Advances in Applied Energy最新文献_第2页

Life cycle CO2e intensity of power-to-liquid sustainable aviation fuel scenarios and specific use cases 动力-液体可持续航空燃料方案和具体用例的生命周期二氧化碳当量强度

IF 13.8 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-10-05 DOI: 10.1016/j.adapen.2025.100248

Aron Bell , Liam Anthony Mannion , Mark Kelly , Robert Parker , Mohammad Reza Ghaani , Stephen Dooley

The life cycle carbon dioxide equivalent (CO₂e) intensity of Power-to-Liquid (PtL) sustainable aviation fuel (SAF) scenarios in Spain are evaluated using a specific, granular, and transparent modelling approach. Post combustion CO₂ capture and direct air CO₂ capture are considered, in addition to grid and renewable electricity sources. The mass and energy requirements of the PtL system are determined from a mass and energy conserved reaction mechanism and a comprehensive literature review. The SAF yield is constrained by its molecular composition, formulated to meet the physical property specifications for Fischer-Tropsch synthetic paraffinic kerosene (FT-SPK) in ASTM D7566 Annex 1. The results of the life cycle assessment (LCA) show large ranges in CO₂e intensity of PtL SAF scenarios, from 11 to 101 gCO₂e/MJ. The electricity emission factors at which the CO₂e intensity of PtL SAFs meet the 70% reduction required under the ReFuelEU Aviation legislation are 112 – 168 gCO₂e/kWh for direct air capture and post combustion capture of biogenic CO₂. As the average EU grid is approximately 300 gCO₂e/kWh, the use of renewable electricity (onsite or power purchase agreement) is therefore essential to achieve the 70% reduction. The carbon intensity of the Madrid to Dublin commercial flight route is analysed, per revenue-passenger-kilometre (RPK), as a specific use case with actual data of Ryanair Boeing 737-800 and 737 MAX 8 aircraft. Compared to the Science Based Targets 1.5°C limit of 3.3 gCO₂/RPK, it is shown that sustainable aviation is challenging using PtL SAF, with a best case of 9 gCO₂/RPK.

采用一种具体的、颗粒状的、透明的建模方法，对西班牙可持续航空燃料（SAF）方案的生命周期二氧化碳当量（CO2e）强度进行了评估。除了电网和可再生电力来源外，还考虑了燃烧后二氧化碳捕获和直接空气二氧化碳捕获。PtL系统的质量和能量需求是根据质量和能量守恒的反应机理和全面的文献综述确定的。SAF产率受其分子组成的限制，其配方符合ASTM D7566附件1中费托合成石蜡煤油（FT-SPK）的物理性能规范。生命周期评价（LCA）的结果表明，PtL - SAF情景的CO2e强度变化幅度较大，在11 ~ 101 gCO2e/MJ之间。在直接空气捕获和燃烧后捕获生物源二氧化碳的情况下，PtL saf的二氧化碳当量强度达到燃料燃料航空立法要求的70%的电力排放因子为112 - 168 gCO2e/kWh。由于欧盟电网的平均排放量约为300克二氧化碳当量/千瓦时，因此使用可再生电力（现场或电力购买协议）对于实现70%的减排至关重要。以瑞安航空波音737-800和737 MAX 8飞机的实际数据为例，分析了马德里至都柏林商业航线的碳强度，每收入乘客公里（RPK）。与基于科学的1.5°C目标3.3 gCO2/RPK相比，使用PtL SAF的可持续航空具有挑战性，最佳情况下为9 gCO2/RPK。

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引用次数: 0

Advanced fault diagnosis in batteries: Insights into fault mechanisms, sensor fusion, and artificial intelligence 电池的高级故障诊断：故障机制、传感器融合和人工智能

IF 13.8 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-10-03 DOI: 10.1016/j.adapen.2025.100247

Kailong Liu , Shiwen Zhao , Yu Wang , Kang Li , Jiayue Wang , Yaojie Sun , Qiuwei Wu , Qiao Peng

