Applied Energy最新文献_第4页

Bundling electric vehicles with off-grid wind power: A strategy for high-electricity-Price markets 将电动汽车与离网风力发电捆绑：高电价市场的策略

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-05-01 Epub Date: 2026-02-21 DOI: 10.1016/j.apenergy.2026.127564

Ke Gong , Shiyun Wang , Chu Xiong , Sidun Fang , Zhiwei Wang , Jian Hu , Yuqin Huang , Yuanxiang Dong

Persistently high electricity prices in markets like the European Union and Japan significantly impede electric vehicle (EV) adoption by eroding their operational cost advantage, thus jeopardizing transportation decarbonization targets. This study proposes a novel market-based strategy: Bundling EVs with off-grid wind energy generators (WEGs), grounded in product bundling theory. We develop a multi-agent sequential game model to analyze the interactions between a profit-maximizing manufacturer and cost-minimizing consumers, incorporating the dual uncertainties of wind generation and driving demand. The model is calibrated using real-world data from France and the United States. Results demonstrate that the bundling model promotes EV adoption when the utility premium outweighs the WEG cost, particularly under high electricity prices and favorable wind conditions. Specifically, by reducing effective charging costs through self-consumption in markets such as France and the United States, deploying a spatially optimized 2 kW WEG boosts EV adoption by 7–10% and increases corporate profits by 18–30% relative to standalone sales. Moreover, this strategy aligns economic and environmental objectives, achieving substantial life-cycle emission reductions of 25–28%. Consequently, this bundling strategy mitigates adoption barriers while concurrently leveraging EVs as storage and WEGs for off-grid generation to advance zero‑carbon transportation

在欧盟和日本等市场，持续的高电价侵蚀了电动汽车的运营成本优势，严重阻碍了电动汽车的普及，从而危及交通运输脱碳目标。本文以产品捆绑理论为基础，提出了一种新的市场策略：将电动汽车与离网风力发电机捆绑销售。考虑风力发电和驱动需求的双重不确定性，建立了一个多智能体序列博弈模型来分析利润最大化的制造商和成本最小化的消费者之间的相互作用。该模型使用来自法国和美国的真实数据进行校准。结果表明，当公用事业溢价超过WEG成本时，特别是在高电价和有利风力条件下，捆绑模式促进了电动汽车的采用。具体而言，在法国和美国等市场，通过自我消费降低有效充电成本，部署空间优化的2kw WEG可使电动汽车的采用率提高7-10%，并使企业利润相对于独立销售提高18-30%。此外，该战略将经济和环境目标结合起来，实现了25-28%的生命周期大幅减排。因此，这种捆绑策略减轻了采用障碍，同时利用电动汽车作为离网发电的储能和weg来推进零碳运输

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引用次数: 0

Optimal day-ahead scheduling for explicit demand response provision of a renewable energy hub with hot water preparation 带热水准备的可再生能源枢纽明确需求响应的最优日前调度

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-05-01 Epub Date: 2026-02-20 DOI: 10.1016/j.apenergy.2026.127562

Antonio Karneluti, Petar Lovrić, Mario Vašak

With the rising share of intermittent renewable energy in the energy mix, there is a growing need for the employment of demand side management. End consumption/production power grid nodes should, in the future, participate in the power system balancing market with the demand response service provision. This paper introduces a joint optimization framework for the day-ahead scheduling of a renewable energy hub that simultaneously determines nominal operational behaviour and bidirectional (positive and negative) explicit demand response capacity. The probabilities of flexibility contracting are considered in the objective function, such that the expectation of the operational cost for interaction with the power system is minimised. Scenarios of multiple activations during the day are taken into account through joint optimization in the worst-case sense of all possible flexibility activation scenarios. The formulation utilises Linear Programming (LP), ensuring global optimality and high computational efficiency. Case studies involving an industrial plant and a power-to-X hub demonstrate significant expected operational cost reductions of 31% and 17%, respectively, compared to standard deterministic scheduling. A free software tool that implements the outlined procedure is provided.

