Energy and AI最新文献_第2页

Data-driven modeling of open circuit voltage hysteresis for LiFePO4 batteries with conditional generative adversarial network

IF 9.6

Energy and AI Pub Date : 2025-01-31 DOI: 10.1016/j.egyai.2025.100478

Lisen Yan , Jun Peng , Zeyu Zhu , Heng Li , Zhiwu Huang , Dirk Uwe Sauer , Weihan Li

{"title":"Data-driven modeling of open circuit voltage hysteresis for LiFePO4 batteries with conditional generative adversarial network","authors":"Lisen Yan , Jun Peng , Zeyu Zhu , Heng Li , Zhiwu Huang , Dirk Uwe Sauer , Weihan Li","doi":"10.1016/j.egyai.2025.100478","DOIUrl":"10.1016/j.egyai.2025.100478","url":null,"abstract":"<div><div>The hysteresis effect represents the difference in open circuit voltage (OCV) between the charge and discharge processes of batteries. An accurate estimation of open circuit voltage considering hysteresis is critical for precise modeling of <span><math><msub><mrow><mtext>LiFePO</mtext></mrow><mrow><mtext>4</mtext></mrow></msub></math></span> batteries. However, the intricate influence of state-of-charge (SOC), temperature, and battery aging have posed significant challenges for hysteresis modeling, which have not been comprehensively considered in existing studies. This paper proposes a data-driven approach with adversarial learning to model hysteresis under diverse conditions, addressing the intricate dependencies on SOC, temperature, and battery aging. First, a comprehensive experimental scheme is designed to collect hysteresis dataset under diverse SOC paths, temperatures and aging states. Second, the proposed data-driven model integrates a conditional generative adversarial network with long short-term memory networks to enhance the model’s accuracy and adaptability. The generator and discriminator are designed based on LSTM networks to capture the dependency of hysteresis on historical SOC and conditional information. Third, the conditional matrix, incorporating temperature, health state, and historical paths, is constructed to provide the scenario-specific information for the adversarial network, thereby enhancing the model’s adaptability. Experimental results demonstrate that the proposed model achieves a voltage error of less than 3.8 mV across various conditions, with accuracy improvements of 31.3–48.7% compared to three state-of-the-art models.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100478"},"PeriodicalIF":9.6,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143094836","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

Optimising quantile-based trading strategies in electricity arbitrage

IF 9.6

Energy and AI Pub Date : 2025-01-27 DOI: 10.1016/j.egyai.2025.100476

Ciaran O’Connor , Joseph Collins , Steven Prestwich , Andrea Visentin

{"title":"Optimising quantile-based trading strategies in electricity arbitrage","authors":"Ciaran O’Connor , Joseph Collins , Steven Prestwich , Andrea Visentin","doi":"10.1016/j.egyai.2025.100476","DOIUrl":"10.1016/j.egyai.2025.100476","url":null,"abstract":"<div><div>Efficiently integrating renewable resources into electricity markets is vital for addressing the challenges of matching real-time supply and demand while mitigating revenue losses caused by curtailments. To address this challenge effectively, the incorporation of storage devices can enhance the reliability and efficiency of the grid, improving market liquidity and reducing price volatility. In short-term electricity markets, participants face numerous options, each presenting unique challenges and opportunities, with trading strategies fundamental towards maximising profits. This study explores the optimisation of day-ahead and balancing market trading in the Irish electricity market from 2019 to 2022, leveraging quantile-based forecasts. Employing three trading approaches with practical constraints, our research evaluates trading strategies, increases trading frequency, and employs flexible timestamp orders. Our findings underscore the profit potential of simultaneous participation in both day-ahead and balancing markets, especially with larger battery storage systems; despite increased costs and narrower profit margins associated with higher-volume trading, with the implementation of dynamic dual-market strategies playing a significant role in maximising profits and addressing market challenges. Finally, we evaluate the economic viability of four commercial battery storage systems through scenario analysis, showing that larger batteries achieve shorter returns on investment.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100476"},"PeriodicalIF":9.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143094837","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

