Energy and Buildings最新文献_第9页

Linear temperature model for rapid prediction of multi-scale urban thermal environments toward climate-resilient city design 面向气候适应型城市设计的多尺度城市热环境快速预测线性温度模型

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

Energy and Buildings

Pub Date : 2026-02-04 DOI: 10.1016/j.enbuild.2026.117117

Chen Ren , Hao-Cheng Zhu , Shi-Jie Cao

Urban heat island (UHI) effects, intensified by global climate change and rapid urbanization, significantly elevate urban air temperatures. This phenomenon leads to substantial increases in building energy consumption, aggravated urban carbon emission, and raised public health risks under extreme heat. Facing these multifaceted challenges, there is a pressing need to develop accurate and efficient tools for predicting non-uniform urban thermal environment to support the UHI mitigation towards climate-resilient city design. High-resolution computational fluid dynamics (CFD) required large expenses for practical application, prompting the exploration of alternative approach such as machine learning. Nevertheless, these data-driven methods still encounter challenge in extensive training data requirement and limited physical interpretability. To address these gaps, this study develops a linear temperature model (LTM) to rapidly predict urban temperature distributions by linear superposition of pre-computed thermal contributions from heat sources. The prediction model is systematically validated across three representative test cases at different urban scales, i.e., an isolated building, a neighborhood, and a real street block. Results showed that by largely improving computational efficiency, the LTM maintained high prediction accuracy in terms of mean absolute error (MAE), root mean square error (RMSE), and mean relative error (MRE). Compared to CFD simulations, the LTM achieved good prediction precision at the building scale (MAE = 0.11°C, RMSE = 0.22°C, MRE = 0.28%), the neighborhood scale (MAE = 0.12°C, RMSE = 0.17°C, MRE = 0.31%), and the street block scale (MAE = 0.29°C, RMSE = 0.41°C, MRE = 0.84%). This model can provide urban planners, designers as well as policymakers with a practical tool for rapid thermal impact assessment, thereby guiding the development of UHI mitigation and climate-resilient design strategies.

全球气候变化和快速城市化加剧了城市热岛效应，显著提高了城市气温。这一现象导致建筑能耗大幅增加，加剧了城市碳排放，增加了极端高温下的公共健康风险。面对这些多方面的挑战，迫切需要开发准确和有效的工具来预测不均匀的城市热环境，以支持缓解城市热岛影响，实现气候适应型城市设计。高分辨率计算流体动力学（CFD）在实际应用中需要大量的费用，这促使人们探索机器学习等替代方法。然而，这些数据驱动的方法仍然面临着大量训练数据需求和有限的物理可解释性的挑战。为了解决这些差距，本研究开发了一个线性温度模型（LTM），通过预先计算的热源热贡献的线性叠加来快速预测城市温度分布。该预测模型在三个不同城市尺度的代表性测试案例中进行了系统验证，即一个孤立的建筑，一个社区和一个真实的街道街区。结果表明，通过大幅提高计算效率，LTM在平均绝对误差（MAE）、均方根误差（RMSE）和平均相对误差（MRE）方面保持了较高的预测精度。与CFD模拟相比，LTM在建筑尺度（MAE = 0.11°C, RMSE = 0.22°C, MRE = 0.28%）、邻域尺度（MAE = 0.12°C, RMSE = 0.17°C, MRE = 0.31%）和街区尺度（MAE = 0.29°C, RMSE = 0.41°C, MRE = 0.84%）均取得了较好的预测精度。该模型可为城市规划者、设计师和决策者提供快速热影响评估的实用工具，从而指导城市热岛缓解和气候适应型设计战略的制定。

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引用次数: 0

Integrating life cycle assessment with GIS and AI for the building sector: a comprehensive review 建筑行业生命周期评估与GIS和人工智能的整合：全面回顾

