Energy and Buildings最新文献

{"title":"Energy management of net-zero energy buildings: A two-layer hierarchical approach","authors":"Seyyed Reza Ebrahimi ,&nbsp;Morteza Rahimiyan ,&nbsp;Mohsen Assili ,&nbsp;Amin Hajizadeh","doi":"10.1016/j.enbuild.2025.115592","DOIUrl":"10.1016/j.enbuild.2025.115592","url":null,"abstract":"<div><div>Net-zero energy buildings (NZEBs) are increasingly recognized as a key component of bottom-up energy transitions and decarbonization efforts. However, due to the variability in weather conditions driven by climate change, the actual annual performance of an NZEB may deviate from its optimal design targets. To address this challenge, daily energy management can play a crucial role in maintaining the intended energy balance. A major challenge in daily energy management is ensuring compliance with the annual zero-energy constraint. This paper introduces a novel hierarchical approach for NZEB energy management, integrating medium- and short-term energy management models into a two-layer framework. The upper layer allocates monthly energy consumption to individual loads, while the lower layer translates these allocations into daily boundary conditions. Within this framework, daily load scheduling is optimized to minimize energy costs and reduce residents’ discomfort. A data-driven approach is employed to model both controllable and uncontrollable loads based on residents’ lifestyles and climatic indicators. In particular, thermostatically controllable loads are predicted as a linear function of climatic variables, ensuring adaptive and efficient energy management. The proposed hierarchical approach is implemented in a fully electric, single-family residential NZEB located in Gaithersburg, Maryland, US. The results indicate that a traditional energy management algorithm fails to satisfy the annual zero-energy constraint when annual photovoltaic energy generation decreases to 90% of its current value due to climate change. In contrast, the proposed hierarchical approach successfully maintains the net-zero energy condition even when photovoltaic generation falls below 90% of the current level, while keeping residents’ discomfort within acceptable limits.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"336 ","pages":"Article 115592"},"PeriodicalIF":6.6,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143637746","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 probabilistic approach to assess electrical plug loads in healthcare facilities","authors":"Ehsan Mousavi ,&nbsp;Milad Jafari ,&nbsp;Walt Vernon ,&nbsp;Troy Savage ,&nbsp;Charlie Ruschke","doi":"10.1016/j.enbuild.2025.115591","DOIUrl":"10.1016/j.enbuild.2025.115591","url":null,"abstract":"<div><div>This paper examines the historical context and ongoing challenges associated with electrical load sizing in healthcare facilities, focusing on oversizing electrical distribution systems mandated by the National Electrical Code (NEC). While these codes have been designed to ensure safety and reliability, they often result in larger than necessary systems, particularly in healthcare settings where energy intensity is notably high. This study presents new evidence demonstrating that aggressive demand factors for receptacles—specifically, general 180 VA and dedicated 120-volt circuits—can be adjusted to reflect actual load conditions better. We utilized a probabilistic methodology to analyze more than six million electrical load readings collected on 1196 circuits in 14 hospitals in the United States. A total of 196 hospitals were chosen randomly across the United States, and due to the conceived data privacy, measurements were only carried out in facilities that agreed to participate. We present a probability model to analyze and recommend safe design requirements based on actual plug loads in the hospital. The findings suggest a convergence of demand factors as the number of circuits increases, showcasing up to 30% savings in copper and system sizing.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"336 ","pages":"Article 115591"},"PeriodicalIF":6.6,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628500","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":"Effects of window views on thermal comfort and health during moderate and high intensity exercise: A summer field experiment","authors":"Jiaxin Chen,&nbsp;Nianping Li,&nbsp;Fangning Shi,&nbsp;Wenrui Zheng,&nbsp;Yongga A","doi":"10.1016/j.enbuild.2025.115581","DOIUrl":"10.1016/j.enbuild.2025.115581","url":null,"abstract":"<div><div>Window views have been demonstrated to benefit physical and mental health of indoor personnels in office, so as to enhance their productivity. Whether window views have similar positive effects on indoor sports personnels is also worth exploring. This study conducted a summer field experiment in two gyms with and without window views to evaluate these effects. Twenty participants were required to walk on treadmills set to 4.5 km/h and 6 km/h to simulate moderate and high intensity exercise. Physiological parameters and psychological assessment were recorded during the experiment. The results indicate that when engaging in moderate and high intensity exercises, window views have positive effect on thermal response, with an improvement of 0.31 in thermal comfort ratings, along with a decrease of 0.29 in thermal sensation ratings. Furthermore, window views contribute to participants’ health during exercise, with reduction of 10.42 % and 8.28 % in dizziness, decreases of 4.04 % and 2.52 % in fatigue degrees for moderate and high intensity exercise respectively. In terms of emotions, the positive emotions were influenced more by dynamic mirror vision than natural views in exercise. The results of the study prove the positive effects of window views on indoor personnel’s thermal response and health during moderate and high intensity exercise. This study is beneficial to enrich theoretical research in dynamic thermal comfort and also provides a scientific basis to satisfy the demands of different application requirements of architectural design.