Energy and AI最新文献_第3页

Optimization of dual-layer flow field in a water electrolyzer using a data-driven surrogate model 利用数据驱动代用模型优化水电解槽中的双层流场

IF 9.6

Energy and AI Pub Date : 2024-08-21 DOI: 10.1016/j.egyai.2024.100411

{"title":"Optimization of dual-layer flow field in a water electrolyzer using a data-driven surrogate model","authors":"","doi":"10.1016/j.egyai.2024.100411","DOIUrl":"10.1016/j.egyai.2024.100411","url":null,"abstract":"<div><p>Serious bubble clogging in flow-field channels will hinder the water supply to the electrode of proton exchange membrane water electrolyzer (PEMWE), deteriorating the cell performance. In order to address this issue, the dual-layer flow field design has been proposed in our previous study. In this study, the VOF (volume of fluid) method is utilized to investigate the effects of different degassing layer and base heights on the bubble behavior in channel and determine the time for the bubbles to detach from the electrode surface. However, it is very time-consuming to get the optimal combination of base layer and degassing layer heights due to the large number of potential cases, which needs to be calculated through computation-intensive physical model. Therefore, machine learning methods are adopted to accelerate the optimization. A data-driven surrogate model based on deep neural network (DNN) is developed and successfully trained using data obtained by the physical VOF method. Based on the highly efficient surrogate, genetic algorithm (GA) is further utilized to determine the optimal heights of base layer and degassing layer. Finally, the reliability of the optimization was validated by bubble visualization in channel and electrochemical characterization in PEMWE through experiments.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000776/pdfft?md5=9b46103bb566dce9bcfe4afe271d8e12&pid=1-s2.0-S2666546824000776-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142050351","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

BCLH2Pro: A novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes BCLH2Pro：通过生物质化学循环过程中的机器学习预测制氢的新型计算工具方法

IF 9.6

Energy and AI Pub Date : 2024-08-13 DOI: 10.1016/j.egyai.2024.100414

{"title":"BCLH2Pro: A novel computational tools approach for hydrogen production prediction via machine learning in biomass chemical looping processes","authors":"","doi":"10.1016/j.egyai.2024.100414","DOIUrl":"10.1016/j.egyai.2024.100414","url":null,"abstract":"<div><p>This study optimizes biomass chemical looping processes (BCLpro), a technique for converting biomass to energy, through machine learning (ML) for sustainable energy production. The study proposes an integrated Fe<sub>2</sub>O<sub>3</sub>-based ฺBCLpro combining steam gasification for H<sub>2</sub> production. Aspen Plus is used as the primary tool to generate extensive datasets covering 24 biomass types with 18 feature inputs in a supervised model. A methodology involving K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), Support Vector Machine (SVM), Random Forest (RF), and CatBoost (CB) algorithms was employed to predict H<sub>2</sub> yields in the BCLpro, utilizing 10-fold cross-validation for robust model evaluation. Findings highlight the CB algorithm's superior performance, achieving up to 98% predictive accuracy, with carbon content, reducer temperature, and Fe<sub>2</sub>O<sub>3</sub>/Al<sub>2</sub>O<sub>3</sub> mass ratio identified as crucial features. The algorithm has been developed into a user-friendly tool, BCLH2Pro, accessible via a web server. This tool is designed to assist in reducing costs, optimizing biomass selection, and planning operational conditions to maximize H<sub>2</sub> yield in BCLpro systems. Access to the tool can be obtained through the following link: <span><span>http://bclh2pro.pythonanywhere.com/</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000806/pdfft?md5=1bf861d91694bc24b779a2308fd3e75c&pid=1-s2.0-S2666546824000806-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142096811","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

Machine learning modeling for fuel cell-battery hybrid power system dynamics in a Toyota Mirai 2 vehicle under various drive cycles 各种驱动循环下丰田 Mirai 2 汽车燃料电池-电池混合动力系统动力学的机器学习建模

IF 9.6

Energy and AI Pub Date : 2024-08-13 DOI: 10.1016/j.egyai.2024.100415

{"title":"Machine learning modeling for fuel cell-battery hybrid power system dynamics in a Toyota Mirai 2 vehicle under various drive cycles","authors":"","doi":"10.1016/j.egyai.2024.100415","DOIUrl":"10.1016/j.egyai.2024.100415","url":null,"abstract":"<div><p>Electrification is considered essential for the decarbonization of mobility sector, and understanding and modeling the complex behavior of modern fuel cell-battery electric-electric hybrid power systems is challenging, especially for product development and diagnostics requiring quick turnaround and fast computation. In this study, a novel modeling approach is developed, utilizing supervised machine learning algorithms, to replicate the dynamic characteristics of the fuel cell-battery hybrid power system in a 2021 Toyota Mirai 2nd generation (Mirai 2) vehicle under various drive cycles. The entire data for this study is collected by instrumenting the Mirai vehicle with in-house data acquisition devices and tapping into the Mirai controller area network bus during chassis dynamometer tests. A multi-input - multi-output, feed-forward artificial neural network architecture is designed to predict not only the fuel cell attributes, such as average minimum cell voltage, coolant and cathode air outlet temperatures, but also the battery hybrid system attributes, including lithium-ion battery pack voltage and temperature with the help of 15 system operating parameters. Over 21,0000 data points on various drive cycles having combinations of transient and near steady-state driving conditions are collected, out of which around 15,000 points are used for training the network and 6,000 for the evaluation of the model performance. Various data filtration techniques and neural network calibration processes are explored to condition the data and understand the impact on model performance. The calibrated neural network accurately predicts the hybrid power system dynamics with an R-squared value greater than 0.98, demonstrating the potential of machine learning algorithms for system development and diagnostics.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":null,"pages":null},"PeriodicalIF":9.6,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000818/pdfft?md5=082215c925da11b34457d3b7bdf0c6fb&pid=1-s2.0-S2666546824000818-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142006357","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

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

Energy and AI Pub Date : 2024-08-13 DOI: 10.1016/j.egyai.2024.100410

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

Energy and AI Pub Date : 2024-08-12 DOI: 10.1016/j.egyai.2024.100409

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

Energy and AI Pub Date : 2024-08-09 DOI: 10.1016/j.egyai.2024.100412

{"title":"Cross-domain diagnosis for polymer electrolyte membrane fuel cell based on digital twins and transfer learning network✰","authors":"","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":null,"pages":null},"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

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

Energy and AI Pub Date : 2024-08-09 DOI: 10.1016/j.egyai.2024.100413

{"title":"Data-driven strategy for state of health prediction and anomaly detection in lithium-ion batteries","authors":"","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":null,"pages":null},"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

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

Energy and AI Pub Date : 2024-08-04 DOI: 10.1016/j.egyai.2024.100407

{"title":"Energy Disaggregation of Industrial Machinery Utilizing Artificial Neural Networks for Non-intrusive Load Monitoring","authors":"","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":null,"pages":null},"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

Energy and AI Pub Date : 2024-08-03 DOI: 10.1016/j.egyai.2024.100405

{"title":"Enhanced vision-transformer integrating with semi-supervised transfer learning for state of health and remaining useful life estimation of lithium-ion batteries","authors":"","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":null,"pages":null},"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

Energy and AI Pub Date : 2024-08-03 DOI: 10.1016/j.egyai.2024.100403

{"title":"An artificial intelligence framework for explainable drift detection in energy forecasting","authors":"","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":null,"pages":null},"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