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

Energy and Buildings

Pub Date : 2026-02-03 DOI: 10.1016/j.enbuild.2026.117095

Huili Xie, Shengping Li, Atiq Zaman, Yongze Song, Peng Wu

引用次数: 0

Non-intrusive occupant detection in a real environment: a pilot study in an educational test bed facility 真实环境中的非侵入式乘员检测：教育试验台设施的试点研究

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

Energy and Buildings

Pub Date : 2026-02-03 DOI: 10.1016/j.enbuild.2026.117107

Thomas Olsson , Myriam Aries

Building automation systems increasingly rely on advanced sensor technologies to improve energy efficiency and occupant comfort in indoor environments. This study investigates the real-world performance of a commercially available optical occupancy sensor with edge computing capabilities in a classroom setting. The sensor was tested under controlled conditions to evaluate its accuracy in detecting occupants based on spatial and human-related factors, including distance, posture, orientation, and clothing. Results showed that detection accuracy significantly declined with increased distance and when participants faced away from the sensor. The average detection accuracy was 61%, which is below the threshold typically required for reliable building automation. Exploratory regression analysis confirmed that proximity and orientation were the most influential factors affecting the probability of detection. The findings highlight the importance of strategic sensor placement and suggest that multi-sensor configurations may be necessary for effective coverage. Limitations include a small sample size, static testing conditions, and the lack of comparison with alternative technologies.

楼宇自动化系统越来越依赖于先进的传感器技术来提高能源效率和室内环境的舒适度。本研究调查了具有边缘计算功能的商用光学占用传感器在教室环境中的实际性能。该传感器在受控条件下进行了测试，以评估其基于空间和人类相关因素（包括距离、姿势、方向和服装）检测居住者的准确性。结果表明，随着距离的增加和受试者背对传感器时，检测精度显著下降。平均检测精度为61%，低于可靠的楼宇自动化通常所需的阈值。探索性回归分析证实，距离和方向是影响检测概率的最重要因素。研究结果强调了战略性传感器放置的重要性，并建议多传感器配置可能是有效覆盖所必需的。局限性包括样本量小，静态测试条件，以及缺乏与替代技术的比较。

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引用次数: 0

Designing glass materials for renewable-energy production through building integrated photovoltaics (BIPV) −A computational approach 通过建筑集成光伏（BIPV）设计可再生能源生产的玻璃材料-一种计算方法

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

Energy and Buildings

Pub Date : 2026-02-03 DOI: 10.1016/j.enbuild.2026.117108

Aqsa Aleem , Uzma Habib , Waqas Salman , Muhammad Tariq Saeed

Building Integrated Photovoltaics (BIPV) can transform buildings from passive energy consumers into active energy producers; however, BIPV glazing performance depends strongly on the thermal, optical, and mechanical properties of the glass materials. In practical BIPV applications, transparency and thermal stability act as prerequisite constraints, while photovoltaic (PV) efficiency is the primary optimization objective within the feasible design space. Existing studies typically evaluate these attributes independently and provide limited strategies for rapid composition optimization across large chemical spaces. To address this limitation, this work presents an integrated computational framework combining molecular dynamics (MD) simulations with a deep learning (DL) model to design and optimize multicomponent silicate glasses for BIPV applications. Using α-cristobalite SiO₂ as the base composition, the effects of Na₂O, CaO, and Al₂O₃ additives (5–50 %) on glass transition temperature (Tg), thermal conductivity, and PV cell efficiency were systematically investigated. MD simulations reveal that Na₂O acts as a strong network modifier, significantly reducing Tg at low concentrations, whereas CaO enhances mechanical stability and Al₂O₃ functions as a network intermediate. Increasing additive content leads to higher heat flux and thermal conductivity, enabling tunable thermal management to support improved PV performance. The predicted Tg values and thermal conductivity trends are consistent with reported literature, confirming the reliability of the simulation approach. The DL model, trained on melt–quench MD data, achieved high predictive accuracy for glass density, enabling rapid property prediction across composition–temperature–energy space. Error analysis and response surface mapping demonstrate strong model robustness with only localized deviations. Overall, the proposed MD–DL framework offers a rapid and cost-effective strategy for screening and tailoring glass compositions to balance structural and thermal stability with PV efficiency, accelerating the development of high-performance BIPV glazing for nearly zero-energy buildings.