With the increasing demand for sustainable and clean energy, lithium-ion batteries have emerged as one of the most essential energy storage technologies. However, safety concerns have become a major bottleneck, significantly constraining their widespread deployment. This highlights the critical need for efficient fault diagnosis to ensure the safe and reliable operation of battery systems. In recent years, artificial intelligence (AI) techniques, in combination with advanced sensing technologies, have attracted growing attention for battery fault diagnosis and prognosis. Nevertheless, their full potential and broad applicability remain underexplored. This review provides a systematic analysis of the integration of AI methodologies with advanced sensors, emphasizing their capabilities for accurate fault detection and prediction, while also identifying key challenges and future research directions in this evolving field. The study begins by outlining common battery fault types and their underlying mechanisms, offering a foundational understanding of the associated complexities. It then introduces state-of-the-art AI techniques applied in fault diagnosis. Then, recent advances in combining AI with advanced sensing technologies for battery diagnostics are examined. Finally, the limitations of current approaches are discussed, and promising directions are proposed to facilitate the development of intelligent, scalable, and robust fault diagnosis frameworks for lithium-ion battery systems.

随着人们对可持续和清洁能源的需求日益增长，锂离子电池已成为最重要的储能技术之一。然而，安全问题已经成为主要的瓶颈，极大地限制了它们的广泛部署。因此，高效的故障诊断是保证电池系统安全可靠运行的关键。近年来，人工智能技术与先进的传感技术相结合，在电池故障诊断与预测方面受到越来越多的关注。然而，它们的全部潜力和广泛适用性仍未得到充分开发。本文对人工智能方法与先进传感器的集成进行了系统分析，强调了其准确故障检测和预测的能力，同时也确定了这一不断发展的领域的关键挑战和未来研究方向。该研究首先概述了常见的电池故障类型及其潜在机制，为相关复杂性提供了基本的理解。然后介绍了应用于故障诊断的最先进的人工智能技术。然后，研究了将人工智能与先进传感技术相结合用于电池诊断的最新进展。最后，讨论了当前方法的局限性，并提出了有希望的方向，以促进智能，可扩展和健壮的锂离子电池系统故障诊断框架的发展。

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引用次数: 0

Prediction-based control of energy storage systems using dynamic accuracy weighting 基于动态精度加权的储能系统预测控制

IF 13.8 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-09-29 DOI: 10.1016/j.adapen.2025.100246

Xiao Wang , Xue Liu , Xuyuan Kang , Fu Xiao , Da Yan

Integrating domain knowledge into artificial intelligence models is increasingly recognized as essential for improving energy storage system control based on load predictions. Commonly used accuracy metrics for load prediction models, such as mean absolute percentage error, coefficient of variation of mean absolute error, and coefficient of variation of root mean squared error, are not monotonically correlated with final control performance; in other words, the model with the highest prediction accuracy does not necessarily yield optimal control outcomes. This study introduces a dynamically weighted error metric, which incorporates the attributes of energy storage systems and the temporal dynamics of prediction-based control by leveraging domain knowledge from heating, ventilation, and air conditioning systems. The proposed dynamically weighted error metric enhanced the selection of load prediction models, and these models reduced the operating cost of six energy storage systems by up to 6.5 % compared to those using traditional prediction accuracy metrics. The scalability of dynamically weighted error metric was further validated across 10 energy storage capacities and 18 Time-of-Use tariffs in the six building cases, achieving 93.9 %–97.2 % of the ideal cost reductions and outperforming traditional metrics (86.4 %–95.4 %). The applicability of dynamically weighted error metric to common energy storage systems is discussed and confirmed. Additionally, a web-based tool was developed to facilitate dynamically weighted error calculation in practical applications. This study demonstrates that incorporating domain knowledge through dynamic accuracy weighting evidently enhances the whole-process performance of artificial intelligence in energy storage system control.

将领域知识集成到人工智能模型中，对于改善基于负荷预测的储能系统控制至关重要。负荷预测模型常用的精度指标，如平均绝对百分比误差、平均绝对误差变异系数和均方根误差变异系数，与最终控制性能不是单调相关的；换句话说，具有最高预测精度的模型并不一定产生最优的控制结果。本研究引入了一个动态加权误差度量，该度量通过利用供热、通风和空调系统的领域知识，结合了储能系统的属性和基于预测的控制的时间动态。提出的动态加权误差度量增强了负荷预测模型的选择，与使用传统预测精度度量相比，这些模型可将6个储能系统的运行成本降低6.5%。动态加权误差指标的可扩展性在6个建筑案例的10个储能容量和18个使用时间关税中得到进一步验证，实现了93.9% - 97.2%的理想成本降低，优于传统指标（86.4% - 95.4%）。讨论并验证了动态加权误差度量在普通储能系统中的适用性。此外，还开发了一个基于web的工具，以便在实际应用中进行动态加权误差计算。研究表明，通过动态精度加权方法引入领域知识，可以明显提高人工智能在储能系统控制中的全过程性能。