Physics-Informed Neural Network for modeling and predicting temperature fluctuations in proton exchange membrane electrolysis

IF 9.6

Energy and AI Pub Date : 2025-01-22 DOI: 10.1016/j.egyai.2025.100474

Islam Zerrougui , Zhongliang Li , Daniel Hissel

{"title":"Physics-Informed Neural Network for modeling and predicting temperature fluctuations in proton exchange membrane electrolysis","authors":"Islam Zerrougui , Zhongliang Li , Daniel Hissel","doi":"10.1016/j.egyai.2025.100474","DOIUrl":"10.1016/j.egyai.2025.100474","url":null,"abstract":"<div><div>Proton Exchange Membrane (PEM) electrolysis stands as a cornerstone technology in the clean energy sector, driving the production of hydrogen and oxygen from water. A critical aspect of ensuring the efficiency and safety of this process lies in the precise monitoring and control of temperature at the electrolysis outlet. However, accurately characterizing temperature changes within the PEM electrolysis system can be challenging due to the fluctuation of renewable energies. This study introduces an approach integrating data with fundamental physics principles known as Physics-Informed Neural Networks (PINNs). This method solves differential equations and estimates the unknown parameters governing the temperature dynamics within the PEM electrolysis system. We consider two distinct scenarios: a zero-dimensional model and a one-dimensional model. The results demonstrate the PINN’s proficiency in accurately identifying the parameters and solving for temperature fluctuations within the system with different input conditions. Furthermore, we compare the PINN with the Long Short-Term Memory (LSTM) method to predict the outlet temperature of the electrolysis. The PINN outperformed the LSTM method, highlighting its reliability and precision, achieving a Mean Squared Error (MSE) of 0.1596 compared to 1.2132 for LSTM models. The proposed method shows a high performance in dealing with sensor noises and avoids overfitting problems. This synergy of physics knowledge and data-driven learning opens new pathways towards real-time digital twins, enhanced predictive control, and improved reliability for PEM electrolysis and other complex, data-scarce energy systems.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100474"},"PeriodicalIF":9.6,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143094839","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

Methodology for predicting material performance by context-based modeling: A case study on solid amine CO2 adsorbents

IF 9.6

Energy and AI Pub Date : 2025-01-21 DOI: 10.1016/j.egyai.2025.100477

Shuangjun Li , Zhixin Huang , Yuanming Li , Shuai Deng , Xiangkun Elvis Cao

{"title":"Methodology for predicting material performance by context-based modeling: A case study on solid amine CO2 adsorbents","authors":"Shuangjun Li , Zhixin Huang , Yuanming Li , Shuai Deng , Xiangkun Elvis Cao","doi":"10.1016/j.egyai.2025.100477","DOIUrl":"10.1016/j.egyai.2025.100477","url":null,"abstract":"<div><div>Traditional materials informatics leverages big data and machine learning (ML) to forecast material performance based on structural features but often overlooks valuable textual information. In this work, we proposed a novel methodology for predicting material performance through context-based modeling using large language models (LLMs). This method integrates both numerical and textual information, enhancing predictive accuracy and scalability. In the case study, the approach is applied to predict the performance of solid amine CO<sub>2</sub> adsorbents under direct air capture (DAC) conditions. ChatGPT 4o model was used to employ in-context learning to predict CO<sub>2</sub> adsorption uptake based on input features, including material properties and experimental conditions. The results show that context-based modeling can reduce prediction error in comparison to traditional ML models in the prediction task. We adopted Sapley Additive exPlanations (SHAP) to further elucidate the importance of various input features. This work highlights the potential of LLMs in materials science, offering a cost-effective, efficient solution for complex predictive tasks.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100477"},"PeriodicalIF":9.6,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143094838","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

Comparison of Kolmogorov–Arnold Networks and Multi-Layer Perceptron for modelling and optimisation analysis of energy systems