建筑集成光伏（BIPV）可以将建筑物从被动的能源消费者转变为主动的能源生产者；然而，BIPV玻璃的性能在很大程度上取决于玻璃材料的热学、光学和机械性能。在实际BIPV应用中，透明度和热稳定性是先决条件，而光伏效率是可行设计空间内的首要优化目标。现有的研究通常是独立评估这些属性，并提供有限的策略来快速优化大型化学空间的成分。为了解决这一限制，本工作提出了一个集成的计算框架，将分子动力学（MD）模拟与深度学习（DL）模型相结合，以设计和优化用于BIPV应用的多组分硅酸盐玻璃。以α-方石英SiO2为基料，系统研究了Na2O、CaO和Al2O3添加剂（5 - 50%）对玻璃化转变温度（Tg）、导热系数和光伏电池效率的影响。MD模拟表明，Na2O作为一种强网络调节剂，在低浓度下显著降低Tg，而CaO增强了机械稳定性，Al2O3作为网络中间体。增加添加剂含量会导致更高的热流密度和导热系数，从而实现可调的热管理，以支持改进的PV性能。预测的Tg值和热导率趋势与文献报道一致，证实了模拟方法的可靠性。DL模型在熔融淬火MD数据上进行了训练，实现了对玻璃密度的高预测精度，实现了跨成分-温度-能量空间的快速性能预测。误差分析和响应面映射表明，模型具有较强的鲁棒性，只有局部偏差。总体而言，MD-DL框架为筛选和定制玻璃成分提供了一种快速且具有成本效益的策略，以平衡结构和热稳定性与光伏效率，加速高性能BIPV玻璃的发展，用于几乎零能耗的建筑。

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引用次数: 0

Data-Driven assessment of demand response potential of residential loads based on LSTM-WGCNA and Customer directrix load 基于LSTM-WGCNA和Customer directrix负荷的住宅负荷需求响应潜力数据驱动评估

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

Energy and Buildings

Pub Date : 2026-02-02 DOI: 10.1016/j.enbuild.2026.117101

Bo Peng, Baolin Cui, Xin Ma, Che Liu

Effectively identifying residential flexible resources is critical for carbon–neutral power systems. To address the complexity of user behavior, this paper proposes an integrated framework combining Long Short-Term Memory (LSTM) networks and Weighted Gene Co-expression Network Analysis (WGCNA). By encoding daily load profiles as dynamic gene expressions, the method constructs a topological overlap matrix to identify co-varying load patterns. A two-level assessment model based on Customer Directrix Load (CDL) is then established to quantify theoretical demand response potential. A case study on 200 households identify five typical usage patterns. The proposed method achieves a Davies-Bouldin Index of 2.24, which is superior to K-means at 2.84 and AE + K-means at 2.45. Furthermore, it improves cluster boundary separability by approximately 43%. Evaluation results quantify flexibility disparities, identifying Pattern C2, representing active evening users, as the highest potential group with a score of 0.58, and Pattern C5 as the lowest potential group with a score of 0.40. These findings provide actionable data support for aggregators to formulate differentiated flexibility allocation strategies.

有效识别住宅灵活资源对碳中和电力系统至关重要。为了解决用户行为的复杂性，本文提出了一个结合长短期记忆（LSTM）网络和加权基因共表达网络分析（WGCNA）的集成框架。该方法通过将日负荷谱编码为动态基因表达，构建拓扑重叠矩阵来识别共变负荷模式。建立了基于客户直接负荷（CDL）的两级评估模型，量化了理论需求响应潜力。一项针对200户家庭的案例研究确定了五种典型的使用模式。该方法的Davies-Bouldin指数为2.24，优于K-means的2.84和AE的 + K-means的2.45。此外，它将簇边界可分离性提高了约43%。评估结果量化了灵活性差异，确定模式C2（代表活跃的夜间用户）为最高潜力组，得分为0.58，模式C5为最低潜力组，得分为0.40。这些发现为聚合商制定差异化的灵活性分配策略提供了可操作的数据支持。