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引用次数: 0

Price formation and intersectoral distributional effects in a fully decarbonised European electricity market 完全脱碳的欧洲电力市场中的价格形成和部门间分配效应

IF 13.8 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-09-25 DOI: 10.1016/j.adapen.2025.100245

Silke Johanndeiter , Niina Helistö , Juha Kiviluoma , Valentin Bertsch

Future power supply will be dominated by solar and wind energy with near zero variable costs. Hence, wholesale market prices could frequently drop near zero. We use a sector-coupled power system model to optimise scenarios of a fully decarbonised European electricity market with a high penetration of variable renewables. Resulting electricity prices exceed near zero levels throughout most hours of the year as they are predominantly determined by the opportunity costs of cross-sectoral demand, particularly electrolysers. Consequently, even in markets with a high penetration of variable renewables, electricity prices continue to be driven by fuel costs, as they determine the opportunity costs of a price-setting demand. We find market actors in different sectors to be heterogeneously exposed to associated price risks. Price-responsive electricity demand can mitigate cost increases, while investors in variable renewables and inflexible electricity consumers are similarly exposed to revenue and cost risks. Thus, they could mutually benefit from risk-mitigating instruments. Conversely, our results indicate that hydrogen producers and consumers do not share such a common interest as hydrogen consumers’ final energy consumption costs vary more across scenarios and countries than electrolysers’ profits due to their role as price-setters.

未来的电力供应将以太阳能和风能为主，其可变成本接近于零。因此，批发市场价格可能经常降至接近零的水平。我们使用一个部门耦合电力系统模型来优化完全脱碳的欧洲电力市场的情景，其中可变可再生能源的渗透率很高。因此，在一年中的大部分时间里，电价都超过接近零的水平，因为它们主要取决于跨部门需求的机会成本，特别是电解槽。因此，即使在可变可再生能源渗透率较高的市场，电价也继续受到燃料成本的驱动，因为燃料成本决定了定价需求的机会成本。我们发现，不同行业的市场参与者面临的相关价格风险是不同的。价格敏感型电力需求可以缓解成本上涨，而可变可再生能源的投资者和不灵活的电力消费者同样面临收入和成本风险。因此，它们可以从降低风险的工具中相互受益。相反，我们的研究结果表明，氢气生产商和消费者并没有共同的利益，因为氢气消费者的最终能源消耗成本在不同的情景和国家之间的差异比电解槽的利润更大，因为它们是价格制定者。

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引用次数: 0

Reaching carbon neutrality in China: Temporal and subnational limitations of renewable energy scale-up 中国实现碳中和：可再生能源规模扩大的时间和地方限制

IF 13.8 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-09-23 DOI: 10.1016/j.adapen.2025.100238

Zhenhua Zhang , Ziheng Zhu , Jessica A. Gordon , Xi Lu , Da Zhang , Michael R. Davidson

Acute temporal impacts and subnational limitations can hinder a country’s decarbonization pathway, despite national planning efforts. China, as the world’s largest greenhouse gas (GHG) emitter, has announced an ambitious climate policy goal of achieving carbon neutrality by 2060, which will require an unprecedented scale-up of low-carbon energy technologies. China’s variable renewable energy (VRE) deployment is historically imbalanced with large geographic concentrations driven by resource endowment and institutional heterogeneities. If continued, this pattern can run into deployment limits and exacerbate challenges associated with socio-economic benefits distribution, threatening the ability to timely integrate VRE. We develop a capacity expansion model with grid operational detail and high spatial resolution to examine decadal pathways to carbon neutrality by 2060 considering localized and temporal impacts. Over these four decades, we find that all regions will increase deployment rates of renewable energy, first driven by the use of high-quality resources, and later by coal retirement and electricity demand growth. The share of provinces with high deployment pressure, where deployment requirements exceed historical rates, increases from around 45% to 100% by the final decade. If carbon capture and storage (CCS) is not available, maximum annual average deployment rates will increase by 33% and occur a decade earlier. A more stringent 1.5 °C emission target leads to more acute temporal and spatial deployment pressures in the first decade, with VRE concentrated in regions with high-quality resources and demand centers and a doubling of new transmission capacity in the first decade. Effective national and subnational policy support is necessary to coordinate VRE deployment and facilitate transitions in impacted regions.

