首页 > 最新文献

Energy and AI最新文献

英文 中文
Learning the optimal power flow: Environment design matters 学习最佳功率流:环境设计很重要
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-08-13 DOI: 10.1016/j.egyai.2024.100410
Thomas Wolgast, Astrid Nieße

To solve the optimal power flow (OPF) problem, reinforcement learning (RL) emerges as a promising new approach. However, the RL-OPF literature is strongly divided regarding the exact formulation of the OPF problem as an RL environment. In this work, we collect and implement diverse environment design decisions from the literature regarding training data, observation space, episode definition, and reward function choice. In an experimental analysis, we show the significant impact of these environment design options on RL-OPF training performance. Further, we derive some first recommendations regarding the choice of these design decisions. The created environment framework is fully open-source and can serve as a benchmark for future research in the RL-OPF field.

为了解决最优功率流(OPF)问题,强化学习(RL)成为一种很有前途的新方法。然而,RL-OPF 文献在将 OPF 问题作为 RL 环境的确切表述方面存在严重分歧。在这项工作中,我们收集并实施了文献中关于训练数据、观察空间、情节定义和奖励函数选择的各种环境设计决策。在实验分析中,我们展示了这些环境设计选项对 RL-OPF 训练性能的重大影响。此外,我们还就这些设计决策的选择提出了一些初步建议。创建的环境框架是完全开源的,可以作为 RL-OPF 领域未来研究的基准。
{"title":"Learning the optimal power flow: Environment design matters","authors":"Thomas Wolgast,&nbsp;Astrid Nieße","doi":"10.1016/j.egyai.2024.100410","DOIUrl":"10.1016/j.egyai.2024.100410","url":null,"abstract":"<div><p>To solve the optimal power flow (OPF) problem, reinforcement learning (RL) emerges as a promising new approach. However, the RL-OPF literature is strongly divided regarding the exact formulation of the OPF problem as an RL environment. In this work, we collect and implement diverse environment design decisions from the literature regarding training data, observation space, episode definition, and reward function choice. In an experimental analysis, we show the significant impact of these environment design options on RL-OPF training performance. Further, we derive some first recommendations regarding the choice of these design decisions. The created environment framework is fully open-source and can serve as a benchmark for future research in the RL-OPF field.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100410"},"PeriodicalIF":9.6,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000764/pdfft?md5=9a476707ca477944ae06662f8d552385&pid=1-s2.0-S2666546824000764-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Unsupervised learning of charge-discharge cycles from various lithium-ion battery cells to visualize dataset characteristics and to interpret model performance 对各种锂离子电池的充放电循环进行无监督学习,以直观显示数据集特征并解释模型性能
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-08-12 DOI: 10.1016/j.egyai.2024.100409
Akihiro Yamashita , Sascha Berg , Egbert Figgemeier

Machine learning (ML) is a rapidly growing tool even in the lithium-ion battery (LIB) research field. To utilize this tool, more and more datasets have been published. However, applicability of a ML model to different information sources or various LIB cell types has not been well studied. In this paper, an unsupervised learning model called variational autoencoder (VAE) is evaluated with three datasets of charge-discharge cycles with different conditions. The model was first trained with a publicly available dataset of commercial cylindrical cells, and then evaluated with our private datasets of commercial pouch and hand-made coin cells. These cells used different chemistry and were tested with different cycle testers under different purposes, which induces various characteristics to each dataset. We report that researchers can recognise these characteristics with VAE to plan a proper data preprocessing. We also discuss about interpretability of a ML model.

即使在锂离子电池(LIB)研究领域,机器学习(ML)也是一种快速发展的工具。为了利用这一工具,已经发布了越来越多的数据集。然而,ML 模型对不同信息源或各种锂离子电池类型的适用性还没有得到很好的研究。本文利用三个不同条件下的充放电循环数据集,对一种名为变异自动编码器(VAE)的无监督学习模型进行了评估。该模型首先使用公开的商用圆柱形电池数据集进行训练,然后使用我们自己的商用袋装电池和手工制造的硬币电池数据集进行评估。这些电池使用了不同的化学成分,并在不同目的下使用不同的循环测试仪进行了测试,从而使每个数据集都具有不同的特征。我们的报告指出,研究人员可以利用 VAE 识别这些特征,从而制定适当的数据预处理计划。我们还讨论了 ML 模型的可解释性。
{"title":"Unsupervised learning of charge-discharge cycles from various lithium-ion battery cells to visualize dataset characteristics and to interpret model performance","authors":"Akihiro Yamashita ,&nbsp;Sascha Berg ,&nbsp;Egbert Figgemeier","doi":"10.1016/j.egyai.2024.100409","DOIUrl":"10.1016/j.egyai.2024.100409","url":null,"abstract":"<div><p>Machine learning (ML) is a rapidly growing tool even in the lithium-ion battery (LIB) research field. To utilize this tool, more and more datasets have been published. However, applicability of a ML model to different information sources or various LIB cell types has not been well studied. In this paper, an unsupervised learning model called variational autoencoder (VAE) is evaluated with three datasets of charge-discharge cycles with different conditions. The model was first trained with a publicly available dataset of commercial cylindrical cells, and then evaluated with our private datasets of commercial pouch and hand-made coin cells. These cells used different chemistry and were tested with different cycle testers under different purposes, which induces various characteristics to each dataset. We report that researchers can recognise these characteristics with VAE to plan a proper data preprocessing. We also discuss about interpretability of a ML model.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100409"},"PeriodicalIF":9.6,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000752/pdfft?md5=4fe4525928ca81e3686b18c3d211341f&pid=1-s2.0-S2666546824000752-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Data-driven strategy for state of health prediction and anomaly detection in lithium-ion batteries 锂离子电池健康状态预测和异常检测的数据驱动战略
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-08-09 DOI: 10.1016/j.egyai.2024.100413
Slimane Arbaoui , Ahmed Samet , Ali Ayadi , Tedjani Mesbahi , Romuald Boné