随着间歇性可再生能源在能源结构中的份额不断上升，需求侧管理的就业需求也越来越大。未来，终端消费/生产电网节点应参与电力系统均衡市场，提供需求响应服务。本文介绍了可再生能源枢纽日前调度的联合优化框架，该框架同时确定了标称运行行为和双向（正负）显式需求响应能力。在目标函数中考虑了柔性承包的概率，使得与电力系统交互的运行成本期望最小。在所有可能的柔性激活场景的最坏情况下，通过联合优化考虑了白天多个激活场景。该公式采用线性规划（LP），保证了全局最优性和较高的计算效率。涉及工业工厂和power-to-X集线器的案例研究表明，与标准的确定性调度相比，预期运营成本分别降低了31%和17%。提供了一个实现概述过程的免费软件工具。

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引用次数: 0

Defect engineering for high-performance lithium/sodium-ion capacitor electrodes: mechanisms, advances, and future perspectives 高性能锂/钠离子电容器电极的缺陷工程：机制、进展和未来展望

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-05-01 Epub Date: 2026-02-12 DOI: 10.1016/j.apenergy.2026.127529

Mingyuan Pang , Min Yang , Haohao Zhang , Zhen Kong , Laixin Hong , Yingxin Guo , Yen Leng Pak , Zifan Wang , Jiajia Ye , Jibin Song , Juan An

The increasing need for sophisticated energy storage solutions that integrate high energy density, high power density, and extended cycle life has propelled the advancement of hybrid devices like lithium/sodium-ion capacitors (LICs/SICs). These devices combine battery-type and capacitor-type electrodes to address the performance disparity between traditional batteries and supercapacitors. However, the fundamental kinetic imbalance and interfacial instability between Faradaic and non-Faradaic electrodes continue to pose significant challenges to realizing their complete potential. In this context, this review thoroughly outlines the latest advancements in defect engineering approaches aimed at improving the electrochemical performance of LICs and SICs electrode materials. Specifically, through the systematic tailoring of vacancies (such as oxygen, cation, and anion vacancies), heteroatom doping (including N, F, S), and interfacial defects, significant improvements have been made in electronic conductivity, ion diffusion, the introduction of additional active sites, and structural stability. Furthermore, the review examines the essential mechanisms by which these defects influence charge storage behavior and the interactions between electrodes and electrolytes, thereby highlighting their significance in enhancing pseudocapacitive contributions and reducing degradation. Finally, the review underscores ongoing challenges in defect control, scalability, and mechanistic understanding, while delineating future directions focused on achieving precise defect manipulation and practical application. By offering a comprehensive examination of defect-enabled material design, this review intends to steer the advancement of high-performance LICs and SICs that can satisfy the demands of future energy storage systems.

对集成高能量密度、高功率密度和延长循环寿命的复杂储能解决方案的需求日益增长，推动了锂/钠离子电容器（lic / sic）等混合设备的发展。这些装置结合了电池型和电容器型电极，以解决传统电池和超级电容器之间的性能差距。然而，法拉第电极和非法拉第电极之间的基本动力学不平衡和界面不稳定性继续对实现其全部潜力构成重大挑战。在此背景下，本文全面概述了旨在改善lic和sic电极材料电化学性能的缺陷工程方法的最新进展。具体来说，通过系统地裁剪空位（如氧、阳离子和阴离子空位）、杂原子掺杂（包括N、F、S）和界面缺陷，在电子导电性、离子扩散、引入额外的活性位点和结构稳定性方面取得了显著的改善。此外，本文还研究了这些缺陷影响电荷存储行为和电极与电解质之间相互作用的基本机制，从而强调了它们在增强假电容贡献和减少降解方面的重要性。最后，回顾强调了在缺陷控制、可伸缩性和机械理解方面正在进行的挑战，同时描绘了未来的方向，重点是实现精确的缺陷操作和实际应用。通过对缺陷材料设计的全面研究，本综述旨在引导高性能集成电路和集成电路的发展，以满足未来储能系统的需求。

