Energy and Buildings最新文献_第10页

Thermal management performance enhancement of polyimide aerogel phase change composites and their application in fire early-warning 聚酰亚胺气凝胶相变复合材料热管理性能增强及其在火灾预警中的应用

IF 7.1 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2026-03-15 Epub Date: 2026-01-16 DOI: 10.1016/j.enbuild.2026.117014

Hongyang Li , Zhan Liu , Ming Niu , Changda Nie , Zelin Guo , Menghan Li , Zhonghao Rao

Phase change materials (PCMs) have exhibited significant potential for thermal management in buildings, but their widespread application is hindered by inherent drawbacks including leakage, low thermal conductivity, and flammability. To simultaneously address these issues, this study designed a novel multifunctional composite PCM based on polyimide (PI) aerogel. The PI aerogel was prepared by a polycondensation reaction into poly(amic acid), followed by freeze-drying and thermal imidization. Octadecane (OD) was then encapsulated within this matrix using vacuum impregnation to form the phase change composite (PI@OD). To improve the composite performance, amino-functionalized carbon nanotubes (ACNT) were integrated into the aerogel. The PI@OD composites were characterized via thermophysical property and microstructural tests. Results demonstrate that the PI@OD-5 (containing 5 wt% ACNT) exhibits insignificant leakage after 6 h heating at 80 ℃. It possesses a high latent heat of 163.3 J/g and shows a 47.19 % enhancement in thermal conductivity compared with the pristine PI aerogel. The composite also shows superior flame retardancy. Its peak heat release rate and total heat release are significantly reduced by 80.2 % and 68.1 % compared with pure OD. Moreover, the integration of ACNT enables an ultra-fast fire warning response within merely 3.5 s upon flame exposure, drastically outperforming conventional alarm systems (∼100 s). This work successfully integrates efficient thermal management, robust flame retardancy, and rapid fire-warning into a single composite, presenting a promising and innovative solution for developing energy-efficient and fire-safe building envelopes.

相变材料（PCMs）在建筑热管理方面显示出巨大的潜力，但其广泛应用受到其固有缺陷的阻碍，包括泄漏、低导热性和易燃性。为了同时解决这些问题，本研究设计了一种基于聚酰亚胺（PI）气凝胶的新型多功能复合PCM。以聚胺酸为原料，通过缩聚反应制备PI气凝胶，然后进行冷冻干燥和热亚酰化。然后利用真空浸渍将十八烷（OD）封装在该基体中，形成相变复合材料（PI@OD）。为了提高气凝胶的复合性能，将氨基功能化碳纳米管（ACNT）集成到气凝胶中。通过热物性和显微组织测试对PI@OD复合材料进行了表征。结果表明，PI@OD-5（含5 wt% ACNT）在80℃下加热6 h后，渗漏不明显。它具有163.3 J/g的高潜热，与原始PI气凝胶相比，导热系数提高了47.19%。该复合材料还表现出优异的阻燃性。与纯OD相比，其峰值放热率和总放热率分别降低了80.2%和68.1%。此外，ACNT的集成可在火焰暴露后仅3.5秒内实现超快速火灾警报响应，大大优于传统报警系统（约100秒）。这项工作成功地将高效的热管理、强大的阻燃性和快速火灾警报集成到一个复合材料中，为开发节能和防火的建筑围护结构提供了一个有前途的创新解决方案。

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引用次数: 0

TiN based thin film coatings for energy efficient glazing: experimental and simulation insights for sustainable building applications 用于节能玻璃的TiN基薄膜涂料：可持续建筑应用的实验和模拟见解

IF 7.1 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2026-03-15 Epub Date: 2026-01-20 DOI: 10.1016/j.enbuild.2026.117037

Sayan Atta , Joel Ashirvadam , Arun Tom Mathew , Sitaram Dash , Ariful Rahaman , Saboor Shaik , Uttamchand NarendraKumar