This study addresses the crucial challenge of monitoring the State of Health (SOH) of Lithium-Ion Batteries (LIBs) in response to the escalating demand for renewable energy systems and the imperative to reduce CO2 emissions. The research introduces deep learning (DL) models, namely Encoder-Long Short-Term Memory (E-LSTM) and Convolutional Neural Network-LSTM (CNN–LSTM), each designed to forecast battery SOH. E-LSTM integrates an encoder for dimensionality reduction and an LSTM model to capture data dependencies. CNN–LSTM, on the other hand, employs CNN layers for encoding followed by LSTM layers for precise SOH estimation. Significantly, we prioritize model explainability by employing a game-theoretic approach known as SHapley Additive exPlanations (SHAP) to elucidate the output of our models. Furthermore, a method based on pattern mining was developed, synergizing with the model, to identify patterns contributing to abnormal SOH decrease. These insights are presented through informative plots. The proposed approach relies on the battery dataset from the Massachusetts Institute of Technology (MIT) and showcases promising results in accurately estimating SOH values, in which the E-LSTM model outperformed the CNN–LSTM model with a Mean Absolute Error (MAE) of less than 1%.

本研究探讨了监测锂离子电池(LIB)健康状况(SOH)的关键挑战,以应对对可再生能源系统日益增长的需求和减少二氧化碳排放的迫切要求。该研究引入了深度学习(DL)模型,即编码器-长短期记忆(E-LSTM)和卷积神经网络-LSTM(CNN-LSTM),这两种模型均用于预测电池的健康状况。E-LSTM 集成了用于降维的编码器和用于捕捉数据依赖性的 LSTM 模型。另一方面,CNN-LSTM 采用 CNN 层进行编码,然后采用 LSTM 层进行精确的 SOH 估算。值得注意的是,我们优先考虑模型的可解释性,采用了一种称为 "SHAPLEY Additive exPlanations(SHAP)"的博弈论方法来阐明模型的输出。此外,我们还开发了一种基于模式挖掘的方法,与模型协同识别导致 SOH 异常下降的模式。这些见解通过信息图呈现出来。所提出的方法依赖于麻省理工学院(MIT)的电池数据集,在准确估计 SOH 值方面取得了可喜的成果,其中 E-LSTM 模型的平均绝对误差(MAE)小于 1%,优于 CNN-LSTM 模型。
{"title":"Data-driven strategy for state of health prediction and anomaly detection in lithium-ion batteries","authors":"Slimane Arbaoui ,&nbsp;Ahmed Samet ,&nbsp;Ali Ayadi ,&nbsp;Tedjani Mesbahi ,&nbsp;Romuald Boné","doi":"10.1016/j.egyai.2024.100413","DOIUrl":"10.1016/j.egyai.2024.100413","url":null,"abstract":"<div><p>This study addresses the crucial challenge of monitoring the State of Health (SOH) of Lithium-Ion Batteries (LIBs) in response to the escalating demand for renewable energy systems and the imperative to reduce CO2 emissions. The research introduces deep learning (DL) models, namely Encoder-Long Short-Term Memory (E-LSTM) and Convolutional Neural Network-LSTM (CNN–LSTM), each designed to forecast battery SOH. E-LSTM integrates an encoder for dimensionality reduction and an LSTM model to capture data dependencies. CNN–LSTM, on the other hand, employs CNN layers for encoding followed by LSTM layers for precise SOH estimation. Significantly, we prioritize model explainability by employing a game-theoretic approach known as SHapley Additive exPlanations (SHAP) to elucidate the output of our models. Furthermore, a method based on pattern mining was developed, synergizing with the model, to identify patterns contributing to abnormal SOH decrease. These insights are presented through informative plots. The proposed approach relies on the battery dataset from the Massachusetts Institute of Technology (MIT) and showcases promising results in accurately estimating SOH values, in which the E-LSTM model outperformed the CNN–LSTM model with a Mean Absolute Error (MAE) of less than 1%.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100413"},"PeriodicalIF":9.6,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266654682400079X/pdfft?md5=2f6d3403ffc70047c7693e2edcf06cd9&pid=1-s2.0-S266654682400079X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Cross-domain diagnosis for polymer electrolyte membrane fuel cell based on digital twins and transfer learning network✰ 基于数字双胞胎和迁移学习网络的聚合物电解质膜燃料电池跨域诊断✰
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-08-09 DOI: 10.1016/j.egyai.2024.100412
Zhichao Gong , Bowen Wang , Mohamed Benbouzid , Bin Li , Yifan Xu , Kai Yang , Zhiming Bao , Yassine Amirat , Fei Gao , Kui Jiao