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引用次数: 0

Cross-array fault diagnosis of photovoltaic arrays with different configurations based on endpoint-dense gram feature encoding and mixup-enhanced domain adversarial network 基于端点密集克特征编码和混合增强域对抗网络的不同配置光伏阵列交叉阵故障诊断

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-05-01 Epub Date: 2026-02-24 DOI: 10.1016/j.apenergy.2026.127572

Jiaqi Qu , Pengyuan Ma , Qiang Sun , Xiaogang Wu , Weigui Zhang , Zhao Yang Dong , Bin Li

Recently, photovoltaic (PV) arrays fault diagnosis technology has advanced rapidly. However, existing PV array fault diagnosis models typically rely on large datasets collected under specific array configurations. Operating on the premise that training and testing data follow the same distribution, these algorithms fail to address feature distribution discrepancies caused by varying array configurations, resulting in poor transferability and limited generalization when applied to unseen arrays with different structural topologies or PV modules. To address this, considering inter-array relationships, this study proposes a novel fault diagnosis method for cross-array scenarios, i.e., mixup-enhanced domain adversarial network (MDAN). To our knowledge, this study represents an early investigation into unsupervised model transfer across heterogeneous PV arrays to mitigate the resultant domain shifts. The method features three key innovations. First, a two-dimensional Gram feature matrix (2D-GFM) encoding method based on endpoint-dense resampling is designed to extract fault-related similarities from I-V curves. Second, a dual-objective adversarial framework is established, utilizing a Gradient Reversal Layer (GRL) to align feature distributions between the source (labeled) and target (unlabeled) domains. Third, a feature-wise mixup layer is integrated to enhance the decision boundary's robustness against inter-domain variations. Experimental results demonstrate that the proposed method effectively handles scenarios with extremely scarce, unlabeled target- domain samples and enables robust cross-domain transfer across arrays of diverse configurations (e.g., 3 × 4, 2 × 8, 5 × 5, etc.), outperforming existing methods in accuracy and reliability.

近年来，光伏阵列故障诊断技术发展迅速。然而，现有的光伏阵列故障诊断模型通常依赖于特定阵列配置下收集的大型数据集。这些算法在训练和测试数据遵循相同分布的前提下运行，无法解决不同阵列配置导致的特征分布差异，导致在应用于不同结构拓扑或光伏组件的未见阵列时，可移植性差，泛化能力有限。为了解决这一问题，考虑到阵列间的关系，本文提出了一种新的跨阵列故障诊断方法，即混合增强域对抗网络（MDAN）。据我们所知，这项研究代表了对跨异质光伏阵列的无监督模型转移的早期调查，以减轻由此产生的域移位。这种方法有三个关键的创新。首先，设计了一种基于端点密集重采样的二维Gram特征矩阵（2D-GFM）编码方法，从I-V曲线中提取故障相关相似度；其次，建立了一个双目标对抗框架，利用梯度反转层（GRL）对齐源（标记）和目标（未标记）域之间的特征分布。第三，集成特征混合层，增强决策边界对域间变化的鲁棒性。实验结果表明，该方法有效地处理了极其稀缺、未标记的目标域样本场景，并实现了不同配置阵列（如3 × 4、2 × 8、5 × 5等）的鲁棒跨域转移，在准确性和可靠性方面优于现有方法。

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引用次数: 0

Biogas-to-methane conversion via sorption-enhanced methanation: experimental evaluation of reactor configuration strategies 通过吸附强化甲烷化的沼气转化为甲烷：反应器配置策略的实验评估

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-05-01 Epub Date: 2026-02-25 DOI: 10.1016/j.apenergy.2026.127598