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引用次数: 0

Neighborhood-level analysis of building use and socio-economic effects on urban energy consumption 建筑使用与社会经济对城市能源消耗的影响

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

Energy and Buildings

Pub Date : 2026-02-02 DOI: 10.1016/j.enbuild.2026.117084

Jihyun Kang, Chaerin Kim, Albert Tonghoon Han

Urban energy consumption is unevenly distributed across neighborhoods, yet most studies in South Korea remain focused on national or metropolitan scales. This study examines neighborhood-level (dong-level) energy demand in Seoul by integrating building characteristics, socio-economic indicators, and infrastructural access. Using multivariate regression models for gas, electricity, and total energy use across 426 neighborhoods, we identify localized drivers of urban energy demand. Results show that apartments and townhouses are consistently associated with higher energy usage, while commercial and educational facilities such as cafés, restaurants, tutoring centers, and universities substantially increase electricity demand. Socio-economic variables including income, worker density, household size, and demographic dependency ratios produce varying effects, with lower consumption in some aging or dependent populations reflecting energy deprivation rather than efficiency. District heating access demonstrates a strong negative association with neighborhood gas use, underscoring the role of centralized infrastructure. These findings reveal how built environment, social structure, and energy infrastructure jointly shape intra-urban disparities in energy demand. We argue for neighborhood-scale, equity-sensitive energy governance as a complement to city-level policies.

通过综合建筑特征、社会经济指标和基础设施的可及性，估算首尔的能源需求。利用426个社区的天然气、电力和总能源使用的多元回归模型，我们确定了城市能源需求的局部驱动因素。结果显示，公寓和联排别墅的能源使用量一直较高，而咖啡厅、餐馆、辅导中心和大学等商业和教育设施的电力需求大幅增加。社会经济变量——包括收入、工人密度、家庭规模和人口抚养比——产生了不同的影响，一些老龄化或依赖人口的消费下降反映了能源匮乏，而不是效率。区域供热接入显示出与社区天然气使用的强烈负相关，强调了集中式基础设施的作用。这些发现揭示了建筑环境、社会结构和能源基础设施如何共同影响城市内部能源需求差异。我们主张将社区规模、公平敏感的能源治理作为全市政策的补充。

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引用次数: 0

Multi-phase optimization framework of residential blocks: Balancing building energy consumption, solar energy potential, and outdoor thermal comfort 住宅小区多相优化框架：平衡建筑能耗、太阳能潜力和室外热舒适

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

Energy and Buildings

Pub Date : 2026-02-01 DOI: 10.1016/j.enbuild.2026.117074

Siyao Wang, Hongwei Yang, Ye Zhang

The building energy efficiency and outdoor thermal comfort (OTC) in residential blocks are critical factors for the economic and social benefits of cities. Several studies have proposed frameworks for the optimal design of residential blocks targeting building energy efficiency and OTC. However, these studies have paid limited attention to the trade-off between these two objectives, and have placed less focus on the integration of solar energy potential (SEP). Therefore, this paper proposes a multi-phase optimization framework: first, obtaining Pareto optimal solutions that lower building energy consumption (BEC) and higher SEP; then, simulating OTC to inform second-phase decisions and selecting the final optimal design solutions. The overarching goal of this framework is to achieve co-optimization across energy supply, energy demand, and thermal comfort within residential blocks. To implement this approach, nine building group prototypes were identified in Wuhan, China, and 1000 samples were generated using Latin hypercube sampling. With the support of XGBoost and NSGA-II, prediction models were established between the layout of the nine group types and both BEC and SEP, yielding Pareto optimal solutions. Subsequent OTC simulations were conducted to guide the selection of the final optimal design solutions. These final solutions reduced BEC by 3.77%, increased SEP by 35.99%, and improved OTC by 33.35%. Overall, the results provide a decision-making basis for promoting high-quality urban construction in other climate zones and design solutions for comfort and efficiency in residential blocks.