Existing research on fault diagnosis for polymer electrolyte membrane fuel cells (PEMFC) has advanced significantly, yet performance is hindered by variations in data distributions and the requirement for extensive fault data. In this study, a cross-domain adaptive health diagnosis method for PEMFC is proposed, integrating the digital twin model and transfer convolutional diagnosis model. A physical-based high-fidelity digital twin model is developed to obtain diverse and high-quality datasets for training diagnosis method. To extract long-term time series features from the data, a temporal convolutional network (TCN) is proposed as a pre-trained diagnosis model for the source domain, with feature extraction layers that can be reused to the transfer learning network. It is demonstrated that the proposed pre-trained model can hold the ability to accurately diagnose the various fuel cell faults, including pressure, drying, flow, and flooding faults, with 99.92 % accuracy, through the effective capture of the long-term dependencies in time series data. Finally, a domain adaptive transfer convolutional network (DATCN) is established to improve the diagnosis accuracy across diverse fuel cells by learning domain-invariant features. The results show that the DATCN model, tested on three different target domain devices with adversarial training using only 10 % normal data, can achieve an average accuracy of 98.5 % (30 % improved over traditional diagnosis models). This proposed method provides an effective solution for accurate cross-domain diagnosis of PEMFC devices, significantly reducing the reliance on extensive fault data.

有关聚合物电解质膜燃料电池(PEMFC)故障诊断的现有研究已取得重大进展,但数据分布的变化和对大量故障数据的要求阻碍了其性能的提高。本研究提出了一种用于 PEMFC 的跨域自适应健康诊断方法,将数字孪生模型和传递卷积诊断模型融为一体。开发了一种基于物理的高保真数字孪生模型,以获得用于训练诊断方法的多样化高质量数据集。为了从数据中提取长期时间序列特征,提出了一个时序卷积网络(TCN)作为源域的预训练诊断模型,其特征提取层可重复用于迁移学习网络。结果表明,通过有效捕捉时间序列数据中的长期依赖关系,所提出的预训练模型能够准确诊断各种燃料电池故障,包括压力、干燥、流动和淹没故障,准确率高达 99.92%。最后,建立了一个域自适应传递卷积网络(DATCN),通过学习域不变特征来提高不同燃料电池的诊断准确率。结果表明,DATCN 模型在三个不同的目标域设备上进行了测试,只使用了 10% 的正常数据进行对抗训练,平均准确率可达 98.5%(比传统诊断模型提高了 30%)。所提出的方法为 PEMFC 设备的精确跨域诊断提供了有效的解决方案,大大减少了对大量故障数据的依赖。
{"title":"Cross-domain diagnosis for polymer electrolyte membrane fuel cell based on digital twins and transfer learning network✰","authors":"Zhichao Gong ,&nbsp;Bowen Wang ,&nbsp;Mohamed Benbouzid ,&nbsp;Bin Li ,&nbsp;Yifan Xu ,&nbsp;Kai Yang ,&nbsp;Zhiming Bao ,&nbsp;Yassine Amirat ,&nbsp;Fei Gao ,&nbsp;Kui Jiao","doi":"10.1016/j.egyai.2024.100412","DOIUrl":"10.1016/j.egyai.2024.100412","url":null,"abstract":"<div><p>Existing research on fault diagnosis for polymer electrolyte membrane fuel cells (PEMFC) has advanced significantly, yet performance is hindered by variations in data distributions and the requirement for extensive fault data. In this study, a cross-domain adaptive health diagnosis method for PEMFC is proposed, integrating the digital twin model and transfer convolutional diagnosis model. A physical-based high-fidelity digital twin model is developed to obtain diverse and high-quality datasets for training diagnosis method. To extract long-term time series features from the data, a temporal convolutional network (TCN) is proposed as a pre-trained diagnosis model for the source domain, with feature extraction layers that can be reused to the transfer learning network. It is demonstrated that the proposed pre-trained model can hold the ability to accurately diagnose the various fuel cell faults, including pressure, drying, flow, and flooding faults, with 99.92 % accuracy, through the effective capture of the long-term dependencies in time series data. Finally, a domain adaptive transfer convolutional network (DATCN) is established to improve the diagnosis accuracy across diverse fuel cells by learning domain-invariant features. The results show that the DATCN model, tested on three different target domain devices with adversarial training using only 10 % normal data, can achieve an average accuracy of 98.5 % (30 % improved over traditional diagnosis models). This proposed method provides an effective solution for accurate cross-domain diagnosis of PEMFC devices, significantly reducing the reliance on extensive fault data.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100412"},"PeriodicalIF":9.6,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000788/pdfft?md5=eea03f9690b3b69a433d3bdeadbcbad8&pid=1-s2.0-S2666546824000788-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142011770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Energy Disaggregation of Industrial Machinery Utilizing Artificial Neural Networks for Non-intrusive Load Monitoring 利用人工神经网络对工业机械进行能量分解,实现非侵入式负载监测
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-08-04 DOI: 10.1016/j.egyai.2024.100407
Philipp Pelger , Johannes Steinleitner , Alexander Sauer

This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on energy efficiency and decarbonization measures, achieving energy transparency in production becomes crucial. Utilizing non-intrusive load monitoring, energy data analysis and processing can provide valuable insights for informed decision-making on energy efficiency improvements and emission reductions. While non-intrusive load monitoring has been extensively researched in the building and residential sectors, the application in the industrial manufacturing domain needs to be further explored. This paper addresses this research gap by adapting established non-intrusive load monitoring techniques to an industrial dataset. By employing artificial neural networks for energy disaggregation, the determination of energy consumption of industrial machinery is made possible. Therefore, a generally applicable cross-energy carrier method to disaggregate the energy consumption of machinery in manufacturing processes is developed using a design science research approach and validated through a practical case study utilizing a compressed air demonstrator. The results show that the utilization of artificial neural networks is well-suited for energy disaggregation of industrial data, effectively identifying on and off states, multi-level states and continuously variable states. Non-intrusive load monitoring should be further considered in the research of emerging artificial intelligence technologies in energy consumption evaluation. It can be a viable alternative for intrusive load monitoring and is a prerequisite to installing energy meters for every machine.