Laura Gómez, Isabel Martínez, Ramón Murillo

Producing synthetic natural gas (SNG) from renewable sources is crucial for decarbonising the energy system, as it provides a storable, dispatchable energy carrier that complements intermittent renewable electricity while enabling the substitution of fossil-derived natural gas through existing storage and distribution infrastructures. This study employed the sorption-enhanced methanation (SEM) process to upgrade biogas to high-purity methane, using a commercial Ni-based catalyst and an LTA zeolite (4 A). Experiments were conducted in a TRL-3 laboratory-scale fixed-bed reactor under three configurations, namely conventional, polytropic (poly-H₂ and poly-biogas) and stratified beds. The conventional configuration yielded the best overall performance, achieving 100 vol%. CH₄ purity, full CO₂ conversion and 100% selectivity under optimal conditions (225 °C, 9.5 bar and a H₂/CO₂ feed ratio of 4:1). Polytropic feeding reduced hot spots, but limited H₂O adsorption. This resulted in shorter pre-breakthrough times (24 min) and lower CH₄ purities (96.7–99.2 vol%.). The stratified configuration yielded similar conversions, but suffered from higher local temperatures (up to 287 °C) and shorter breakthrough times (26 min). Optimization of regeneration conditions confirmed complete zeolite recovery using a PSA step of 30 min with a purge flow of 400 Nl/h. Stability tests over 11 cycles demonstrated that the catalyst–adsorbent system maintained its kinetic and adsorptive properties, supporting the robustness of the SEM process. Overall, these findings validate SEM as a promising, scalable strategy for producing renewable methane from biogas.

利用可再生能源生产合成天然气（SNG）对于能源系统的脱碳至关重要，因为它提供了一种可储存、可调度的能源载体，补充了间歇性的可再生电力，同时通过现有的储存和分配基础设施实现了化石衍生天然气的替代。本研究采用吸附强化甲烷化（SEM）工艺，利用商业镍基催化剂和LTA沸石（4a）将沼气升级为高纯度甲烷。实验在TRL-3实验室规模的固定床反应器中进行，实验采用常规、多向（多h2和多沼气）和分层床三种配置。传统的配置产生了最好的整体性能，达到100vol %。在最佳条件下（225℃，9.5 bar， H2/CO2进料比为4:1），CH4纯度，CO2完全转化和100%选择性。多元加料减少了热点，但限制了H2O的吸附。这导致较短的突破前时间（24 min）和较低的CH4纯度（96.7-99.2 vol%）。分层结构产生了类似的转化，但受到更高的局部温度（高达287°C）和更短的突破时间（26分钟）的影响。通过对再生条件的优化，确定了在吹扫流量为400 Nl/h、PSA步骤为30 min的条件下完全回收沸石。经过11次循环的稳定性测试表明，催化剂-吸附剂体系保持了其动力学和吸附性能，支持了SEM过程的鲁棒性。总的来说，这些发现证实了SEM是一种有前途的、可扩展的从沼气中生产可再生甲烷的策略。

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引用次数: 0

Climate-aware capacity expansion planning for power grids exposed to heat waves 针对受热浪影响的电网的气候敏感容量扩张规划

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-05-01 Epub Date: 2026-02-24 DOI: 10.1016/j.apenergy.2026.127575

Berk Sahin, Erhan Kutanoglu

Climate change influences weather, with particular concern around rising temperatures, which in turn affect electric power supply and demand. These impacts require consideration of evolving weather patterns in power system planning. In addition to rising temperatures, extreme weather events, such as heat waves, drive even higher demand and have the potential to reduce available supply, making it significantly more challenging to satisfy the demand. Hence, long-term power system planning approaches may benefit from the inclusion of these extreme heat events. Capacity expansion planning is used to understand system changes needed to maintain a reliable grid against future demands and regulations. It can be used to plan for a future grid topology that is reliable under normal conditions during a future climate, and resilient in heat waves, expected to be more severe with a changing climate. In this paper, we integrate climate change effects on the power system into capacity expansion planning to consider resilience. We analyze the impact of climate change on capacity expansion decisions, considering its effects in both normal and extreme heat conditions. We account for projected demand growth due to population increases and electrification, as well as hourly and seasonal variations in supply and demand due to weather conditions. We consider two mathematical models: one for climate-aware capacity expansion planning, and one for the evaluation of the decisions of the planning model. We conduct our analysis considering two SSP (Shared Socioeconomic Pathways)-RCP (Representative Concentration Pathways) combinations, SSP1-RCP2.6 (SSP126) and SSP3-RCP7.0 (SSP370), representing optimistic and upper-middle emissions scenarios, respectively, and compare them against a case with no climate change. We test our proposed formulation on a synthetic test system representative of the Texas grid, compare the expansion needs across various climate change scenarios, and examine the differences in optimal expansion strategies. Our findings show that incorporating climate change into capacity expansion planning can significantly reduce load shedding during heat waves, with only a slight increase in expansion costs. Hence, our results suggest that planning under the severe climate change scenario may represent an effective strategy.