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"336 ","pages":"Article 115581"},"PeriodicalIF":6.6,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620982","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 comprehensive review and future research directions of ensemble learning models for predicting building energy consumption","authors":"Zeyu Wang ,&nbsp;Yuelan Hong ,&nbsp;Luying Huang ,&nbsp;Miaocui Zheng ,&nbsp;Hongping Yuan ,&nbsp;Ruochen Zeng","doi":"10.1016/j.enbuild.2025.115589","DOIUrl":"10.1016/j.enbuild.2025.115589","url":null,"abstract":"<div><div>Ensemble learning has garnered increasing attention in building energy prediction over the past decade due to its exceptional predictive accuracy. However, there is a lack of systematic reviews that comprehensively analyze its current research status, limitations, and challenges, particularly in the context of large-scale practical applications. To address this gap, this review article systematically evaluates the application of ensemble learning models in building energy prediction. Using the PRISMA method, 82 relevant articles published between 2013 and 2024 in the Web of Science database were analyzed. The findings indicate that heterogeneous ensemble models, which integrate diverse algorithms, and homogeneous ensemble models, which utilize multiple data subsets, both hold significant potential for enhancing prediction accuracy. Specifically, heterogeneous models achieved accuracy improvements ranging from 2.59% to 80.10%, while homogeneous models demonstrated more stable improvements of 3.83% to 33.89%. Nonetheless, the integration of multiple base models increases computational complexity, resulting in higher computation times. Despite this drawback, the improved prediction accuracy, robustness, and generalization capabilities of ensemble models justify the additional computational cost. The review identifies key limitations, including the subjective selection of learning algorithms, the lack of systematic methods for evaluating model diversity, and insufficient exploration of combination strategies. Future research should focus on developing objective criteria for algorithm selection, advancing diversity evaluation techniques, analyzing the effects of combination methods, comparing computational efficiency, and validating the robustness and generalizability of ensemble models. This study offers valuable insights for researchers and practitioners aiming to optimize ensemble learning models in building energy prediction.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"335 ","pages":"Article 115589"},"PeriodicalIF":6.6,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610156","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":"Decarbonising non-domestic buildings at scale − A systematic review","authors":"Daniel Fosas ,&nbsp;Ozgur Gocer ,&nbsp;Arianna Brambilla ,&nbsp;Anastasia Globa ,&nbsp;Muhammed Yildirim ,&nbsp;Daniel Friedrich","doi":"10.1016/j.enbuild.2025.115587","DOIUrl":"10.1016/j.enbuild.2025.115587","url":null,"abstract":"<div><div>The imperative for decarbonization demands the swift realization of net-zero buildings by 2050. Significant efforts have been directed at new buildings but, looking ahead, there is a need to shift towards whole-life management given buildings’ extensive lifetimes. With this objective in mind, this paper delves into scalable strategies for decarbonizing building stocks by conducting a systematic literature review concentrated on the in-use life cycle stage of buildings. Its scope is non-domestic building portfolios, a complex, under-researched stock that features dedicated teams overseeing their maintenance and operation – a management approach ideally placed to address sustainability challenges. Recent literature is analysed according to assessment of building portfolios, intervention types, and rollout mechanisms. This is then complemented with non-academic literature and governmental initiatives worldwide. Findings highlight challenges in understanding the performance of buildings in sufficient detail to inform effective retrofit planning and financing interventions. Fabric-first approaches, while desirable for their multiple benefits beyond reduced environmental impacts, do not necessarily arise as the most economically competitive measures. In contrast, just understanding and optimising energy use is reported to deliver 20% energy savings cost-effectively. Overall, findings highlight challenges and opportunities associated with proactive management of buildings alongside areas for future research.