The growing energy demand in modern buildings, especially those with extensive glazing, underscores the need for energy-efficient solutions. This study explores the potential of magnetron-sputtered TiN mono and multilayer thin films to reduce air conditioning costs and promote sustainable building applications. Coatings were applied to glass substrates of varying thicknesses (4 mm, 6 mm, and 8 mm) and evaluated their optical, thermal, and environmental performance under the hot-dry climate of Vellore, TamilNadu, India. Surface characterization using AFM and FESEM revealed nano-hill structures with increased surface roughness in Ti/TiN multilayers, which enhanced light scattering. UV–VIS-NIR spectroscopy demonstrated that Ti/TiN films effectively blocked ultraviolet (UV) and near-infrared (NiR) radiation while maintaining high visible light transmittance. Spectroscopic ellipsometry highlighted substrate thickness-dependent variations in optical properties. The Ti/TiN film on a 6 mm glass substrate exhibited an optimal combination for low-E applications, balancing high infrared reflectance, visible light transmittance, and low UV penetration. Simulation studies using MATLAB and Design-Builder showed a 12.92% reduction in solar heat gain and improved indoor daylight distribution. Economic analysis indicated substantial reductions in air conditioning loads and electricity costs, with a payback period of 5–7 years. Environmental analysis quantified a significant reduction in carbon emissions, with Ti/TiN film on a 4 mm glass substrate capable of mitigating up to 290 kg CO₂/m² annually. These findings highlight TiN-based coatings as a scalable and cost-effective solution for enhancing energy efficiency, thermal comfort, and sustainability in modern buildings, particularly in regions with hot climatic conditions.

现代建筑不断增长的能源需求，特别是那些广泛使用玻璃的建筑，强调了对节能解决方案的需求。本研究探讨磁控溅射TiN单层和多层薄膜在降低空调成本和促进可持续建筑应用方面的潜力。在印度泰米尔纳德邦Vellore的干热气候下，将涂层涂在不同厚度（4毫米、6毫米和8毫米）的玻璃基板上，并评估其光学、热学和环境性能。利用AFM和FESEM对Ti/TiN多层膜进行表面表征，发现表面粗糙度增加的纳米山丘结构增强了光散射。紫外-可见-近红外光谱分析表明，Ti/TiN薄膜在保持较高的可见光透过率的同时，有效地阻挡了紫外（UV）和近红外（NiR）辐射。光谱椭偏强调了基片厚度在光学性质上的依赖变化。6毫米玻璃基板上的Ti/TiN薄膜表现出低e应用的最佳组合，平衡了高红外反射率、可见光透射率和低紫外线穿透率。利用MATLAB和Design-Builder进行的仿真研究表明，太阳能热增益降低了12.92%，室内日光分布得到改善。经济分析表明，空调负荷和电力成本大幅降低，投资回收期为5-7年。环境分析量化了碳排放的显著减少，在4毫米玻璃基板上的Ti/TiN膜每年能够减少高达290千克/平方米的二氧化碳。这些发现突出了tin基涂料作为一种可扩展且具有成本效益的解决方案，可提高现代建筑的能源效率、热舒适性和可持续性，特别是在气候条件炎热的地区。

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引用次数: 0

Good practices for documenting AI-based studies on energy and buildings 记录基于人工智能的能源和建筑研究的良好实践

IF 7.1 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2026-03-15 Epub Date: 2026-01-20 DOI: 10.1016/j.enbuild.2026.117043

Tianzhen Hong, Han Li

Artificial intelligence has transformed building science research over the past decade, with applications spanning energy modeling, energy prediction, HVAC optimization and controls, fault detection, and occupancy modeling. However, many studies lack adequate documentation of datasets, algorithms, training procedures, and validation methods. Building science research faces additional challenges including inconsistent evaluation metrics, limited generalizability across building types, climates, and significant gaps between experimental studies and deployed systems. This communication provides practical guidance for good practices in documenting and publishing AI-based research following established standards from the computer science and machine learning communities. By adopting frameworks such as Datasheets for Datasets, Model Cards, and standardized reproducibility checklists, researchers can ensure their work meets the rigorous documentation standards necessary for reproducible, comparable, and impactful building science research.