气候变化影响天气，尤其是气温上升，进而影响电力供应和需求。这些影响需要在电力系统规划中考虑不断变化的天气模式。除了气温上升，极端天气事件，如热浪，推动更高的需求，并有可能减少可用供应，使其更具有挑战性，以满足需求。因此，长期电力系统规划方法可能受益于这些极端高温事件的纳入。容量扩展规划用于了解系统变化，以根据未来的需求和法规维持可靠的电网。它可以用来规划未来的电网拓扑结构，在未来气候的正常条件下是可靠的，在热浪中是有弹性的，随着气候的变化，热浪预计会更加严重。在本文中，我们将气候变化对电力系统的影响纳入到扩容规划中，以考虑恢复力。我们分析了气候变化对产能扩张决策的影响，考虑了其在正常和极端高温条件下的影响。我们考虑了由于人口增长和电气化导致的预计需求增长，以及由于天气条件导致的供需每小时和季节性变化。我们考虑了两个数学模型：一个用于气候敏感的产能扩张规划，另一个用于规划模型决策的评估。我们考虑了两种SSP（共享社会经济路径）-RCP（代表性浓度路径）组合，SSP1-RCP2.6 （SSP126）和SSP3-RCP7.0 (SSP370)，分别代表了乐观和中上排放情景，并将它们与无气候变化的情况进行了比较。我们在一个代表德克萨斯州电网的综合测试系统上测试了我们提出的公式，比较了不同气候变化情景下的扩展需求，并检查了最佳扩展策略的差异。我们的研究结果表明，将气候变化纳入产能扩张规划可以显著减少热浪期间的负荷减少，而扩张成本仅略有增加。因此，我们的研究结果表明，在严峻的气候变化情景下进行规划可能是一种有效的策略。

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引用次数: 0

Short-term forecasting of energy production and consumption using extreme learning machine: A comprehensive MIMO based ELM approach 基于极限学习机的能源生产和消费短期预测：基于MIMO的综合ELM方法

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-05-01 Epub Date: 2026-02-26 DOI: 10.1016/j.apenergy.2026.127599

Cyril Voyant , Milan Despotovic , Luis Garcia-Gutierrez , Mohammed Asloune , Yves-Marie Saint-Drenan , Jean-Laurent Duchaud , Ghjuvan Antone Faggianelli , Elena Magliaro

A multiple-input multiple-output (MIMO) extreme learning machine (

ELM

) is introduced for short-term forecasting of seven grid variables in Corsica (France): total demand and generation from solar, wind, hydropower, thermal, bioenergy, and imports. Based on six years of hourly data, the model integrates sliding windows and cyclic time encodings to handle non-stationarity and seasonal effects without heavy preprocessing. At a 1-hour horizon, solar and thermal achieve

nRMSE

of

0.179

and

0.051

with

R^{2} > 0.98

, while total demand forecasts remain reliable up to 5 h ahead. Wind and bioenergy remain challenging due to high intrinsic variability, but overall accuracy is robust across sources. Compared with persistence and an

LSTM

configured under realistic tuning budgets,

MIMO - ELM

consistently improves skill, offering small but stable gains over Single-Input Single-Output models (

SISO

). Beyond accuracy, the closed-form solution ensures fast training and suitability for real-time updates, enabling potential use in online learning contexts. A key advantage of the

MIMO

formulation is internal coherence between aggregate demand and its components, an important requirement for operators. The methodology adapts to local constraints such as grid characteristics, resource availability, and market structures, ensuring transferability beyond the Corsican case. The study shows that a parsimonious approach such as

MIMO - ELM

can deliver forecasts that are accurate, coherent, and computationally efficient, providing a practical decision-support tool for energy management and renewable integration.