本文探讨了非侵入式负荷监测技术在工业领域的应用,以分解生产过程中的机械能耗。随着人们越来越重视能源效率和去碳化措施,实现生产过程中的能源透明度变得至关重要。利用非侵入式负荷监测、能源数据分析和处理,可以为提高能效和减少排放的知情决策提供有价值的见解。虽然非侵入式负荷监测在建筑和住宅领域得到了广泛研究,但在工业制造领域的应用还有待进一步探索。本文针对这一研究空白,将成熟的非侵入式负荷监测技术应用于工业数据集。通过采用人工神经网络进行能量分解,可以确定工业机械的能耗。因此,利用设计科学研究方法开发了一种普遍适用的跨能源载体方法,用于分解制造过程中的机械能耗,并通过利用压缩空气演示器进行的实际案例研究进行了验证。研究结果表明,人工神经网络非常适合用于工业数据的能耗分解,能有效识别开和关状态、多级状态和连续可变状态。在研究能耗评估中的新兴人工智能技术时,应进一步考虑非侵入式负荷监测。它可以成为侵入式负载监控的可行替代方案,也是为每台机器安装能源计量表的先决条件。
{"title":"Energy Disaggregation of Industrial Machinery Utilizing Artificial Neural Networks for Non-intrusive Load Monitoring","authors":"Philipp Pelger ,&nbsp;Johannes Steinleitner ,&nbsp;Alexander Sauer","doi":"10.1016/j.egyai.2024.100407","DOIUrl":"10.1016/j.egyai.2024.100407","url":null,"abstract":"<div><p>This paper explores the application of non-intrusive load monitoring techniques in the industrial sector for disaggregating the energy consumption of machinery in manufacturing processes. With an increasing focus on energy efficiency and decarbonization measures, achieving energy transparency in production becomes crucial. Utilizing non-intrusive load monitoring, energy data analysis and processing can provide valuable insights for informed decision-making on energy efficiency improvements and emission reductions. While non-intrusive load monitoring has been extensively researched in the building and residential sectors, the application in the industrial manufacturing domain needs to be further explored. This paper addresses this research gap by adapting established non-intrusive load monitoring techniques to an industrial dataset. By employing artificial neural networks for energy disaggregation, the determination of energy consumption of industrial machinery is made possible. Therefore, a generally applicable cross-energy carrier method to disaggregate the energy consumption of machinery in manufacturing processes is developed using a design science research approach and validated through a practical case study utilizing a compressed air demonstrator. The results show that the utilization of artificial neural networks is well-suited for energy disaggregation of industrial data, effectively identifying on and off states, multi-level states and continuously variable states. Non-intrusive load monitoring should be further considered in the research of emerging artificial intelligence technologies in energy consumption evaluation. It can be a viable alternative for intrusive load monitoring and is a prerequisite to installing energy meters for every machine.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100407"},"PeriodicalIF":9.6,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000739/pdfft?md5=1ae6290c0db1d3d6779ce8eb7568918e&pid=1-s2.0-S2666546824000739-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Enhanced vision-transformer integrating with semi-supervised transfer learning for state of health and remaining useful life estimation of lithium-ion batteries 集成了半监督转移学习的增强型视觉转换器,用于锂离子电池的健康状况和剩余使用寿命评估
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-08-03 DOI: 10.1016/j.egyai.2024.100405
Ya-Xiong Wang , Shangyu Zhao , Shiquan Wang , Kai Ou , Jiujun Zhang

The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are crucial for health management and diagnosis. However, most data-driven estimation methods heavily rely on scarce labeled data, while traditional transfer learning faces challenges in handling domain shifts across various battery types. This paper proposes an enhanced vision-transformer integrating with semi-supervised transfer learning for SOH and RUL estimation of lithium-ion batteries. A depth-wise separable convolutional vision-transformer is developed to extract local aging details with depth-wise convolutions and establishes global dependencies between aging information using multi-head attention. Maximum mean discrepancy is employed to initially reduce the distribution difference between the source and target domains, providing a superior starting point for fine-tuning the target domain model. Subsequently, the abundant aging data of the same type as the target battery are labeled through semi-supervised learning, compensating for the source model's limitations in capturing target battery aging characteristics. Consistency regularization incorporates the cross-entropy between predictions with and without adversarial perturbations into the gradient backpropagation of the overall model. In particular, across the experimental groups 13–15 for different types of batteries, the root mean square error of SOH estimation was less than 0.66 %, and the mean relative error of RUL estimation was 3.86 %. Leveraging extensive unlabeled aging data, the proposed method could achieve accurate estimation of SOH and RUL.