在过去的十年里，人工智能已经改变了建筑科学研究，其应用涵盖了能源建模、能源预测、暖通空调优化和控制、故障检测和占用建模。然而，许多研究缺乏足够的数据集、算法、训练程序和验证方法的文档。建筑科学研究还面临着其他挑战，包括不一致的评估指标，建筑类型、气候的有限通用性，以及实验研究和部署系统之间的显著差距。本交流为遵循计算机科学和机器学习社区的既定标准记录和发布基于人工智能的研究的良好实践提供了实用指导。通过采用数据集数据表、模型卡和标准化可重复性检查表等框架，研究人员可以确保他们的工作符合可重复、可比较和有影响力的建筑科学研究所需的严格文档标准。

引用次数: 0

A machine learning based rapid thermal performance modeling method for modular buildings with BIPV: A novel decomposition strategy with real-time prediction capabilities 基于机器学习的模块化建筑热性能快速建模：一种具有实时预测能力的新型分解策略

IF 7.1 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2026-03-15 Epub Date: 2026-01-26 DOI: 10.1016/j.enbuild.2026.117063

Yiqian Zheng , Biao Yang , Miaomiao Hou , Yi Zhang , Yuekuan Zhou , Xing Zheng , Pengyuan Shen

The global push for carbon neutrality has intensified the need for rapid and accurate energy prediction methods for BIPV-integrated modular buildings. Traditional physics-based simulation approaches suffer from excessive computational burden. This study presents a novel machine learning-based rapid energy prediction methodology specifically designed for modular buildings with building-integrated photovoltaics. A comprehensive feature engineering framework captures the unique thermal and geometric characteristics of modular construction through six-surface property encoding, geometric parameters, and solar irradiance calculations. The methodology employs a modular building decomposition strategy that enables individual module analysis while maintaining system-level accuracy. An XGBoost-based prediction model achieves superior performance across four representative climate zones. The model achieves R² values exceeding 0.93 for heating loads, cooling loads, and total energy consumption. Experimental validation using a real-world BIPV-integrated modular building demonstrates prediction accuracy within industry-acceptable limits, with mean absolute errors below 1.5°C. The computational efficiency assessment shows prediction speeds over 2,000 × faster than traditional simulation approaches, enabling real-time design iteration. Successful integration with Grasshopper parametric design platforms facilitates immediate energy feedback during conceptual design phases. This advancement removes computational barriers to energy performance optimization and supports the broader adoption of sustainable modular construction practices by providing practical tools for energy-informed design decision-making.

全球对碳中和的推动加强了对bipv集成模块化建筑快速准确的能源预测方法的需求。传统的基于物理的仿真方法存在计算量过大的问题。本研究提出了一种新的基于机器学习的快速能源预测方法，专门为具有建筑集成光伏的模块化建筑设计。通过六面属性编码、几何参数和太阳辐照度计算，一个全面的特征工程框架捕捉了模块化建筑独特的热学和几何特征。该方法采用模块化的建筑分解策略，在保持系统级准确性的同时支持单个模块分析。基于xgboost的预测模型在四个代表性气候带中实现了卓越的性能。该模型的热负荷、冷负荷和总能耗的R2值均超过0.93。使用现实世界bipv集成模块化建筑的实验验证表明，预测精度在行业可接受的范围内，平均绝对误差低于1.5°C。计算效率评估显示，预测速度超过2000 × 比传统的模拟方法快，实现实时设计迭代。与Grasshopper参数化设计平台的成功集成促进了概念设计阶段的即时能量反馈。这一进步消除了能源性能优化的计算障碍，并通过为能源设计决策提供实用工具，支持可持续模块化建筑实践的广泛采用。

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引用次数: 0

Adaptive thermostat preference learning using behaviour nudging and multi-armed bandits: A field implementation 自适应恒温偏好学习使用行为轻推和多武装强盗：现场实施