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"336 ","pages":"Article 115587"},"PeriodicalIF":6.6,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143705865","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":"Conditional generative adversarial network (cGAN) for generating building load profiles with photovoltaics and electric vehicles","authors":"Yuewei Li ,&nbsp;Bing Dong ,&nbsp;Yueming Qiu","doi":"10.1016/j.enbuild.2025.115584","DOIUrl":"10.1016/j.enbuild.2025.115584","url":null,"abstract":"<div><div>Building load profiles are essential in research on building energy management, efficiency, demand response, and grid planning. With the growing adoption of solar photovoltaics (PV) and electric vehicles (EVs), integrated building load profiles are becoming increasingly important for effective management. However, current methods for generating building load profiles focus only on buildings without considering PV and/or EV adoptions. To address this gap, we propose using the conditional generative adversarial network (cGAN), a machine learning technique that generates realistic data conditioned on specific inputs, to create building load profiles that account for PV and EVs. This approach was tested using a smart meter dataset from a major metropolitan area in the southwest United States, containing years of hourly readings from 110 households with PV and EV adoptions. We extracted the key parameters that can describe the generated and real load profiles, and compared their mean and standard deviation to validate the results. KL divergence and FID scores were also used to compare the distributions. The results showed strong alignment between the generated and actual smart meter data across all PV, EV and seasonal conditions. The data under different combinations of PV, EV and weather conditions serve as conditional inputs for the cGAN, allowing it to generate building load profiles that maintain key statistical characteristics for both cooling and heating seasons, and various installation status of PV and EV. Additionally, this method safeguards customer privacy and reduces the effort needed for analyzing occupant behavior and building physics, which are typically required in physics-based energy models.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"335 ","pages":"Article 115584"},"PeriodicalIF":6.6,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591419","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":"Opto-electro-thermal analysis of semi-transparent perovskite solar cells applied to BIPV","authors":"Penghui Liu,&nbsp;Longxiang Wang,&nbsp;Jiapeng Wang,&nbsp;Yirong Zhai,&nbsp;Guiqiang Li","doi":"10.1016/j.enbuild.2025.115585","DOIUrl":"10.1016/j.enbuild.2025.115585","url":null,"abstract":"<div><div>Semi-transparent perovskite solar cells (ST-PSCs) are regarded as ideal for building-integrated photovoltaic (BIPV) applications due to their many advantages, but practical applications still face challenges, among which how to improve the stability and simultaneously increase the power conversion efficiency (PCE) and average visible transmission (AVT) values are the most critical. For the first time, this paper uses rigorous opto-electro-thermal coupling simulation to explain the energy conversion mechanism inside the ST-PSC device, quantify the contribution of heat generation of each internal part, and propose optimization methods for each part. By optimizing the device structure, the light utilization efficiency (LUE) value is increased from 1.28 % to 3.56 %, and the PCE and AVT of the device are 12.6 % and 28.26 % respectively. In addition, the ST-PSC heat transfer model applied to BIPV is proposed, and the theoretical operating temperature of the device is found to be 32.9 °C at the maximum LUE. On this basis, the back electrode was optimized to increase the LUE value to 3.99 %, proving that improving the transparency of the back electrode is a powerful way to get rid of the obvious negative correlation between PCE and AVT and significantly increase the LUE value. The day and night use of the device was also investigated, with efficiencies of more than 14 % maintained at night under the reverse illumination of an indoor light source, and efficiencies of up to 17.53 % in high color temperature environments. This study provides an exploration of the energy analysis and the equilibrium relationship between PCE and AVT for ST-PSC devices, which provides a strong guideline to promote the multifaceted application of ST-PSC in BIPV systems.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"335 ","pages":"Article 115585"},"PeriodicalIF":6.6,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591422","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":"Spatial distribution and influencing factors of data centers in China: An empirical analysis based on the geodetector model","authors":"Lei Wang ,&nbsp;Donglin Chen ,&nbsp;Mengdi Yao ,&nbsp;Guolong She","doi":"10.1016/j.enbuild.2025.115588","DOIUrl":"10.1016/j.enbuild.2025.115588","url":null,"abstract":"<div><div>Data centers are vital infrastructure for the digital economy’s growth. Analyzing the spatial distribution of data centers and the factors influencing this distribution can guide their sustainable and regionally balanced development. Using data from Chinese data centers between 2016 and 2022, this study employs the nearest neighbor index, geographic concentration index, imbalance index, kernel density estimation, and Anselin Local Moran’s I to quantitatively analyze the spatial distribution characteristics of data centers. Additionally, Geodetector and Pearson correlation analysis are used to identify factors that significantly correlate with the spatial distribution of data centers. The results indicate that: (1) Data centers exhibit clear agglomeration characteristics, forming a “dense east and sparse west” distribution pattern, with three cores in the Beijing-Tianjin-Hebei region, the Yangtze River Delta, and the Pearl River Delta. (2) Provincially, the spatial distribution of data centers shows a significant imbalance, with “high-low” clustering observed in Guangzhou and “high-high” clustering in Shanghai. (3) Multiple factors influence the spatial distribution, with computing demand and economic development showing the strongest correlations. Furthermore, data center distribution is shifting from solely pursuing economic benefits to taking into account both economic and environmental benefits. (4) Regional variations exist in influencing factors. In the eastern region, computing demand and economic development levels show the strongest correlations, while in the central and western regions, government financial support is more significantly correlated. Based on the analysis results, this study proposes specific recommendations for the development and distribution of data centers across various regions of China from the perspectives of policymakers and data center operators.