锂离子电池的健康状况(SOH)和剩余使用寿命(RUL)对于健康管理和诊断至关重要。然而,大多数数据驱动的估算方法严重依赖稀缺的标记数据,而传统的迁移学习在处理各种电池类型的领域转换方面面临挑战。本文提出了一种集成了半监督迁移学习的增强型视觉变换器,用于锂离子电池的 SOH 和 RUL 估算。本文开发了一种深度可分离卷积视觉变换器,利用深度卷积提取局部老化细节,并利用多头注意力建立老化信息之间的全局依赖关系。利用最大均值差异来初步缩小源域和目标域之间的分布差异,为微调目标域模型提供了一个良好的起点。随后,通过半监督学习标记与目标电池同类型的丰富老化数据,弥补源模型在捕捉目标电池老化特征方面的局限性。一致性正则化将有对抗扰动和无对抗扰动预测之间的交叉熵纳入整体模型的梯度反向传播中。特别是,在不同类型电池的 13-15 组实验中,SOH 估计的均方根误差小于 0.66%,RUL 估计的平均相对误差为 3.86%。利用大量未标记的老化数据,所提出的方法可以实现对 SOH 和 RUL 的精确估算。
{"title":"Enhanced vision-transformer integrating with semi-supervised transfer learning for state of health and remaining useful life estimation of lithium-ion batteries","authors":"Ya-Xiong Wang ,&nbsp;Shangyu Zhao ,&nbsp;Shiquan Wang ,&nbsp;Kai Ou ,&nbsp;Jiujun Zhang","doi":"10.1016/j.egyai.2024.100405","DOIUrl":"10.1016/j.egyai.2024.100405","url":null,"abstract":"<div><p>The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are crucial for health management and diagnosis. However, most data-driven estimation methods heavily rely on scarce labeled data, while traditional transfer learning faces challenges in handling domain shifts across various battery types. This paper proposes an enhanced vision-transformer integrating with semi-supervised transfer learning for SOH and RUL estimation of lithium-ion batteries. A depth-wise separable convolutional vision-transformer is developed to extract local aging details with depth-wise convolutions and establishes global dependencies between aging information using multi-head attention. Maximum mean discrepancy is employed to initially reduce the distribution difference between the source and target domains, providing a superior starting point for fine-tuning the target domain model. Subsequently, the abundant aging data of the same type as the target battery are labeled through semi-supervised learning, compensating for the source model's limitations in capturing target battery aging characteristics. Consistency regularization incorporates the cross-entropy between predictions with and without adversarial perturbations into the gradient backpropagation of the overall model. In particular, across the experimental groups 13–15 for different types of batteries, the root mean square error of SOH estimation was less than 0.66 %, and the mean relative error of RUL estimation was 3.86 %. Leveraging extensive unlabeled aging data, the proposed method could achieve accurate estimation of SOH and RUL.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100405"},"PeriodicalIF":9.6,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000715/pdfft?md5=69ea1922b5cb753426903122e7193acd&pid=1-s2.0-S2666546824000715-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141998259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
An artificial intelligence framework for explainable drift detection in energy forecasting 能源预测中可解释漂移检测的人工智能框架
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-08-03 DOI: 10.1016/j.egyai.2024.100403
Chamod Samarajeewa , Daswin De Silva , Milos Manic , Nishan Mills , Harsha Moraliyage , Damminda Alahakoon , Andrew Jennings

Accurate energy consumption forecasting is crucial for reducing operational costs, achieving net-zero carbon emissions, and ensuring sustainable buildings and cities of the future. Despite the frequent use of Artificial Intelligence (AI) algorithms for learning energy consumption patterns and predictions in Building Science, relying solely on these techniques for energy demand prediction addresses only a fraction of the challenge. A drift in energy usage can lead to inaccuracies in these AI models and subsequently to poor decision-making and interventions. While drift detection techniques have been reported, a reliable and robust approach capable of explaining identified discrepancies with actionable insights has not been discussed in extant literature. Hence, this paper presents an Artificial Intelligence framework for energy consumption forecasting with explainable drift detection, aimed at addressing these challenges. The proposed framework is composed of energy embeddings, an optimized dimensional model integrated within a data warehouse, and scalable cloud implementation for effective drift detection with explainability capability. The framework is empirically evaluated in the real-world setting of a multi-campus, mixed-use tertiary education setting in Victoria, Australia. The results of these experiments highlight its capabilities in detecting concept drift, adapting forecast predictions, and providing an interpretation of the changes using energy embeddings.