IF 7.1 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2026-03-15 Epub Date: 2026-01-19 DOI: 10.1016/j.enbuild.2026.117030

Hussein Elehwany , Andre Markus , Burak Gunay , Mohamed Ouf , Nunzio Cotrufo , Jean-Simon Venne , Junfeng Wen

Occupant behaviour (OB) centric controls have significant potential in advancing next-generation HVAC systems. Many OB-centric control studies solicit feedback from occupants to tackle the thermal preference learning problem. Behaviour nudging was also implemented in various systems to influence occupant behaviour to be more energy efficient. This study addresses the gap of using behaviour nudging and unsolicited occupant thermostat overrides to learn their thermal preferences. A multi-armed bandit (MAB) reinforcement learning (RL) was used to learn occupant thermal preferences from their thermostat interactions. The reward signal of the algorithm was designed to reward energy savings and penalize discomfort. The occupants were continuously nudged by slowly reducing the zone setpoint during the heating season, to encourage them to override the thermostats. The algorithm was implemented in two zones with multiple occupants in an academic facility in Ottawa, Canada, achieving energy savings of up to 12.7% compared to static setpoints.

以乘员行为（OB）为中心的控制在推进下一代HVAC系统中具有巨大的潜力。许多以ob为中心的控制研究征求居住者的反馈，以解决热偏好学习问题。在各种系统中也实施了行为推动，以影响乘员的行为，从而提高能源效率。本研究解决了使用行为轻推和未经请求的乘员恒温器覆盖来了解他们的热偏好的差距。使用多臂强盗（MAB）强化学习（RL）从他们的恒温器相互作用中学习乘员的热偏好。该算法的奖励信号被设计为奖励节能和惩罚不适。在供暖季节，通过缓慢降低区域设定值来不断推动居住者，以鼓励他们超越恒温器。该算法在加拿大渥太华的一个学术设施的两个区域中实施，与静态设定值相比，节能高达12.7%。

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引用次数: 0

Surrogate model evaluation and building energy benchmarking for commercial buildings 商业建筑替代模型评价与建筑能源标杆

IF 7.1 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2026-03-15 Epub Date: 2026-01-24 DOI: 10.1016/j.enbuild.2026.117033

Venkatesh Chinde , Rohit Chintala, Janghyun Kim, Alex Chapin, Jie Xiong, Katherine Fleming, Brian L. Ball

Building energy consumption benchmarking involves challenges associated with various energy patterns for different building types; heating, ventilating, and air-conditioning (HVAC) system types; and climates. Given significant variation in energy use patterns, accurate prediction of long-term energy use using surrogate models remains challenging. Multiple linear regression (MLR) is commonly used for building energy benchmarking because of its simple structure; however, it lacks accuracy compared to other black-box models. Although many studies have compared surrogate models and offer guidance on model selection based on metrics, they do not provide detailed analysis on improving the surrogate model accuracy. In this paper, we implement a surrogate model using polynomial ridge regression (i.e., MLR with interaction terms combined with ridge regularization) for small office and retail strip mall buildings across six HVAC system types and all climate zones, for electricity and natural gas in baseline and proposed scenarios. A simulation workflow is developed using OpenStudio™/EnergyPlus™ to generate simulation data using measures over a wide range of efficiency inputs. Enhancements based on statistical insights are used for improving the model accuracy using filters, input transformations, and change points. Surrogate models achieved average coefficient of variation of the root mean squared error (CVRMSE) values of 2.17, 1.06, 2.05, and 3.26 for proposed electricity, proposed natural gas, baseline electricity, and baseline natural gas, respectively, with enhancements reducing CVRMSE by an average of 14.9% across all combinations. We provide model interpretation via Shapley additive explanations to determine which input variables most influence energy consumption and provide supportive arguments for enhancements.