住宅小区的建筑能效和室外热舒适是影响城市经济效益和社会效益的关键因素。一些研究提出了针对建筑能效和OTC的住宅区域优化设计框架。然而，这些研究对这两个目标之间的权衡关注有限，并且对太阳能潜力（SEP）的整合关注较少。为此，本文提出了一个多阶段优化框架：首先，求出较低建筑能耗（BEC）和较高SEP的Pareto最优解；然后，模拟OTC，为第二阶段决策提供信息，并选择最终的最优设计方案。该框架的总体目标是实现住宅街区内能源供应、能源需求和热舒适的协同优化。为了实现这一方法，在中国武汉确定了9个建筑群原型，并使用拉丁超立方体抽样生成了1000个样本。在XGBoost和NSGA-II的支持下，建立了9种组型布局与BEC和SEP之间的预测模型，得到了Pareto最优解。随后进行了OTC仿真，以指导最终最优设计方案的选择。这些最终溶液使BEC降低3.77%，使SEP提高35.99%，使OTC提高33.35%。总体而言，研究结果为促进其他气候区高质量的城市建设以及住宅街区的舒适和效率设计方案提供了决策依据。

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引用次数: 0

A new approach for HEMS to optimize cost, emissions and comfort through a smart integration of V2G, load scheduling, and PVT generation 通过V2G、负载调度和PVT发电的智能集成，HEMS优化成本、排放和舒适性的新方法

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

Energy and Buildings

Pub Date : 2026-02-01 DOI: 10.1016/j.enbuild.2026.117094

Rodrigo Fiorotti , Jussara F. Fardin , Helder R.O. Rocha , Augusto C. Rueda-Medina , Antonio M. Pantaleo , Mohammad R. Nasab , Sergio Bruno

This paper introduces a new Home Energy Management System (HEMS) strategy designed for Smart Homes that performs load scheduling and contains Photovoltaic/Thermal (PVT) generation and Electric Vehicle (EV) using two approaches: one designed for mono-directional EV charger and the other for bi-directional one, which can perform vehicle-to-grid (V2G). The problem is solved using the Non-dominated Sorting Genetic Algorithm III metaheuristic and Long Short-Term Memory to predict the PVT generation. The occupant behavior applied utilizes 3 feature parameters to define the usage profile of controllable loads and determine the periods in which the user is most adapted to using the equipment to quantify their comfort, in addition to modeling the dependency between the operation of some loads. The electrical and thermal loads are categorized into non-controllable, deferrable, and thermo-controllable. An annual case study shows an average cost reduction of 14.57% achieved by leveraging the flexibility of the bidirectional charger for similar values of emissions and user comfort. This reduction occurs by exploiting time-of-use tariffs (which lead to an average savings of 22.23%) and reducing the maximum demand (resulting in an average reduction of 21.73%). These savings are sufficient to offset the increase in battery losses and degradation costs to perform V2G. Finally, the comparison of various HEMS architectures highlights the advantages of EV adoption through V2G implementation, positioning EVs as a more competitive solution for promoting clean and affordable energy in residential buildings.

本文介绍了一种为智能家居设计的新的家庭能源管理系统（HEMS）策略，该策略执行负载调度，包含光伏/热（PVT）发电和电动汽车（EV），采用两种方法：一种设计用于单向电动汽车充电器，另一种设计用于双向电动汽车充电器，可以执行车辆到电网（V2G）。采用非支配排序遗传算法III元启发式长短期记忆法预测PVT生成。所应用的乘员行为利用3个特征参数来定义可控负载的使用概况，并确定用户最适合使用设备的时间段，以量化他们的舒适度，此外还对一些负载操作之间的依赖关系进行建模。电负荷和热负荷可分为不可控制负荷、可延迟负荷和热控制负荷。一项年度案例研究表明，利用双向充电器的灵活性，在相似的排放值和用户舒适度下，平均降低了14.57%的成本。这种减少是通过利用使用时间关税（平均节省22.23%）和减少最大需求（平均减少21.73%）来实现的。这些节省足以抵消电池损耗的增加和执行V2G的退化成本。最后，对各种HEMS架构的比较突出了通过V2G实施电动汽车的优势，将电动汽车定位为在住宅建筑中推广清洁和负担得起的能源的更具竞争力的解决方案。