准确的能耗预测对于降低运营成本、实现净零碳排放以及确保未来建筑和城市的可持续发展至关重要。尽管建筑科学领域经常使用人工智能(AI)算法来学习能耗模式和进行预测,但仅靠这些技术来预测能源需求只能解决挑战的一小部分。能源使用的偏移会导致这些人工智能模型的不准确性,进而导致决策和干预的失误。虽然已有漂移检测技术的报道,但现有文献中还没有讨论过一种可靠、稳健的方法,能够以可操作的见解解释已识别的差异。因此,本文提出了一种可解释漂移检测的能耗预测人工智能框架,旨在应对这些挑战。所提出的框架由能源嵌入、集成在数据仓库中的优化维度模型和可扩展的云实施组成,用于有效检测具有可解释性的漂移。该框架在澳大利亚维多利亚州一个多校区、混合使用的高等教育环境中进行了实证评估。实验结果凸显了该框架在检测概念漂移、调整预测预报以及利用能量嵌入对变化进行解释方面的能力。
{"title":"An artificial intelligence framework for explainable drift detection in energy forecasting","authors":"Chamod Samarajeewa ,&nbsp;Daswin De Silva ,&nbsp;Milos Manic ,&nbsp;Nishan Mills ,&nbsp;Harsha Moraliyage ,&nbsp;Damminda Alahakoon ,&nbsp;Andrew Jennings","doi":"10.1016/j.egyai.2024.100403","DOIUrl":"10.1016/j.egyai.2024.100403","url":null,"abstract":"<div><p>Accurate energy consumption forecasting is crucial for reducing operational costs, achieving net-zero carbon emissions, and ensuring sustainable buildings and cities of the future. Despite the frequent use of Artificial Intelligence (AI) algorithms for learning energy consumption patterns and predictions in Building Science, relying solely on these techniques for energy demand prediction addresses only a fraction of the challenge. A drift in energy usage can lead to inaccuracies in these AI models and subsequently to poor decision-making and interventions. While drift detection techniques have been reported, a reliable and robust approach capable of explaining identified discrepancies with actionable insights has not been discussed in extant literature. Hence, this paper presents an Artificial Intelligence framework for energy consumption forecasting with explainable drift detection, aimed at addressing these challenges. The proposed framework is composed of energy embeddings, an optimized dimensional model integrated within a data warehouse, and scalable cloud implementation for effective drift detection with explainability capability. The framework is empirically evaluated in the real-world setting of a multi-campus, mixed-use tertiary education setting in Victoria, Australia. The results of these experiments highlight its capabilities in detecting concept drift, adapting forecast predictions, and providing an interpretation of the changes using energy embeddings.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100403"},"PeriodicalIF":9.6,"publicationDate":"2024-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000697/pdfft?md5=43ed6a129e42eadda8715a969f5410c8&pid=1-s2.0-S2666546824000697-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141953183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Uplifting the complexity of analysis for probabilistic security of electricity supply assessments using artificial neural networks 利用人工神经网络提高电力供应安全概率评估分析的复杂性
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-08-02 DOI: 10.1016/j.egyai.2024.100401
Justin Münch , Jan Priesmann , Marius Reich , Marius Tillmanns , Aaron Praktiknjo , Mario Adam

The energy sector faces rapid decarbonisation and decision-makers demand reliable assessments of the security of electricity supply. For this, detailed simulation models with a high temporal and technological resolution are required. When confronted with increasing weather-dependent renewable energy generation, probabilistic simulation models have proven. The significant computational costs of calculating a scenario, however, limit the complexity of further analysis. Advances in code optimization as well as the use of computing clusters still lead to runtimes of up to eight hours per scenario. However ongoing research highlights that tailor-made approximations are potentially the key factor in further reducing computing time. Consequently, current research aims to provide a method for the rapid prediction of widely varying scenarios. In this work artificial neural networks (ANN) are trained and compared to approximate the system behavior of the probabilistic simulation model. To do so, information needs to be sampled from the probabilistic simulation in an efficient way. Because only a limited space in the whole design space of the 16 independent variables is of interest, a classification is developed. Finally it required only around 35 min to create the regression models, including sampling the design space, simulating the training data and training the ANNs. The resulting ANNs are able to predict all scenarios within the validity range of the regression model with a coefficient of determination of over 0.9998 for independent test data (1.051.200 data points). They need only a few milliseconds to predict one scenario, enabling in-depth analysis in a brief period of time.

能源行业面临着快速的去碳化,决策者需要对电力供应安全进行可靠的评估。为此,需要具有较高时间和技术分辨率的详细模拟模型。面对日益增长的依赖天气的可再生能源发电量,概率模拟模型已得到证实。然而,计算一个情景的巨大计算成本限制了进一步分析的复杂性。代码优化方面的进步以及计算集群的使用仍然导致每个情景的运行时间长达 8 小时。然而,正在进行的研究表明,量身定制的近似值可能是进一步缩短计算时间的关键因素。因此,当前的研究旨在提供一种方法,用于快速预测千差万别的场景。在这项工作中,对人工神经网络(ANN)进行了训练和比较,以逼近概率模拟模型的系统行为。为此,需要以有效的方式从概率模拟中抽取信息。由于在 16 个自变量的整个设计空间中,只有有限的空间是人们感兴趣的,因此开发了一种分类方法。最后,创建回归模型只需要大约 35 分钟,包括设计空间采样、模拟训练数据和训练 ANN。生成的人工智能网络能够预测回归模型有效范围内的所有情况,对独立测试数据(1.051200 个数据点)的判定系数超过 0.9998。它们只需要几毫秒就能预测一个场景,从而能够在短时间内进行深入分析。
{"title":"Uplifting the complexity of analysis for probabilistic security of electricity supply assessments using artificial neural networks","authors":"Justin Münch ,&nbsp;Jan Priesmann ,&nbsp;Marius Reich ,&nbsp;Marius Tillmanns ,&nbsp;Aaron Praktiknjo ,&nbsp;Mario Adam","doi":"10.1016/j.egyai.2024.100401","DOIUrl":"10.1016/j.egyai.2024.100401","url":null,"abstract":"<div><p>The energy sector faces rapid decarbonisation and decision-makers demand reliable assessments of the security of electricity supply. For this, detailed simulation models with a high temporal and technological resolution are required. When confronted with increasing weather-dependent renewable energy generation, probabilistic simulation models have proven. The significant computational costs of calculating a scenario, however, limit the complexity of further analysis. Advances in code optimization as well as the use of computing clusters still lead to runtimes of up to eight hours per scenario. However ongoing research highlights that tailor-made approximations are potentially the key factor in further reducing computing time. Consequently, current research aims to provide a method for the rapid prediction of widely varying scenarios. In this work artificial neural networks (ANN) are trained and compared to approximate the system behavior of the probabilistic simulation model. To do so, information needs to be sampled from the probabilistic simulation in an efficient way. Because only a limited space in the whole design space of the 16 independent variables is of interest, a classification is developed. Finally it required only around 35 min to create the regression models, including sampling the design space, simulating the training data and training the ANNs. The resulting ANNs are able to predict all scenarios within the validity range of the regression model with a coefficient of determination of over 0.9998 for independent test data (1.051.200 data points). They need only a few milliseconds to predict one scenario, enabling in-depth analysis in a brief period of time.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100401"},"PeriodicalIF":9.6,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000673/pdfft?md5=037a3df4e229de699ce8e60d069d4893&pid=1-s2.0-S2666546824000673-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Leveraging machine learning to generate a unified and complete building height dataset for Germany 利用机器学习生成统一完整的德国建筑高度数据集
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-07-31 DOI: 10.1016/j.egyai.2024.100408
Kristina Dabrock , Noah Pflugradt , Jann Michael Weinand , Detlef Stolten