建筑能源消耗基准涉及不同建筑类型的不同能源模式所带来的挑战；供暖、通风和空调（HVAC）系统类型；和气候。鉴于能源使用模式的显著变化，使用替代模型准确预测长期能源使用仍然具有挑战性。多元线性回归（MLR）因其结构简单而被广泛应用于建筑能耗基准分析；然而，与其他黑箱模型相比，它缺乏准确性。尽管许多研究已经比较了代理模型，并提供了基于度量的模型选择指导，但它们并没有对如何提高代理模型的准确性进行详细的分析。在本文中，我们使用多项式岭回归（即，与交互项结合岭正则化的MLR）实现了一个代理模型，该模型适用于六种HVAC系统类型和所有气候带的小型办公和零售条形商场建筑，用于基线和建议场景中的电力和天然气。使用OpenStudio™/EnergyPlus™开发了一个仿真工作流程，通过广泛的效率输入来生成仿真数据。基于统计洞察力的增强用于使用过滤器、输入转换和更改点来提高模型准确性。替代模型对建议电力、建议天然气、基线电力和基线天然气的均方根误差（CVRMSE）的平均变异系数分别为2.17、1.06、2.05和3.26，在所有组合中，增强后的CVRMSE平均降低了14.9%。我们通过沙普利加性解释提供模型解释，以确定哪些输入变量最影响能源消耗，并为增强提供支持性论据。

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引用次数: 0

Effect of local heating on thermal responses of cleaning staff when working in an unheated space in winter and comparisons with the college students 局部供暖对冬季清洁人员在非供暖空间工作时热响应的影响及与大学生的比较

IF 7.1 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2026-03-15 Epub Date: 2026-01-21 DOI: 10.1016/j.enbuild.2026.117039

Yicheng Ren , Yuxin Wu , Yujie Zhou , Yuting Li , Shuang Zheng , Yonghong Wu

The cleaning staff plays a crucial role in maintaining the good condition of buildings. In southern China, they were frequently exposed to non-heated cold environments in winter, where a personal comfort system is needed. However, the study about their thermal comfort is insufficient. This study seeks to examine the impact of cold stress and local heating on the thermal responses of cleaning staff and college students during cleaning work under cold winter conditions. Thirty-two participants (16 cleaning staff and 16 college students) were recruited to perform cleaning activities in a semi-open corridor (9.2 °C, 52.5% RH) in winter (outside temperature: 5.4 °C). Three heating cases (head, hands, and feet heating modes) using heating sheets with adjustable power levels (max to 5.5 W / 2 sheets) were tested to compare with the no heating case. Each case lasted for 60 min and included three 20-minute periods: windless cleaning activities (initial period), windless rest (second period), and wind-exposed cleaning activities (third period). The results indicated that the cleaning staff group felt satisfied with the cold environments under the no heating case and insensitive to local heating. Head heating was unwanted in short-term (within 20 min) cold exposure because of the higher sensitivity of the head, and overheating at the head causes thermoregulation disorder. For students, feet heating resulted in significantly lower thermal sensation vote (TSV) compared to head or hands heating during the wind-exposed cleaning activities period (p < 0.01). For cleaning staff, feet heating was found to have no significant influence on overall TSV, while hands heating maintained the lowest blood pressure throughout the experiment. Hands heating consistently resulted in the highest average thermal pleasure throughout the experiment, with mean values of 0.47 for students and 0.97 for cleaning staff. Thus, hands heating with gloves was recommended for local heating of cleaning staff in cold, windy environments.