尽管做出了国家规划努力，但严重的时间影响和地方限制可能会阻碍一个国家的脱碳之路。作为世界上最大的温室气体（GHG）排放国，中国宣布了一项雄心勃勃的气候政策目标，即到2060年实现碳中和，这将需要前所未有地扩大低碳能源技术的规模。中国的可变可再生能源（VRE）部署在历史上是不平衡的，受资源禀赋和制度异质性驱动的大地理集中度。如果持续下去，这种模式可能会遇到部署限制，并加剧与社会经济效益分配相关的挑战，从而威胁到及时整合VRE的能力。我们开发了一个具有网格运行细节和高空间分辨率的容量扩展模型，以考虑局部和时间影响，研究到2060年实现碳中和的年代际路径。在这40年里，我们发现所有地区都将提高可再生能源的部署率，首先是受优质资源使用的推动，然后是煤炭退役和电力需求的增长。在未来十年内，部署压力大的省份（部署需求超过历史水平）所占比例将从45%左右增加到100%。如果碳捕集与封存（CCS）无法实现，最高年平均部署率将提高33%，并提前十年实现。更严格的1.5°C排放目标会在第一个十年带来更大的时空部署压力，VRE集中在拥有优质资源和需求中心的地区，并且在第一个十年中将新增传输能力增加一倍。有效的国家和地方政策支持对于协调VRE的部署和促进受影响地区的过渡是必要的。

{"title":"Reaching carbon neutrality in China: Temporal and subnational limitations of renewable energy scale-up","authors":"Zhenhua Zhang , Ziheng Zhu , Jessica A. Gordon , Xi Lu , Da Zhang , Michael R. Davidson","doi":"10.1016/j.adapen.2025.100238","DOIUrl":"10.1016/j.adapen.2025.100238","url":null,"abstract":"<div><div>Acute temporal impacts and subnational limitations can hinder a country’s decarbonization pathway, despite national planning efforts. China, as the world’s largest greenhouse gas (GHG) emitter, has announced an ambitious climate policy goal of achieving carbon neutrality by 2060, which will require an unprecedented scale-up of low-carbon energy technologies. China’s variable renewable energy (VRE) deployment is historically imbalanced with large geographic concentrations driven by resource endowment and institutional heterogeneities. If continued, this pattern can run into deployment limits and exacerbate challenges associated with socio-economic benefits distribution, threatening the ability to timely integrate VRE. We develop a capacity expansion model with grid operational detail and high spatial resolution to examine decadal pathways to carbon neutrality by 2060 considering localized and temporal impacts. Over these four decades, we find that all regions will increase deployment rates of renewable energy, first driven by the use of high-quality resources, and later by coal retirement and electricity demand growth. The share of provinces with high deployment pressure, where deployment requirements exceed historical rates, increases from around 45% to 100% by the final decade. If carbon capture and storage (CCS) is not available, maximum annual average deployment rates will increase by 33% and occur a decade earlier. A more stringent 1.5 °C emission target leads to more acute temporal and spatial deployment pressures in the first decade, with VRE concentrated in regions with high-quality resources and demand centers and a doubling of new transmission capacity in the first decade. Effective national and subnational policy support is necessary to coordinate VRE deployment and facilitate transitions in impacted regions.</div></div>","PeriodicalId":34615,"journal":{"name":"Advances in Applied Energy","volume":"20 ","pages":"Article 100238"},"PeriodicalIF":13.8,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145321503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rethinking the atmospheric downward longwave radiation: A black-gray body model for accurate estimation 对大气向下长波辐射的再思考：一种精确估计的黑灰体模型

IF 13.8 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-09-18 DOI: 10.1016/j.adapen.2025.100244

Lanxin Li , Xianze Ao , Qiangyan Hao , Meiling Liu , Xiansheng Li , Kegui Lu , Chongwen Zou , Bin Zhao , Gang Pei