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"336 ","pages":"Article 115588"},"PeriodicalIF":6.6,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143620981","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":"Innovative AI strategies for enhancing smart building operations through digital twins: A survey","authors":"Adel Oulefki ,&nbsp;Hamza Kheddar ,&nbsp;Abbes Amira ,&nbsp;Fatih Kurugollu ,&nbsp;Yassine Himeur ,&nbsp;Ahcene Bounceur","doi":"10.1016/j.enbuild.2025.115567","DOIUrl":"10.1016/j.enbuild.2025.115567","url":null,"abstract":"<div><div>The Digital Twins (DT) have emerged as a digital transformation automation process with ubiquitous applications that span various domains, including buildings, manufacturing, and healthcare. These virtual clones of physical systems provide relevant insights, enhance decision-making processes, and optimize operations, along with allowing the prediction of future operations. Artificial intelligence (AI) has been instrumental in enhancing the functionalities of DT. This survey paper explores recent developments in advanced AI algorithms tailored for DT in building settings. Moreover, a wide spectrum of AI techniques designed to address the challenges posed by DT in buildings are categorized and reviewed, including convolution neural networks (CNN), recurrent neural networks (RNNs), and generative adversarial networks (GANs), among other cutting edge transformative technologies. Furthermore, the integration of reinforcement learning (RL) and transfer learning (TL) into the DT ecosystem is discussed. This survey explores practical use cases, such as predictive scenarios, anomaly detection, and optimization of DT models. The incorporation of multimodal AI sensor data and edge computing in enhancing the accuracy and efficiency of DT is analyzed. Additionally, challenges and future directions in the field are explored, including data privacy concerns using Blockchain (BC), scalability issues, and the potential impact of quantum computing (QC) and large language models (LLMs) on DT technology. This comprehensive survey serves as a valuable resource for researchers, practitioners, and decision makers looking to utilize cutting-edge techniques to harness the full potential of DT technology in smart buildings (SB).</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"335 ","pages":"Article 115567"},"PeriodicalIF":6.6,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610282","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":"Effects of green façade retrofitting on thermal performance and energy efficiency of existing buildings in northern China: An experimental study","authors":"Sun Qi ,&nbsp;Nangkula Utaberta ,&nbsp;Allen Lau Khin Kiet ,&nbsp;Xu Yanfang ,&nbsp;Han Xiyao","doi":"10.1016/j.enbuild.2025.115550","DOIUrl":"10.1016/j.enbuild.2025.115550","url":null,"abstract":"<div><div>In recent years, vertical greenery systems have gradually entered public attention, and more and more people are beginning to know it. This study aims to explore the impact of green façade retrofitting on building thermal performance and building energy consumption under hot summer conditions in northern China and to derive detailed data for future building energy efficiency retrofitting. This article used a comparative experiment to complete this research. Four laboratories of the same structure were constructed in Shandong Province, China. A movable metal frame was installed on the outside of the laboratory, and the green façades could be adjusted to direct green façades or indirect green façades according to the need of the experiment. In addition, two different plants were studied. This study was carried out under two experimental conditions: cooling and no cooling. The experiment was carried out from July to August 2024. According to the data obtained from the experiment, the green façade well reduced the surface temperature of the walls around the experimental room and the average temperature level in the room. The most significant temperature drop of 23.1 °C was observed on the surface of the external walls of the room with the indirect green façade covered with Parthenocissus quinquefolia. The data show that the average temperature in several experimental rooms decreased by 1–5 °C. The indirect green façade improves the thermal insulation of the building envelope better than the direct green façade. In the cooling experiments, the room with indirect green façades covered with Parthenocissus quinquefolia has the highest energy-saving rate of 45.75 %. However, the room with direct green façades covered with Humulus scandens only has an energy saving rate of 6.43 %.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"335 ","pages":"Article 115550"},"PeriodicalIF":6.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591523","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}