清洁人员在保持建筑物的良好状态方面起着至关重要的作用。在中国南方，他们经常在冬天暴露在没有暖气的寒冷环境中，在那里需要个人舒适系统。然而，对其热舒适性的研究尚不充分。本研究旨在探讨冷应激和局部加热对清洁人员和大学生在寒冷冬季清洁工作中的热反应的影响。研究招募了32名参与者（16名清洁人员和16名大学生），在冬季（室外温度：5.4°C）在一个半开放的走廊（9.2°C, 52.5% RH）进行清洁活动。测试了三种加热情况（头，手和脚加热模式），使用可调节功率水平的加热片（最大到5.5 W / 2片），并与无加热情况进行比较。每个病例持续60分钟，包括3个20分钟的时间段：无风清洁活动（第一阶段）、无风休息（第二阶段）和有风暴露的清洁活动（第三阶段）。结果表明：清洁人员组对无暖气情况下的寒冷环境较为满意，对局部暖气不敏感；头部加热是不希望在短期内（在20分钟内）冷暴露，因为头部的灵敏度较高，在头部过热会导致体温调节紊乱。对于学生来说，在风暴露的清洁活动期间，脚加热导致的热感觉投票（TSV）显著低于头或手加热（p < 0.01）。对于清洁人员来说，脚部加热对整体TSV没有显著影响，而手部加热在整个实验过程中保持最低的血压。在整个实验过程中，手部加热的平均热愉悦度最高，学生的平均值为0.47，清洁人员的平均值为0.97。因此，在寒冷、多风的环境中，建议清洁人员使用手套进行局部加热。

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引用次数: 0

Impact of battery storage on residential prosumers considering residential virtual power plants: an Australian case study 电池存储对考虑住宅虚拟发电厂的住宅产用户的影响：一个澳大利亚案例研究

IF 6.7 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2026-03-15 DOI: 10.1016/j.enbuild.2026.117323

Nameer Al Khafaf, Mahdi Jalili, Richardt Wilkinson, Brendan McGrath

As energy systems transition toward decentralization and decarbonization, residential prosumers are playing an increasingly vital role in grid stability and energy market participation. However, the economic viability of investing in battery energy storage systems (BESS) and joining virtual power plant (VPP) schemes remains uncertain, particularly under real-world operating conditions where battery dispatch is optimized for grid objectives rather than prosumer benefit. Although a growing body of research examines VPP optimization and grid-level services, relatively limited attention has been given to evaluating the economic feasibility of prosumer participation using empirical operational data.. This study addresses that gap by using empirical data from a residential VPP trial involving 750 Australian households to evaluate the economic performance and dispatch characteristics of residential battery systems under real VPP control. A clustering analysis is used to characterize battery operational patterns, and a techno-economic framework assesses investment viability across twelve scenarios that vary electricity tariff structures and battery degradation assumptions. Financial performance is evaluated using payback period, net present value, and internal rate of return. The results show that VPP participation combined with government incentives can reduce the payback period by up to five years and improve annual energy bill savings by approximately 20 percent. However, under low tariff conditions and without targeted incentives, residential battery adoption remains economically challenging. These findings highlight the importance of aligning VPP incentive structures with prosumer economic viability to enable sustained participation and realize broader system-level benefits.

随着能源系统向分散化和脱碳过渡，住宅产消户在电网稳定和能源市场参与方面发挥着越来越重要的作用。然而，投资电池储能系统（BESS）和加入虚拟电厂（VPP）计划的经济可行性仍然不确定，特别是在现实运行条件下，电池调度是为了电网目标而不是为消费者利益而优化的。尽管越来越多的研究考察了VPP优化和电网级服务，但利用经验操作数据评估产消参与的经济可行性的关注相对有限。本研究通过使用涉及750个澳大利亚家庭的住宅VPP试验的经验数据来评估真实VPP控制下住宅电池系统的经济性能和调度特征，从而解决了这一差距。聚类分析用于描述电池运行模式，技术经济框架评估了12种不同电价结构和电池退化假设的投资可行性。财务绩效是用投资回收期、净现值和内部回报率来评估的。结果表明，参与VPP与政府激励相结合，可以将投资回收期缩短长达五年，并将每年的能源账单节省约20%。然而，在低关税条件下，没有针对性的激励措施，住宅电池的采用仍然具有经济挑战性。这些发现强调了将VPP激励结构与生产消费者经济可行性相结合的重要性，以实现持续参与和更广泛的系统级效益。

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引用次数: 0

Surrogate models for evaluating HVAC retrofit options in multi-unit residential buildings 评价多单元住宅暖通空调改造方案的替代模型

