better cognitive performance to create a suitable learning environment for students in educational buildings. These non-visual effects of light need to be considered from the beginning of the design process, making an interdisciplinary effort necessary. Even with adequate light and dark, the human eye reacts differently under various conditions, influenced by light’s photometric and colorimetric properties. While natural sunlight is ideal for aligning with our biological clock, it is not always sufficient, making artificial lighting essential indoors. LED technology offers promising solutions, catering to our non-visual needs in the absence of natural light and providing energy efficiency. This study reviews the literature that includes students’ cognitive performance and well-being, energy efficiency, running costs, and environment-related issues such as light pollution. It aims to explore the impact of lighting design in learning environments.","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"23 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142841311","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 privacy preserving multi-center federated learning framework for district heating forecast","authors":"Kais Dai, Esteban Fabello González, Rebeca Isabel García-Betances","doi":"10.1016/j.enbuild.2024.115164","DOIUrl":"https://doi.org/10.1016/j.enbuild.2024.115164","url":null,"abstract":"This paper presents a privacy-preserving Multi-Center Federated Learning (MCFL) framework for district heating demand forecasting with a 24-hour prediction horizon. To evaluate the effectiveness of this framework, we conducted a comparative analysis across three models: a monolithic model, a traditional federated learning (FL) model, and the proposed MCFL model. Our results demonstrate that the MCFL model improves the prediction accuracy of the standard FL model by 13.86%, suggesting it as a promising enhancement in federated settings. Furthermore, MCFL is particularly well-suited for district heating forecasting, as it handles data heterogeneity, reinforces privacy protections, and supports scalability, making it an ideal choice for complex, distributed environments.","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"5 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142840855","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":"BIM-based generative design approach for integral residential energy-efficient façades","authors":"Wei Ma, Xiangyu Wang","doi":"10.1016/j.enbuild.2024.115118","DOIUrl":"https://doi.org/10.1016/j.enbuild.2024.115118","url":null,"abstract":"Residential buildings significantly impact global energy consumption. Appropriate residential façade designs can considerably reduce energy consumption in maintaining indoor comfort. Current research on residential energy-efficient façade design primarily focuses on single-objective studies and exploring parameter boundaries of isolated façade elements, neglecting holistic perspectives. It results in a research deficiency in multi-objective optimisation and integral design approaches. This study presents an innovative AI-aided methodology integrating Building Information Modelling (BIM) and Generative Design (GD) to automate multi-objective optimisation and energy-efficient compliance assurance in Australian residential façade design. Through the developed BIM-based GD program, well-founded and compliant integral energy-efficient façade designs can be generated and modelled automatically and efficiently.","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"20 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797868","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":"Long-Term field testing of the accuracy and HVAC energy savings potential of occupancy presence sensors in A Single-Family home","authors":"Zhihong Pang, Mingyue Guo, Zheng O’Neill, Blake Smith-Cortez, Zhiyao Yang, Mingzhe Liu, Bing Dong","doi":"10.1016/j.enbuild.2024.115161","DOIUrl":"https://doi.org/10.1016/j.enbuild.2024.115161","url":null,"abstract":"The energy-saving potential of occupancy-centric smart thermostats has been extensively explored in simulations but lacked field testing for energy savings quantification and sensor performance assessment in real buildings. This paper presents a long-term field study conducted in a single-family home in Texas, U.S. to evaluate the performance of occupancy-centric controls (OCC) of HVAC (heating, ventilation, and air-conditioning) system in terms of energy savings, sensor accuracy, and impact on electric peak demand. The test site was equipped with a commercial off-the-shelf (COTS) smart thermostat and multiple occupancy presence sensors for OCC implementation. Additionally, a sub-metering system was installed to monitor electricity consumption of various end-use equipment, including the HVAC system. A supplementary device was installed to track the ground-truth occupancy for the accuracy evaluation of the occupancy presence sensor. Scenarios of baseline and OCC controls were alternated weekly over the 20-month testing period. The results indicated an effective OCC execution, as evidenced by indoor temperature profiles. During the 2023 cooling season, OCC achieved total energy savings of 1,958 kWh, corresponding to a 17.6% energy savings ratio. Under certain conditions, daily HVAC energy savings reached as high as 17 kWh, with a savings ratio of 35%. Sensor performance showed an overall accuracy of 83.8%, a False Positive Rate (FPR) of 12.8%, and a False Negative Rate (FNR) of 47.4%. A key limitation was the sensor’s inability to detect stationary occupants during sleep, leading to a midnight FNR of nearly 100% and significantly compromising thermal comfort. Additionally, the implementation of OCC resulted in extended periods of high electricity demand on summer afternoons, affecting occupant’s thermal comfort and posing potential challenges to community-level grid operations if OCC were widely adopted. This study addresses a critical research gap by empirically