介绍了一种多输入多输出（MIMO）极限学习机（ELM），用于短期预测科西嘉岛（法国）的七个电网变量：太阳能、风能、水电、热能、生物能源和进口的总需求和发电量。基于6年每小时数据，该模型集成了滑动窗口和循环时间编码，以处理非平稳性和季节性影响，而无需进行大量预处理。在1小时内，太阳能和热能的nRMSE分别为0.179和0.051,R2>0.98，而总需求预测在未来5小时内仍然可靠。风能和生物能源仍然具有挑战性，因为它们具有很高的内在变异性，但总体精度在各种来源中都很稳定。与持久性和在实际调优预算下配置的LSTM相比，MIMO - ELM不断提高技能，提供比单输入单输出模型（SISO）小但稳定的增益。除了准确性之外，封闭形式的解决方案还确保了快速培训和实时更新的适用性，从而可以在在线学习环境中使用。MIMO公式的一个关键优势是总需求与其组成部分之间的内部一致性，这是运营商的一个重要要求。该方法适应当地的限制条件，如电网特征、资源可用性和市场结构，确保可转移性超越科西嘉案例。研究表明，像MIMO−ELM这样简洁的方法可以提供准确、连贯和计算效率高的预测，为能源管理和可再生能源整合提供实用的决策支持工具。

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引用次数: 0

Contrastive self-supervised learning for lightweight and automated fault detection and diagnosis in HVAC systems 对比自监督学习在暖通空调系统中的轻量化和自动化故障检测与诊断

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-05-01 Epub Date: 2026-02-18 DOI: 10.1016/j.apenergy.2026.127557

Yuan Gao , Zehuan Hu , Junichiro Otomo , Yan Ke

Heating, ventilation, and air conditioning (HVAC) systems often operate with scarce fault labels and limited computational resources, posing challenges for reliable fault detection and diagnosis (FDD). Existing FDD studies largely rely on fully supervised data or post-hoc alarm aggregation, treat FDD as static classification without considering temporal dependencies, and employ complex backbones without evaluating deployment efficiency. Moreover, common contrastive learning (CL) augmentations such as scaling or permutation violate HVAC physical constraints, erasing magnitude anomalies critical for diagnosis. To address these limitations, this study reframes HVAC FDD as a multivariate time-series representation learning problem and proposes a contrastive self-supervised framework coupling a lightweight temporal encoder with a compact classifier. A physics-consistent strategy—combining timestamp masking and partially overlapping cropping—constructs positive pairs without destroying magnitude or channel semantics, while a hierarchical dual contrastive loss aligns same-timestamp embeddings and separates cross-sequence states across multiple resolutions. The resulting encoder–SVM architecture explicitly targets deployability, achieving high diagnostic accuracy with up to 90–97% less memory and 20–25% faster training than Transformer baselines. Experiments on the MZVAV AHU dataset with rigorous day-level splits show consistent superiority over recurrent, linear, and Transformer-based models, improving diagnostic accuracy by 20–30% and macro-F1 by 40–50%. This work delivers a label-efficient, physics-consistent, and deployment-ready framework for automated FDD in real-time building management systems.