Accurately estimating atmospheric downward longwave radiation is critical for applications ranging from radiative cooling to building energy efficiency. The main challenge lies in its spectral variability, which depends strongly on sky conditions such as humidity and cloud cover. In this study, we propose a Black–Gray body atmospheric radiation model that divides the infrared spectrum into three regions, treating the atmosphere as a graybody in the 8–13 μm and a blackbody outside this band. The model integrates locally measured radiative power to dynamically capture temporal and spatial variations. Validation experiments were conducted using radiative cooling processes in three Chinese cities (Hefei, Lhasa, and Haikou) under different climates and weather conditions. The BG model consistently predicted radiative cooling power with high accuracy, with mean absolute percentage errors generally below 10 %, outperforming both the effective sky emissivity method and MODTRAN-based predictions. Furthermore, we introduce the concept of band-resolved atmospheric energy databases, analogous to solar radiation databases, and demonstrate it with a full-year case study in Hefei. This work provides a new modeling framework that enhances precision and enables broader applications in energy systems, climate studies, and environmental design.

准确估算大气向下长波辐射对于辐射冷却和建筑节能等应用至关重要。主要的挑战在于它的光谱变异性，这在很大程度上取决于天空条件，如湿度和云层。在本研究中，我们提出了一个黑-灰体大气辐射模型，该模型将红外光谱划分为3个区域，将8-13 μm波段内的大气视为灰体，将该波段外的大气视为黑体。该模型集成了本地测量的辐射功率，以动态捕获时间和空间变化。在合肥、拉萨和海口三个城市进行了不同气候和天气条件下的辐射冷却验证实验。BG模式对辐射冷却功率的预测具有较高的准确性，平均绝对百分比误差一般在10%以下，优于有效天空发射率方法和基于modtran的预测。此外，我们还引入了类似于太阳辐射数据库的波段分辨大气能量数据库的概念，并以合肥市全年为例进行了验证。这项工作提供了一种新的建模框架，可以提高精度，并在能源系统、气候研究和环境设计中得到更广泛的应用。

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引用次数: 0

Revealing drivers of green technology adoption through explainable Artificial Intelligence 通过可解释的人工智能揭示绿色技术采用的驱动因素

IF 13.8 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-09-18 DOI: 10.1016/j.adapen.2025.100242

Dorothea Kistinger , Maurizio Titz , Philipp C. Böttcher , Michael T. Schaub , Sandra Venghaus , Dirk Witthaut

Effective governance of energy system transformation away from fossil resources requires a quantitative understanding of the diffusion of green technologies and its key influencing factors. In this article, we propose a novel machine learning approach to diffusion research focusing on actual decisions and spatial aspects complementing research on intentions and temporal dynamics. We develop machine learning models that predict regional differences in the accumulated peak power of household-scale photovoltaic systems and the share of battery electric vehicles from a large set of demographic, geographic, political, and socio-economic features. Tools from explainable artificial intelligence enable a consistent identification of the key influencing factors and quantify their impact. Focusing on data from German municipal associations, we identify common themes and differences in the adoption of green technologies. Specifically, the adoption of battery electric vehicles is strongly associated with income and election results, while the adoption of photovoltaic systems correlates with the prevalence of large dwellings and levels of global solar radiation.

对能源系统从化石资源转型的有效治理需要对绿色技术的扩散及其关键影响因素进行定量理解。在本文中，我们提出了一种新的机器学习方法来进行扩散研究，重点关注实际决策和空间方面，补充了意图和时间动态的研究。我们开发了机器学习模型，从大量的人口、地理、政治和社会经济特征中预测家庭规模光伏系统累积峰值功率的区域差异和电池电动汽车的份额。来自可解释人工智能的工具能够一致地识别关键影响因素并量化其影响。关注德国市政协会的数据，我们确定了采用绿色技术的共同主题和差异。具体来说，电池电动汽车的采用与收入和选举结果密切相关，而光伏系统的采用与大型住宅的普及和全球太阳辐射水平有关。

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引用次数: 0

Advances and challenges in energy and climate alignment of AI infrastructure expansion 人工智能基础设施扩展在能源和气候方面的进展和挑战

IF 13.8 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-09-12 DOI: 10.1016/j.adapen.2025.100243