investigating energy-saving potential and occupancy sensor performance in residential buildings. Through a comprehensive field-testing study, the research examines the interrelationship between sensor accuracy, energy savings, and thermal comfort, an area that has received limited attention in the current literature.","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"238 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797867","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":"Drivers and barriers to the adoption of passive energy consumption optimisation strategies for UK buildings: A fuzzy approach","authors":"Amirhossein Balali, Akilu Yunusa-Kaltungo","doi":"10.1016/j.enbuild.2024.115148","DOIUrl":"https://doi.org/10.1016/j.enbuild.2024.115148","url":null,"abstract":"Passive energy consumption optimisation strategies present suitable remedies for mitigating the detrimental effects of buildings on both individuals and the environment. Despite their potential, the drivers and barriers to adopting passive strategies for buildings have yet to be comprehensively investigated, leaving a critical gap in the existing body of knowledge. Addressing this gap is of critical importance, as decision-makers need to possess a comprehensive understanding of the drivers and barriers, their relative significance, and the interrelationships among them to enable informed and effective decision-making regarding the implementation of passive strategies. Hence, this research endeavours to address the gap by employing a multifaceted research approach. The drivers and barriers to the adoption of passive strategies for UK buildings were initially identified through a systematic literature review and subsequently validated through semi-structured interviews with experts, during which additional drivers and barriers were suggested and the associations among them were discussed. Subsequently, the harmonised drivers and barriers were analysed using thematic and content analyses. Finally, questionnaire survey was used to quantitatively rank the identified drivers and barriers using trapezoidal fuzzy evaluation based on distance from average solution (F-EDAS) method. According to the results, “lack of strict legislation” and “reduced energy demand of building” were identified as the most important barriers and drivers for the adoption of passive strategies for UK buildings. Also, it was concluded that the enforcement of more stringent legislation can resolve several barriers simultaneously. The results obtained from this study can be used as reference points for future policy-making regarding passive energy consumption optimisation strategies.","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"21 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797869","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":"Thermoelectric power generation in concrete: A study on influential material and structural factors","authors":"Yong Luo, Hai Liu","doi":"10.1016/j.enbuild.2024.115159","DOIUrl":"https://doi.org/10.1016/j.enbuild.2024.115159","url":null,"abstract":"This study presents an innovative design for a concrete-based energy harvesting system, focusing on ordinary concrete, steel fiber concrete, and bamboo fiber concrete to identify the most effective material for power generation and efficiency improvement. The temperature transfer characteristics and output voltage of each concrete type are examined in detail. Simulation analyses assess how ambient temperature, wind speed, and the embedding depth of conductive aluminum plates affect the system’s temperature field and output voltage. Field tests measuring the output voltage of steel fiber concrete confirm the model’s accuracy. Results show that steel fiber concrete achieves the highest output voltage, followed by ordinary and bamboo fiber concrete. Among influencing factors, ambient temperature has the most significant impact on output voltage, ranked as ambient temperature > wind speed > aluminum plate embedding depth. Over time, the influence of embedding depth on output voltage lessens. When the temperature difference across the thermoelectric module reaches 54.6 °C, the system generates an output voltage of 1.09 V, meeting low-power generation requirements. This research aims to reduce pavement temperatures, preventing damage to pavement structures from high temperatures, while harnessing the temperature difference between concrete pavement and air to generate clean energy. This approach meets the power demands of road systems and lays a solid foundation for integrating energy generation with construction.","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"5 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797883","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":"Deep learning based on image analysis for refrigerant charging and leakage detection in building heat pump","authors":"Yanfeng Zhao, Zhao Yang, Zhaoning Hou, Shuping Zhang, Yansong Hu, Yong Zhang","doi":"10.1016/j.enbuild.2024.115157","DOIUrl":"https://doi.org/10.1016/j.enbuild.2024.115157","url":null,"abstract":"In heat pump systems, refrigerant leakage and charging faults are the common issues. Diagnosing refrigerant leakage and charging faults of the heat pump systems are crucial for reducing system energy consumption and maintaining stable high-efficiency operation. With the iteration of computing technology, data-driven approaches play an important role in fault detection and diagnosis. This research introduces a novel algorithm that transforms one-dimensional data into image format using Gramian Angular Field (GAF) and optimizes hyperparameters through Triangular Topology Aggregation Optimization (TTAO) within a Parallel Convolutional Neural Network (PCNN). Additionally, the approach integrates a Multi-head Self-Attention Mechanism (MSA) and employs a Support Vector Machine (SVM) in lieu of a Softmax layer for enhanced fault detection efficiency. A dataset for refrigerant leakage and charging faults was created using a