IF 7.1 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2026-03-15 Epub Date: 2026-01-24 DOI: 10.1016/j.enbuild.2026.117060

Harry Vallianos, Costa Kapsis

This study investigates the use of surrogate modeling to optimize retrofit strategies in multi-unit residential buildings (MURBs), including HVAC systems. A comprehensive synthetic dataset was generated using EnergyPlus simulations, parameterized across a wide range of building and system variables, including ten distinct HVAC configurations. Multiple surrogate modeling approaches were evaluated, including single-output and multi-output artificial neural networks (ANNs) as well as Light Gradient Boosting Machine (LGBM) models. The models were trained to predict key performance metrics: Energy Use Intensity (EUI), Thermal Energy Demand Intensity (TEDI), and Cooling Energy Demand Intensity (CEDI). Results show that multi-output ANN models, with HVAC system as a categorical input, achieved high accuracy (R² > 0.997 and RMSE<2.2kWh/m²/year) and superior generalization compared to both single-output ANNs and LGBM models, while also reducing computational effort. The findings underscore the effectiveness of ANN-based surrogate models for rapid and accurate evaluation of retrofit scenarios involving diverse HVAC systems, supporting more efficient decision-making in building energy retrofits.

本研究探讨了在包括暖通空调系统在内的多单元住宅建筑（murb）中使用替代模型来优化改造策略。使用EnergyPlus模拟生成了一个综合数据集，参数化了广泛的建筑和系统变量，包括十种不同的暖通空调配置。评估了多种代理建模方法，包括单输出和多输出人工神经网络（ANNs）以及光梯度增强机（LGBM）模型。这些模型经过训练，可以预测关键性能指标：能源使用强度（EUI）、热能需求强度（TEDI）和冷却能源需求强度（CEDI）。结果表明，与单输出人工神经网络和LGBM模型相比，以暖通空调系统作为分类输入的多输出人工神经网络模型获得了更高的准确率（R2 >； 0.997和RMSE<；2.2kWh/m2/year）和更好的泛化，同时减少了计算量。研究结果强调了基于人工神经网络的替代模型的有效性，该模型可以快速准确地评估涉及不同HVAC系统的改造方案，从而支持更有效的建筑能源改造决策。

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引用次数: 0

Indoor thermal comfort and energy–saving opportunities in university classrooms: a field study across heating and cooling seasons 大学教室的室内热舒适和节能机会：跨供暖和制冷季节的实地研究

IF 7.1 2区工程技术 Q1 CONSTRUCTION & BUILDING TECHNOLOGY

Energy and Buildings

Pub Date : 2026-03-15 Epub Date: 2026-01-18 DOI: 10.1016/j.enbuild.2026.117005

Qiong He, Lu Han, Yayun Gan

Classroom thermal comfort is often compromised by a lack of localized data, leading to excessive cooling in summer and overheating in winter, resulting in huge energy waste. To meet students’ real thermal comfort needs and achieve the goal of energy saving in university classrooms, this study monitored the indoor thermal parameters in 35 different types of university classrooms and collected 1618 valid questionnaires in energy-consuming seasons in Nanjing with hot summer and cold winter climate. Key findings are as follows:(1) 77.8% and 12% of classrooms have above 60% humidity in summer and winter but the percentage of votes on “normal humidity” in these investigated classrooms is 67% and 68% in the same seasons, implying that most students generally prefer a more humid indoor environment in the area. (2) The gaps between

T_{n}

and

T_{p}

in summer and winter are 2.62°C and 2.0556°C, respectively, indicating that increase and decrease in classroom temperature can still ensure thermal comfort during summer and winter, respectively (3) Applying thermal comfort parameters

T_{p}

,

T_{n}

,

T_{A}^{s}

,

T_{A}^{a}

to maintain indoor environments can significantly reduce energy consumption based on values (19°C-25°C in winter and 22°C-26°C in summer) suggested by standards: the maximum energy savings can reach 4.5%, 13.7%, 15% and 18.1% in summer but up to 24.6%, 31.4%, 33.5% and 33.7% in winter, respectively. Thus, maintaining indoor thermal comfort according to real local demands rather than general specifications has great potential to save energy in university classrooms in Nanjing.