Apoorv Lal , Fengqi You

The rapid growth of artificial intelligence (AI) infrastructure deployment presents significant challenges for global energy systems and climate goals. While previous reviews address the sustainability of traditional data centers, Green AI approaches centered on model-level improvements or the application of AI in advancing sustainability across sectors, the energy and climate consequences of deploying AI infrastructure itself remain underexplored in prior literature. This paper reviews existing analyses on AI infrastructure’s energy and climate implications and proposes quantitative scenario-based frameworks, highlighting key research challenges at the intersection of AI-driven energy demand, region-specific clean energy strategies and their economic competitiveness, strategic levers in energy sourcing decisions, and policy dynamics. Additionally, this work identifies future research directions for aligning AI infrastructure growth with clean energy transitions through targeted mitigation opportunities across spatial and temporal horizons. First, the ambitious investment pathways for AI infrastructure development in the US underscore the need for spatially resolved scenario frameworks that reflect regional differences in deployment patterns and clean energy integration, along with the associated cost trajectories, to guide federal and state regulators. Second, the global expansion of AI infrastructure emphasizes the need for comprehensive frameworks that assess country-specific electricity demand shares, renewable transition pathways, and the influence of geopolitical restrictions, offering actionable insights for climate-conscious strategies. Finally, to prevent reinforcing fossil fuel dependency, particularly under disruptive growth scenarios, energy pathways incorporating nuclear power, renewables, energy storage, and varying grid reliance are explored as part of broader clean energy transitions, especially in regions facing energy security challenges.

人工智能（AI）基础设施部署的快速增长对全球能源系统和气候目标提出了重大挑战。虽然之前的评论涉及传统数据中心的可持续性，但绿色人工智能方法侧重于模型级改进或人工智能在促进跨部门可持续性方面的应用，但在先前的文献中，部署人工智能基础设施本身对能源和气候的影响仍未得到充分探讨。本文回顾了关于人工智能基础设施对能源和气候影响的现有分析，并提出了基于情景的定量框架，强调了人工智能驱动的能源需求、特定区域的清洁能源战略及其经济竞争力、能源采购决策的战略杠杆和政策动态等交叉领域的关键研究挑战。此外，这项工作确定了未来的研究方向，通过跨时空的有针对性的缓解机会，使人工智能基础设施的增长与清洁能源的过渡保持一致。首先，美国人工智能基础设施发展的雄心勃勃的投资途径强调了对空间解决方案框架的需求，这些框架反映了部署模式和清洁能源整合的地区差异，以及相关的成本轨迹，以指导联邦和州监管机构。其次，人工智能基础设施的全球扩张强调需要建立综合框架，评估各国具体的电力需求份额、可再生能源转型途径和地缘政治限制的影响，为气候意识战略提供可操作的见解。最后，为了防止加剧对化石燃料的依赖，特别是在破坏性增长情景下，作为更广泛的清洁能源转型的一部分，特别是在面临能源安全挑战的地区，我们探索了包括核电、可再生能源、储能和不同电网依赖的能源途径。

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引用次数: 0

Ensemble learning framework for radiative cooling coatings in China’s buildings 中国建筑辐射冷却涂料的集成学习框架

IF 13.8 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-09-10 DOI: 10.1016/j.adapen.2025.100241

Ze Li , Jianheng Chen , Wenqi Wang , Yang Fu , Xin Li , Aiqiang Pan , Yiying Zhou , Shimelis Admassie , Chi Yan Tso

Radiative cooling (RC) coatings have emerged as a promising strategy to mitigate the urban heat island effect and improve energy performance in residential buildings. However, their effect varies significantly across different climate zones and urban configurations, underscoring the need for targeted deployment strategies. In this study, an ensemble learning framework was developed by integrating the urban canopy model with the building energy model to predict the energy performance of RC coatings on residential buildings throughout China. A dataset of 5080 cases was generated, and CatBoost demonstrated excellent predictive accuracy (R² = 0.948–0.989). SHapley Additive exPlanations analysis identified longwave radiation and building geometry as the most influential factors affecting RC coating energy performance. The trained prediction model was further applied to evaluate six representative cities across diverse climate zones, for community-level evaluation. Additionally, national-scale predictions were conducted by the framework, using simulations of 111 cities, showing RC coatings are most effective in climate zones with hot summer and warm winter, with maximum annual electricity savings of approximately 50 MWh and maximum carbon emission reductions of around 20 kg·m^-2 per year in a hypothetical residential neighborhood. In contrast, their benefits are more limited in cold climate zones due to increased heating demand. These findings provide an effective framework for optimizing RC coating deployment strategies under varying climatic conditions. Furthermore, the framework holds the potential to expand these analyses globally, enabling the evaluation of RC coatings across diverse building types and regions to support worldwide energy and carbon reduction goals.