Building geometry data is crucial for detailed, spatially-explicit analyses of the building stock in energy systems analysis and beyond. Despite the existence of diverse datasets and methods, a standardized and validated approach for creating a nation-wide unified and complete dataset of German building heights is not yet available. This study develops and validates such a methodology, combining different data sources for building footprints and heights and filling gaps in height data using an XGBoost machine learning algorithm. The XGBoost model achieves a mean absolute error of 1.78 m at the national level and between 1.52 m and 3.47 m at the federal state level. The goal is proving the applicability of the methodology at a large scale and creating a useful dataset. The resulting dataset is thoroughly evaluated on a building-by-building level and spatially resolved statistics on the quality of the dataset are reported. This detailed validation found that the building number and footprint area of German building stock is 90.31 % and 94.84 % correct, respectively, and the building height accuracy is 0.59 m at the national level. However, errors are not homogeneous across Germany and further research is needed into the impact of including additional datasets, especially for regions and building types with lower accuracies. This study proves that the chosen methodology is useful for generating a building height dataset and the workflow, with some modifications for regional data availability, can be transferred to other countries. The generated building dataset for Germany constitutes a valuable data basis for the research community in fields such as energy research, urban planning and building decarbonization policy development.

建筑几何数据对于在能源系统分析及其他方面对建筑群进行详细的空间分析至关重要。尽管存在各种不同的数据集和方法,但目前还没有一种标准化的、经过验证的方法来创建一个全国统一的、完整的德国建筑高度数据集。本研究开发并验证了这种方法,它结合了建筑占地面积和高度的不同数据源,并使用 XGBoost 机器学习算法填补了高度数据的空白。XGBoost 模型在国家一级的平均绝对误差为 1.78 米,在联邦州一级的平均绝对误差为 1.52 米至 3.47 米。目标是证明该方法的大规模适用性,并创建一个有用的数据集。我们对生成的数据集进行了逐栋建筑的全面评估,并报告了数据集质量的空间分辨率统计数据。详细的验证结果表明,德国建筑群的建筑数量和占地面积的正确率分别为 90.31% 和 94.84%,全国范围内的建筑高度精确度为 0.59 米。然而,德国各地的误差并不一致,因此需要进一步研究加入额外数据集的影响,尤其是对精确度较低的地区和建筑类型的影响。这项研究证明,所选择的方法对于生成建筑高度数据集非常有用,而且工作流程在根据地区数据可用性进行一些修改后,也可以推广到其他国家。生成的德国建筑数据集为能源研究、城市规划和建筑脱碳政策制定等领域的研究人员提供了宝贵的数据基础。
{"title":"Leveraging machine learning to generate a unified and complete building height dataset for Germany","authors":"Kristina Dabrock ,&nbsp;Noah Pflugradt ,&nbsp;Jann Michael Weinand ,&nbsp;Detlef Stolten","doi":"10.1016/j.egyai.2024.100408","DOIUrl":"10.1016/j.egyai.2024.100408","url":null,"abstract":"<div><p>Building geometry data is crucial for detailed, spatially-explicit analyses of the building stock in energy systems analysis and beyond. Despite the existence of diverse datasets and methods, a standardized and validated approach for creating a nation-wide unified and complete dataset of German building heights is not yet available. This study develops and validates such a methodology, combining different data sources for building footprints and heights and filling gaps in height data using an XGBoost machine learning algorithm. The XGBoost model achieves a mean absolute error of 1.78 m at the national level and between 1.52 m and 3.47 m at the federal state level. The goal is proving the applicability of the methodology at a large scale and creating a useful dataset. The resulting dataset is thoroughly evaluated on a building-by-building level and spatially resolved statistics on the quality of the dataset are reported. This detailed validation found that the building number and footprint area of German building stock is 90.31 % and 94.84 % correct, respectively, and the building height accuracy is 0.59 m at the national level. However, errors are not homogeneous across Germany and further research is needed into the impact of including additional datasets, especially for regions and building types with lower accuracies. This study proves that the chosen methodology is useful for generating a building height dataset and the workflow, with some modifications for regional data availability, can be transferred to other countries. The generated building dataset for Germany constitutes a valuable data basis for the research community in fields such as energy research, urban planning and building decarbonization policy development.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100408"},"PeriodicalIF":9.6,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000740/pdfft?md5=0c0b5b01fe19056c6830a6c702ac7eb8&pid=1-s2.0-S2666546824000740-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A review of control strategies for proton exchange membrane (PEM) fuel cells and water electrolysers: From automation to autonomy 质子交换膜(PEM)燃料电池和水电解槽控制策略综述:从自动化到自主化
IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2024-07-28 DOI: 10.1016/j.egyai.2024.100406
Jiahao Mao , Zheng Li , Jin Xuan , Xinli Du , Meng Ni , Lei Xing