供暖、通风和空调（HVAC）系统通常在故障标签稀缺和计算资源有限的情况下运行，这给可靠的故障检测和诊断（FDD）带来了挑战。现有的FDD研究很大程度上依赖于完全监督的数据或事后报警聚合，将FDD视为静态分类而不考虑时间依赖性，并且在没有评估部署效率的情况下使用复杂的主干。此外，常见的对比学习（CL）增强，如缩放或排列，违反了HVAC物理限制，消除了对诊断至关重要的幅度异常。为了解决这些限制，本研究将HVAC FDD重新定义为一个多变量时间序列表示学习问题，并提出了一个耦合轻量级时间编码器和紧凑分类器的对比自监督框架。物理一致性策略-结合时间戳屏蔽和部分重叠裁剪-构建正对而不破坏幅度或信道语义，而分层对偶对比损失对齐相同时间戳嵌入并跨多个分辨率分离交叉序列状态。由此产生的编码器- svm架构明确地以可部署性为目标，在比Transformer基线减少90-97%的内存和20-25%的训练速度下实现高诊断准确性。在MZVAV AHU数据集上进行的实验显示，与循环、线性和基于变压器的模型相比，MZVAV AHU数据集具有一贯的优势，诊断准确率提高了20-30%，宏观f1提高了40-50%。这项工作为实时建筑管理系统中的自动化FDD提供了一个标签高效、物理一致和部署就绪的框架。

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引用次数: 0

Urban-scale estimation of window-to-wall ratio from street view imagery via computer vision for improved building energy modeling 通过计算机视觉从街景图像中估计城市尺度的窗墙比，以改进建筑能源建模

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-05-01 Epub Date: 2026-02-23 DOI: 10.1016/j.apenergy.2026.127549

Jaehyun Yoo , Sebin Choi , Donghyuk Yi , Sungmin Yoon

The window-to-wall ratio (WWR) is an important part of building behavior. However, WWR data is not publicly available in most countries. This study proposes a rapid and automated method for estimating the WWR of large-scale buildings using Google Street View (GSV) imagery and computer vision techniques. In contrast to conventional approaches based on field surveys, drawing analysis, or manual modeling, this study offers a scalable and efficient framework that can replace labor-intensive processes. WWR was estimated for 15,740 buildings in a metropolitan district of Seoul, and the entire process was completed in 6 h. The accuracy of WWR estimation was evaluated using manual labeling on 100 building images. As manual calculation of actual WWR is difficult at a large scale, its validity was indirectly assessed based on the change in UBEM accuracy. The estimated WWR showed a median of 17.0%, with significant variation across primary building uses. When all estimated values were used as input, UBEM prediction accuracy improved by 7.42%, increasing to 35.83% for buildings in which the inclusion of estimated WWR improved UBEM accuracy. When envelope area information was used in addition to WWR, prediction accuracy improved by 9.22% for all buildings and by 41.33% for those with improved UBEM accuracy. In the improved buildings, higher WWR led to greater improvements in prediction accuracy. The median improvement was 16.14% for the 30–40% WWR range, 21.49% for 40–50%, and 30.25% for WWR over 50%. Occlusion, glare, low contrast, and image stitching errors were major issues hindering accurate WWR estimation. In addition, the tolerance limits of these four issues were quantified. This study proposes a framework that automatically estimates WWR, incorporates it into UBEM, and indirectly validates the estimates through changes in UBEM accuracy, enhancing the accuracy of urban-scale energy modeling.

窗墙比（WWR）是建筑性能的重要组成部分。然而，WWR的数据在大多数国家都不是公开的。本研究提出了一种基于谷歌街景（GSV）图像和计算机视觉技术的快速、自动化估算大型建筑物WWR的方法。与基于实地调查、绘图分析或手工建模的传统方法相比，本研究提供了一个可扩展且有效的框架，可以取代劳动密集型流程。对首尔首都圈的15740栋建筑进行了WWR估算，整个过程在6小时内完成。对100幅建筑图像进行了人工标注，评估了WWR估算的准确性。由于人工在大尺度上难以计算实际水波比，因此基于UBEM精度的变化间接评估其有效性。估计用水量的中位数为17.0%，不同主要建筑物用途的用水量差异显著。当使用所有的估估值作为输入时，UBEM的预测精度提高了7.42%，对于包含估计的WWR的建筑物，UBEM的预测精度提高到35.83%。当围护线面积信息与WWR同时使用时，所有建筑物的预测精度提高了9.22%，而UBEM精度提高的建筑物的预测精度提高了41.33%。在改进的建筑物中，更高的WWR导致预测精度的更大提高。在30-40%的WWR范围内，中位改善率为16.14%，40-50%为21.49%，50%以上为30.25%。遮挡、眩光、低对比度和图像拼接错误是影响准确估计WWR的主要问题。此外，还对这四个问题的容忍极限进行了量化。本研究提出了一种自动估计WWR的框架，将其纳入UBEM，并通过UBEM精度的变化间接验证估算结果，提高了城市尺度能量模型的精度。