辐射冷却（RC）涂料已成为一种有前途的策略，以减轻城市热岛效应和提高能源性能的住宅建筑。然而，它们的影响在不同的气候带和城市配置中差异很大，这强调了有针对性的部署策略的必要性。在本研究中，通过将城市顶棚模型与建筑能耗模型相结合，建立了一个集成学习框架来预测中国住宅RC涂料的能耗性能。生成了5080个病例的数据集，CatBoost显示出良好的预测准确率（R2 = 0.948-0.989）。SHapley加性解释分析发现，长波辐射和建筑几何是影响RC涂层节能性能的最大因素。将训练后的预测模型应用于6个不同气候带的代表性城市，进行社区层面的评价。此外，通过对111个城市的模拟，该框架进行了全国范围的预测，表明RC涂料在夏季炎热和冬季温暖的气候区最为有效，在一个假设的居民区，每年最多可节省约50兆瓦时的电力，最多可减少约20公斤·m-2的碳排放。相比之下，由于供暖需求增加，它们的效益在寒冷气候地区更为有限。这些发现为在不同气候条件下优化RC涂层部署策略提供了有效的框架。此外，该框架具有在全球范围内扩展这些分析的潜力，能够对不同建筑类型和地区的RC涂料进行评估，以支持全球能源和碳减排目标。

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引用次数: 0

A systematic review of reinforcement learning in Building-Integrated Photovoltaic (BIPV) optimization 建筑集成光伏（BIPV）优化中强化学习的系统综述

IF 13.8 Q1 ENERGY & FUELS

Advances in Applied Energy

Pub Date : 2025-09-01 DOI: 10.1016/j.adapen.2025.100239

Jiaqi Li , Hongbin Xie , Jingyuan Zhang , Lianxin Li , Ge Song , Hongdi Fu , Panxi Chen , Chenyang Liu , Liyu Zhang , Zhuoran Shi , Qing Yu , Xuan Song , Haoran Zhang

Building-Integrated Photovoltaic (BIPV), as an emerging clean energy solution, plays a crucial role in energy saving, emission reduction, and grid load regulation. However, due to the uncertainty of dynamic environments and the complexity of multiple sensitive parameters, traditional scheduling methods fail to achieve optimal results. Considering that reinforcement learning, as an advanced research approach, demonstrates great potential in decision-making for high-dimensional problems and stability in dynamic environments, integrating reinforcement learning with BIPV is a feasible solution to address scheduling challenges in BIPV systems. However, there is still a lack of comprehensive analysis and systematic understanding of reinforcement learning applications in the BIPV field, which, to some extent, limits its further development in the BIPV domain. To this end, this review conducts an in-depth analysis of the effectiveness of reinforcement learning in BIPV applications from the perspective of the system construction life cycle. By considering the algorithm modeling life cycle of reinforcement learning, it comprehensively examines the potential issues in its application to BIPV, highlighting the challenges faced by existing research and future applications. Additionally, this paper integrates cutting-edge reinforcement learning knowledge, summarizes and categorizes its potential applications in BIPV, providing reference guidance for future research directions. Through this systematic review of reinforcement learning applications in the BIPV field, this study aims to offer valuable insights for subsequent research.

建筑一体化光伏（BIPV）作为一种新兴的清洁能源解决方案，在节能减排和调节电网负荷方面发挥着至关重要的作用。然而，由于动态环境的不确定性和多个敏感参数的复杂性，传统的调度方法无法达到最优的调度效果。考虑到强化学习作为一种先进的研究方法，在高维问题的决策和动态环境的稳定性方面显示出巨大的潜力，将强化学习与BIPV集成是解决BIPV系统调度挑战的可行方案。然而，目前对于强化学习在BIPV领域的应用还缺乏全面的分析和系统的认识，这在一定程度上限制了其在BIPV领域的进一步发展。为此，本文从系统构建生命周期的角度深入分析了强化学习在BIPV应用中的有效性。通过考虑强化学习的算法建模生命周期，全面考察了其在BIPV应用中可能存在的问题，突出了现有研究和未来应用面临的挑战。此外，本文整合了最前沿的强化学习知识，对其在BIPV中的潜在应用进行了总结和分类，为未来的研究方向提供了参考指导。通过系统回顾强化学习在BIPV领域的应用，本研究旨在为后续研究提供有价值的见解。

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