IF 9.6

Energy and AI Pub Date : 2025-01-16 DOI: 10.1016/j.egyai.2025.100473

Talha Ansar , Waqar Muhammad Ashraf

{"title":"Comparison of Kolmogorov–Arnold Networks and Multi-Layer Perceptron for modelling and optimisation analysis of energy systems","authors":"Talha Ansar , Waqar Muhammad Ashraf","doi":"10.1016/j.egyai.2025.100473","DOIUrl":"10.1016/j.egyai.2025.100473","url":null,"abstract":"<div><div>Considering the improved interpretable performance of Kolmogorov–Arnold Networks (KAN) algorithm compared to multi-layer perceptron (MLP) algorithm, a fundamental research question arises on how modifying the loss function of KAN affects its modelling performance for energy systems, particularly industrial-scale thermal power plants. In this regard, first, we modify the loss function of both KAN and MLP algorithms and embed Pearson Correlation Coefficient (PCC). Second, the algorithmic configurations built on PCC, i.e., KAN_PCC and MLP_PCC as well as original architecture of KAN and MLP are deployed for modelling and optimisation analyses for two case studies of energy systems: (i) energy efficiency cooling and energy efficiency heating of buildings, and (ii) power generation operation of 660 MW capacity thermal power plant. The analysis reveals superior modelling performance of KAN and KAN_PCC algorithms than those of MLP and MLP_PCC for the two case studies. KAN models are embedded in the optimisation framework of nonlinear programming and feasible optimal solutions are estimated, maximising thermal efficiency up to 42.17 ± 0.88 % and minimising turbine heat rate to 7487 ± 129 kJ/kWh corresponding to power generation of 500 ± 14 MW for the thermal power plant. It is anticipated that the scientific, research and industrial community may benefit from the fundamental insights presented in this paper for the ML algorithm selection and carrying out model-based optimisation analysis for the performance enhancement of energy systems.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100473"},"PeriodicalIF":9.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143094840","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

Law of conservation-guided neural network with gradient aggregation for improved energy efficiency optimization in industrial processes

IF 9.6

Energy and AI Pub Date : 2025-01-16 DOI: 10.1016/j.egyai.2025.100475

Santi Bardeeniz , Chanin Panjapornpon , Moonyong Lee

{"title":"Law of conservation-guided neural network with gradient aggregation for improved energy efficiency optimization in industrial processes","authors":"Santi Bardeeniz , Chanin Panjapornpon , Moonyong Lee","doi":"10.1016/j.egyai.2025.100475","DOIUrl":"10.1016/j.egyai.2025.100475","url":null,"abstract":"<div><div>Energy efficiency in industrial systems remains a critical challenge, with traditional data-driven models often limited by model accuracy and data availability. Incorporation of physical laws governing energy systems can improve performance and physical consistency, but the model often struggles with the calculation of loss and ignores dynamic interplays between sub-systems, which can result in oversimplification and a lack of practical applicability. Therefore, this study investigated a theoretical framework for developing a law of conservation-guided neural network aimed at enhancing energy efficiency prediction in industrial systems. The framework integrates physical principles directly into floating nodes constructed using a long short-term memory architecture to help the model formulate the relationship between process variables, while gradient aggregation increases liquidity and interpretability. Through evaluation of two large-scale case studies—vinyl chloride monomer and detergent powder production—the proposed model produced substantial improvements in prediction accuracy and model reliability, with a test prediction improvement of 12.2 % and 5.87 % over published methods. Compared to network architecture modification approaches, the proposed model provided higher reliability and reproducibility in energy efficiency predictions. Moreover, the model successfully identified energy inefficiencies, resulting in a 4.21 % reduction in energy consumption and a corresponding 377.35 tons of carbon emissions reduction.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"20 ","pages":"Article 100475"},"PeriodicalIF":9.6,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143094841","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

Research on multi-objective control of PPCI diesel engine combustion process based on data driven modelling