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引用次数: 0

Cryogenic closed-cycle linear engine integration for cold energy recovery in fuel cell trucks 用于燃料电池卡车冷能量回收的低温闭循环线性发动机集成

IF 11 1区工程技术 Q1 ENERGY & FUELS

Applied Energy

Pub Date : 2026-05-01 Epub Date: 2026-02-16 DOI: 10.1016/j.apenergy.2026.127551

Ugochukwu Ngwaka , Shunmin Zhu , Janie Ling-Chin , Kumar Vijayalakshmi Shivaprasad , Song Hu , Andrew Smallbone , Anthony Paul Roskilly

This study investigated the integration of a Cryogenic Closed-cycle Free-piston Linear Joule Engine Generator (CCFLJEG) into a 100-kW hydrogen fuel cell truck to recover and convert cryogenic hydrogen cold energy into electricity. At 100% load and a hydrogen flow rate of 1.4 g/s, the CCFLJEG produced up to 2.9 kW of additional electrical power. Approximately 27% of the liquid hydrogen (LH₂) regasification enthalpy was directly absorbed by helium in the cold heat exchanger, while a further 48.9% was indirectly utilised by enhancing hot-side energy recovery, giving an overall cold-energy utilisation of 76.2%. The system reduced annual energy demand from 131.04 MWh to 124.25 MWh (5.2% decrease), equivalent to a hydrogen saving of 333.3 kg. These results demonstrate that coupling CCFLJEG with fuel cell trucks provides an efficient pathway for exploiting cryogenic exergy while improving vehicle-scale energy efficiency. Economic analysis indicated that, when powered by green liquid hydrogen, the system achieved a net present value (NPV) of £23,355 and a payback time (PBT) of 0.7 years. With grey hydrogen, the PBT remained favourable at 1.4 years. Sensitivity analysis identified hydrogen purchase price as the most influential factor affecting NPV, while the capital cost of the CCFLJEG had the strongest influence on PBT. The findings indicated a positive outlook on the technical and economic viability of integrating CCFLJEG in fuel cell trucks, suggesting that it could offer a promising approach to improving energy efficiency and reducing hydrogen consumption in heavy-duty transport applications.

本研究将低温闭式循环自由活塞线性焦耳发动机发电机（CCFLJEG）集成到100千瓦氢燃料电池卡车中，以回收低温氢冷能并将其转化为电能。在100%负载和1.4 g/s的氢气流速下，CCFLJEG可产生高达2.9 kW的额外电力。大约27%的液氢（LH2）再气化焓被冷热交换器中的氦气直接吸收，另外48.9%通过加强热侧能量回收间接利用，总体冷能利用率为76.2%。该系统将年能源需求从131.04兆瓦时减少到124.25兆瓦时（减少5.2%），相当于节约了333.3公斤氢气。这些结果表明，将CCFLJEG与燃料电池卡车相结合，为利用低温火用提供了一条有效途径，同时提高了汽车规模的能源效率。经济分析表明，当采用绿色液态氢供电时，该系统的净现值（NPV）为23,355英镑，投资回收期（PBT）为0.7年。对于灰氢，PBT仍为1.4年。敏感性分析发现，氢气购买价格是影响NPV的最大因素，而CCFLJEG的资金成本对PBT的影响最大。研究结果表明，将CCFLJEG集成到燃料电池卡车上的技术和经济可行性具有积极的前景，这表明它可以为提高重型运输应用的能源效率和减少氢消耗提供一种有希望的方法。

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引用次数: 0