由于缺乏本地化数据，教室的热舒适往往受到影响，导致夏季过度冷却，冬季过热，造成巨大的能源浪费。为了满足学生的真实热舒适需求，实现高校教室节能的目标，本研究在夏热冬冷气候的南京地区，对35个不同类型的高校教室的室内热参数进行了监测，收集了1618份有效问卷。主要研究结果如下：(1)在夏季和冬季，77.8%和12%的教室湿度在60%以上，但在同一季节，被调查教室中“正常湿度”的投票比例分别为67%和68%，这意味着大多数学生普遍倾向于该地区更潮湿的室内环境。(2)夏季和冬季Tn与Tp的差值分别为2.62°C和2.0556°C，表明提高和降低教室温度在夏季和冬季仍能保证热舒适。(3)根据标准建议的值（冬季19°C-25°C，夏季22°C-26°C），应用热舒适参数Tp、Tn、TAs、TAa来维持室内环境，可显著降低能耗：夏季节能最高可达4.5%、13.7%、15%和18.1%，冬季节能最高可达24.6%、31.4%、33.5%和33.7%。因此，根据当地的实际需求而不是一般规格来保持室内热舒适，在南京大学教室节能方面具有很大的潜力。

{"title":"Indoor thermal comfort and energy–saving opportunities in university classrooms: a field study across heating and cooling seasons","authors":"Qiong He, Lu Han, Yayun Gan","doi":"10.1016/j.enbuild.2026.117005","DOIUrl":"10.1016/j.enbuild.2026.117005","url":null,"abstract":"<div><div>Classroom thermal comfort is often compromised by a lack of localized data, leading to excessive cooling in summer and overheating in winter, resulting in huge energy waste. To meet students’ real thermal comfort needs and achieve the goal of energy saving in university classrooms, this study monitored the indoor thermal parameters in 35 different types of university classrooms and collected 1618 valid questionnaires in energy-consuming seasons in Nanjing with hot summer and cold winter climate. Key findings are as follows:(1) 77.8% and 12% of classrooms have above 60% humidity in summer and winter but the percentage of votes on “normal humidity” in these investigated classrooms is 67% and 68% in the same seasons, implying that most students generally prefer a more humid indoor environment in the area. (2) The gaps between <span><math><mrow><msub><mi>T</mi><mi>n</mi></msub></mrow></math></span> and <span><math><mrow><msub><mi>T</mi><mi>p</mi></msub></mrow></math></span> in summer and winter are 2.62°C and 2.0556°C, respectively, indicating that increase and decrease in classroom temperature can still ensure thermal comfort during summer and winter, respectively (3) Applying thermal comfort parameters <span><math><mrow><msub><mi>T</mi><mi>p</mi></msub></mrow></math></span>, <span><math><mrow><msub><mi>T</mi><mi>n</mi></msub></mrow></math></span>, <span><math><mrow><msubsup><mi>T</mi><mrow><mi>A</mi></mrow><mi>s</mi></msubsup></mrow></math></span> , <span><math><mrow><msubsup><mi>T</mi><mrow><mi>A</mi></mrow><mi>a</mi></msubsup></mrow></math></span> to maintain indoor environments can significantly reduce energy consumption based on values (19°C-25°C in winter and 22°C-26°C in summer) suggested by standards: the maximum energy savings can reach 4.5%, 13.7%, 15% and 18.1% in summer but up to 24.6%, 31.4%, 33.5% and 33.7% in winter, respectively. Thus, maintaining indoor thermal comfort according to real local demands rather than general specifications has great potential to save energy in university classrooms in Nanjing.</div></div>","PeriodicalId":11641,"journal":{"name":"Energy and Buildings","volume":"355 ","pages":"Article 117005"},"PeriodicalIF":7.1,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145995553","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}

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