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引用次数: 0

Low-carbon renovation of old residential blocks: An envelope-focused assessment framework for carbon reduction effects and economic benefits 旧住宅区建筑围护结构的低碳改造：碳减排效果和经济效益的综合评估框架

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

Energy and Buildings

Pub Date : 2026-02-01 DOI: 10.1016/j.enbuild.2026.117098

Shen Xu , Yichen Dong , Hongxin Guo , Zhen Yu , Bo Pan , Congyue Qi , Gaomei Li

Retrofitting building envelopes in the era of existing stock is an effective pathway to achieving carbon neutrality goals, requiring a comprehensive consideration of carbon reduction benefits and economic benefits. However, existing research focuses on the individual buildings and has not yet established a comprehensive assessment method at the block scale. This study aims to propose a multi-objective framework to assess the operational carbon reduction and economic benefits of retrofitting building envelopes in old residential blocks. First, this study employs architectural typology and cluster method to categorize 102 real cases of old residential blocks in Wuhan into three fundamental categories along with twelve derived variants. Second, under both single-component and multi-component retrofit scenarios, 2472 renovation schemes are generated. Then, sensitivity and variance analyses within the framework identify the key retrofit components and elements under each objective. Finally, the optimal renovation scheme sets for 3 types of old residential blocks are obtained, and the multi-level technical lists of envelope components-renovation materials-thermal parameters are formed. The proposed scheme sets and multi-level technical lists demonstrated good robustness after undergoing uncertainty analysis and comprehensive verification. The results indicated that multi-component retrofits enhance the carbon reduction effect by 9.25 to 23.95%, compared to single-component approaches. The heat transfer coefficient of external walls primarily affects the carbon reduction effects, while the heat transfer coefficient of external windows and the solar heat gain coefficient primarily affect economic benefits. In terms of comprehensive benefits, the heat transfer coefficient of exterior windows and that of exterior walls collectively serve as decisive factors. The technical framework proposed in this study enables policymakers and practitioners to rapidly assess the effectiveness of schemes and identify renovation priorities in the early stage of renovation. It provides a scalable technical pathway for decarbonizing old residential blocks worldwide, thereby supporting energy efficiency, emissions cuts, and sustainability within the building sector.

在存量时代对建筑围护结构进行改造是实现碳中和目标的有效途径，需要综合考虑减碳效益和经济效益。然而，现有的研究主要集中在单体建筑上，尚未建立起街区尺度上的综合评价方法。本研究旨在提出一个多目标框架，以评估在旧住宅楼宇中进行建筑围护结构改造的可操作性碳减排和经济效益。首先，本研究采用建筑类型学和聚类方法，将武汉市102个真实的老旧住区案例划分为3个基本类别和12个衍生变体。其次，在单组分和多组分改造场景下，共产生2472个改造方案。然后，在框架内进行敏感性和方差分析，确定每个目标下的关键改造部件和要素。最后，获得了3类老旧小区的最优改造方案集，形成了围护结构构件-改造材料-热力参数的多层次技术清单。经不确定性分析和综合验证，所提出的方案集和多层次技术清单具有较好的鲁棒性。结果表明，与单组分改造相比，多组分改造可提高9.25% ~ 23.95%的碳减排效果。外墙换热系数主要影响减碳效果，而外窗换热系数和太阳吸热系数主要影响经济效益。在综合效益方面，外窗换热系数和外墙换热系数共同起决定性作用。本研究提出的技术框架使政策制定者和实践者能够快速评估方案的有效性，并在改造的早期阶段确定改造的优先事项。它为世界范围内的旧住宅街区脱碳提供了可扩展的技术途径，从而支持建筑领域的能源效率、减排和可持续性。