Water-to-water Heat Pump (WWHP) test bench, providing the basis for evaluation against innovative algorithm and three existing algorithms: SVM, CNN-SVM, and PCNN-MSA-SVM. The findings highlight that TTAO successfully optimized the solution, minimizing the adaptation value from 0.167 to 0.025. The iterative process consistently demonstrated low loss values and steady accuracy improvements, trending towards enhanced stability. The proposed algorithm significantly outperformed the compared methods, achieving an impressive 97.5% accuracy rate and enhancing fault detection by 34.2%, 9.2%, and 4.2% respectively. Moreover, it showed robust and uniform F1 Scores across different fault types, marking an average increase of 42.0% over traditional SVM. This methodology not only optimizes hyperparameters adaptively but also identifies the best parameter settings, improving algorithm performance substantially.","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"78 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797886","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":"Impact analysis of uncertainty in thermal resistor-capacitor models on model predictive control performance","authors":"Dan Wang, Wanfu Zheng, Siqi Li, Yixing Chen, Xiaorui Lin, Zhe Wang","doi":"10.1016/j.enbuild.2024.115112","DOIUrl":"https://doi.org/10.1016/j.enbuild.2024.115112","url":null,"abstract":"Model Predictive Control (MPC) is extensively utilized for optimal control in building systems. Despite substantial research being dedicated to exploring the impact of uncertainties in external and internal disturbances on the performance of MPC, the existing studies neglect the potential impact of uncertainties in model parameter identification on control performance. To address this gap, this study quantifies the impact of model uncertainty on MPC performance through a test case in a virtual environment. Various levels of uncertainties for parameters <ce:italic>R</ce:italic> and <ce:italic>C</ce:italic> are artificially introduced to assess the MPC performance. The causes of the impact of model uncertainty on control performance are further explored through analysis. We select a first-order RC model to modelling building thermal dynamics. MPC is employed to optimize the heat pump signal with the goal of minimizing the energy cost while maintaining thermal comfort. The simulation results demonstrate that a negative deviation in model parameter identification has a more pronounced impact on MPC performance than a positive deviation, which has a negligible effect on MPC control performance. Deviations in parameters from their true values affect both heat losses from the zone and thermal capacity, thereby influencing the estimated temperature by the RC model. Consequently, these factors, in turn, affect the system’s control decisions, leading to variations in the objective function values. This study can provide an insight into the relationship of model parameters uncertainties and MPC performance, and facilitate the practical application of MPC in buildings.","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"9 1","pages":""},"PeriodicalIF":6.7,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142797884","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 interpretable data analytics-based energy benchmarking process for supporting retrofit decisions in large residential building stocks","authors":"Marco Savino Piscitelli,&nbsp;Giuseppe Razzano,&nbsp;Giacomo Buscemi,&nbsp;Alfonso Capozzoli","doi":"10.1016/j.enbuild.2024.115115","DOIUrl":"10.1016/j.enbuild.2024.115115","url":null,"abstract":"<div><div>Advanced energy benchmarking in residential buildings, using data-driven modeling, provides a fast, accurate, and systematic approach to assessing energy performance and comparing it with reference standards or targets. This process is essential for identifying opportunities to improve energy efficiency and for shaping effective energy retrofit strategies. However, building professionals often face barriers to adopting these tools, mainly due to the complexity and limited interpretability of data-driven models, which can negatively affect decision-making.</div><div>In order to contribute in addressing these issues, this study combines data-driven modeling with Explainable Artificial Intelligence (XAI) techniques to advance energy benchmarking analysis in residential buildings and enhance their usability by also non-expert users.</div><div>The proposed process focuses on estimating primary energy demand for space heating and domestic hot water in residential building units, extracting knowledge from about 49,000 Energy Performance Certificates (EPCs) issued in the Piedmont Region, Italy. The effectiveness of five machine learning algorithms is assessed to select the most suitable estimation model. Then to ensure the trustworthiness of the selected model, a XAI layer is implemented to identify and remove input variable domain regions that demonstrated to be critical for the robustness of the inference mechanism learnt in the training phase. Moreover, the study assesses the model capability to evaluate building energy performance, examining both the current state and potential scenarios for energy retrofitting. A second XAI layer is then introduced to provide local explanations for model estimations of both pre- and post-retrofit conditions of a building. The final aim is to enable an external benchmarking analysis, by extracting from the analysed EPCs reference groups of similar buildings, that facilitate a performance comparison for the investigated retrofit scenarios. This energy benchmarking process promotes transparent and informed decision-making, aiming to instill confidence in final users when leveraging data-driven models for energy planning in the building sector.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"328 ","pages":"Article 115115"},"PeriodicalIF":6.6,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759692","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}