Proton exchange membrane (PEM) based electrochemical systems have the capability to operate in fuel cell (PEMFC) and water electrolyser (PEMWE) modes, enabling efficient hydrogen energy utilisation and green hydrogen production. In addition to the essential cell stacks, the system of PEMFC or PEMWE consists of four sub-systems for managing gas supply, power, thermal, and water, respectively. Due to the system's complexity, even a small fluctuation in a certain sub-system can result in an unexpected response, leading to a reduced performance and stability. To improve the system's robustness and responsiveness, considerable efforts have been dedicated to developing advanced control strategies. This paper comprehensively reviews various control strategies proposed in literature, revealing that traditional control methods are widely employed in PEMFC and PEMWE due to their simplicity, yet they suffer from limitations in accuracy. Conversely, advanced control methods offer high accuracy but are hindered by poor dynamic performance. This paper highlights the recent advancements in control strategies incorporating machine learning algorithms. Additionally, the paper provides a perspective on the future development of control strategies, suggesting that hybrid control methods should be used for future research to leverage the strength of both sides. Notably, it emphasises the role of artificial intelligence (AI) in advancing control strategies, demonstrating its significant potential in facilitating the transition from automation to autonomy.

基于质子交换膜(PEM)的电化学系统可在燃料电池(PEMFC)和水电解槽(PEMWE)模式下运行,从而实现高效氢能利用和绿色制氢。除基本的电池堆外,PEMFC 或 PEMWE 系统还包括四个子系统,分别用于管理气体供应、电力、热力和水。由于系统的复杂性,即使是某个子系统的微小波动也会导致意外反应,从而降低性能和稳定性。为了提高系统的鲁棒性和响应能力,人们致力于开发先进的控制策略。本文全面回顾了文献中提出的各种控制策略,揭示了传统控制方法因其简单性而被广泛应用于 PEMFC 和 PEMWE,但在精度方面存在局限性。相反,先进的控制方法精度高,但动态性能差。本文重点介绍了结合机器学习算法的控制策略的最新进展。此外,本文还对控制策略的未来发展提出了展望,建议在未来的研究中采用混合控制方法,以充分利用双方的优势。值得注意的是,论文强调了人工智能(AI)在推进控制策略方面的作用,展示了人工智能在促进从自动化向自主化过渡方面的巨大潜力。
{"title":"A review of control strategies for proton exchange membrane (PEM) fuel cells and water electrolysers: From automation to autonomy","authors":"Jiahao Mao ,&nbsp;Zheng Li ,&nbsp;Jin Xuan ,&nbsp;Xinli Du ,&nbsp;Meng Ni ,&nbsp;Lei Xing","doi":"10.1016/j.egyai.2024.100406","DOIUrl":"10.1016/j.egyai.2024.100406","url":null,"abstract":"<div><p>Proton exchange membrane (PEM) based electrochemical systems have the capability to operate in fuel cell (PEMFC) and water electrolyser (PEMWE) modes, enabling efficient hydrogen energy utilisation and green hydrogen production. In addition to the essential cell stacks, the system of PEMFC or PEMWE consists of four sub-systems for managing gas supply, power, thermal, and water, respectively. Due to the system's complexity, even a small fluctuation in a certain sub-system can result in an unexpected response, leading to a reduced performance and stability. To improve the system's robustness and responsiveness, considerable efforts have been dedicated to developing advanced control strategies. This paper comprehensively reviews various control strategies proposed in literature, revealing that traditional control methods are widely employed in PEMFC and PEMWE due to their simplicity, yet they suffer from limitations in accuracy. Conversely, advanced control methods offer high accuracy but are hindered by poor dynamic performance. This paper highlights the recent advancements in control strategies incorporating machine learning algorithms. Additionally, the paper provides a perspective on the future development of control strategies, suggesting that hybrid control methods should be used for future research to leverage the strength of both sides. Notably, it emphasises the role of artificial intelligence (AI) in advancing control strategies, demonstrating its significant potential in facilitating the transition from automation to autonomy.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100406"},"PeriodicalIF":9.6,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000727/pdfft?md5=e5dd0e37800dc069bc5b04e7343ae983&pid=1-s2.0-S2666546824000727-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141844666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
期刊
Energy and AI
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1