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引用次数: 0

Estimation of building energy demand characteristics using bayesian statistics and energy signature models 基于贝叶斯统计和能源签名模型的建筑能源需求特征估计

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

Energy and Buildings

Pub Date : 2026-01-31 DOI: 10.1016/j.enbuild.2026.117075

Justinas Smertinas , Nicolaj Hans Nielsen , Matthias Y.C. Van Hove , Peder Bacher , Henrik Madsen

Energy Signature (ES) models are widely used in building energy performance assessment due to their simplicity, scalability, and physical interpretability. Nevertheless, conventional ES formulations are deterministic and provide limited insight into parameter uncertainty, constraining their value for robust performance evaluation and decision-making under real-world data variability.

This work addresses this gap by investigating how scalable Bayesian statistical inference can be systematically integrated into the ES framework to enable probabilistic, scalable assessments of building thermal performance at both individual building and building stock level. The research examines whether Bayesian ES models improve predictive performance while providing transparent uncertainty quantification for key thermal parameters, such as the effective heat transfer coefficient, solar gain, and wind infiltration.

A scalable Bayesian modelling framework is developed and applied to smart-meter data from 2788 Danish single-family houses. Three model variants are formulated and compared: a baseline ES model, an auto-regressive ES model (ARX-ES) capturing thermal inertia, and an auto-regressive moving average ES model (ARMAX-ES) approximating stochastic grey-box dynamics. The models estimate the effective heat transfer coefficients, solar gains, and wind infiltration, yielding full posterior distributions to reflect parameter uncertainty.

Results show that increased model complexity enhances one-step-ahead predictive performance, with the ARMAX-ES model achieving a median Bayesian R² of 0.94 across the building stock. At the single-building level, the yearly energy demand is estimated with credibility intervals within  ± 1%, showcasing more robust diagnostics than deterministic methods.

Overall, the proposed Bayesian ES framework enhances robustness and interpretability in building energy performance assessment, offering a scalable tool to complement energy certification, investment prioritisation, demand forecasting and data-driven energy planning.

能源签名（ES）模型因其简单、可扩展性和物理可解释性而广泛应用于建筑能效评估。然而，传统的ES公式是确定性的，对参数不确定性的了解有限，限制了它们在现实世界数据变异性下的稳健性能评估和决策的价值。本研究通过研究如何将可扩展的贝叶斯统计推断系统地集成到ES框架中，从而在单个建筑和建筑存量水平上对建筑热性能进行概率性、可扩展的评估，从而解决了这一差距。该研究考察了贝叶斯ES模型是否提高了预测性能，同时为关键热参数（如有效传热系数、太阳增益和风入渗）提供透明的不确定性量化。开发了一个可扩展的贝叶斯建模框架，并将其应用于2788个丹麦单户住宅的智能电表数据。提出并比较了三种模型变体：基线ES模型、捕获热惯性的自回归ES模型（ARX-ES）和近似随机灰盒动力学的自回归移动平均ES模型（ARMAX-ES）。模型估计有效传热系数、太阳能增益和风入渗，产生完全后验分布以反映参数的不确定性。结果表明，模型复杂性的增加提高了一步前的预测性能，ARMAX-ES模型在建筑存量中的中位数贝叶斯R²为0.94。在单个建筑水平上，每年的能源需求估计的可信区间在 ± 1%以内，显示出比确定性方法更稳健的诊断。总的来说，提议的贝叶斯ES框架增强了建筑能源绩效评估的稳健性和可解释性，提供了一个可扩展的工具来补充能源认证、投资优先级、需求预测和数据驱动的能源规划。

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引用次数: 0