IF 9.6

Energy and AI Pub Date : 2025-01-01 DOI: 10.1016/j.egyai.2025.100472

Ziqiang Chen , Peng Ju , Zhe Wang , Du Huang , Lei Shi , Kangyao Deng

{"title":"Research on multi-objective control of PPCI diesel engine combustion process based on data driven modelling","authors":"Ziqiang Chen , Peng Ju , Zhe Wang , Du Huang , Lei Shi , Kangyao Deng","doi":"10.1016/j.egyai.2025.100472","DOIUrl":"10.1016/j.egyai.2025.100472","url":null,"abstract":"<div><div>Control of combustion stability in partial pre-mixed compression ignition (PPCI) engine is one of the main issues facing its application. However, the multi-parameter coupling and nonlinear increase in the combustion process make the model and controller design more difficult. Therefore, this study proposed a diesel engine control method that combines neural networks and model-free adaptive control in the absence of model and controller structure, which can achieve real-time coordination control of crank angle at 50 % of total heat release (CA50) and indicated mean effective pressure (IMEP) in the PPCI combustion process. Through comparisons under different operating conditions, it was found that the adjustment of algorithm parameters needs to adapt to the sensitivity changes of control parameters. In addition, the study validated the real-time performance and control effect of the algorithm, the experimental results indicate that the execution time of the control algorithm is approximately 5.59 milliseconds, which satisfies the real-time control requirements for the combustion process. By adjusting the weight coefficient matrix of the control authority, CA50 and IMEP are effectively tracked within the constraints of maximum pressure rise rate. The control error for CA50 remains within ±2.7 %, while that for IMEP is confined to ±1 %. Furthermore, the root mean square error for CA50 is measured at 1.1 crank angle, and for IMEP it stands at 23.5 kPa, thereby achieving precise real-time control of the PPCI combustion process.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"19 ","pages":"Article 100472"},"PeriodicalIF":9.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155420","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

Harnessing distributed GPU computing for generalizable graph convolutional networks in power grid reliability assessments

IF 9.6

Energy and AI Pub Date : 2025-01-01 DOI: 10.1016/j.egyai.2025.100471

Somayajulu L.N. Dhulipala , Nicholas Casaprima , Audrey Olivier , Bjorn C. Vaagensmith , Timothy R. McJunkin , Ryan C. Hruska

{"title":"Harnessing distributed GPU computing for generalizable graph convolutional networks in power grid reliability assessments","authors":"Somayajulu L.N. Dhulipala , Nicholas Casaprima , Audrey Olivier , Bjorn C. Vaagensmith , Timothy R. McJunkin , Ryan C. Hruska","doi":"10.1016/j.egyai.2025.100471","DOIUrl":"10.1016/j.egyai.2025.100471","url":null,"abstract":"<div><div>Although machine learning (ML) has emerged as a powerful tool for rapidly assessing grid contingencies, prior studies have largely considered a static grid topology in their analyses. This limits their application, since they need to be re-trained for every new topology. This paper explores the development of generalizable graph convolutional network (GCN) models by pre-training them across a wide range of grid topologies and contingency types. We found that a GCN model with auto-regressive moving average (ARMA) layers with a line graph representation of the grid offered the best predictive performance in predicting voltage magnitudes (VM) and voltage angles (VA). We introduced the concept of phantom nodes to consider disparate grid topologies with a varying number of nodes and lines. For pre-training the GCN ARMA model across a variety of topologies, distributed graphics processing unit (GPU) computing afforded us significant training scalability. The predictive performance of this model on grid topologies that were part of the training data is substantially better than the direct current (DC) approximation. Although direct application of the pre-trained model to topologies that are not part of the grid is not particularly satisfactory, fine-tuning with small amounts of data from a specific topology of interest significantly improves predictive performance. In the context of foundational models in ML, this paper highlights the feasibility of training large-scale GNN models to assess the reliability of power grids by considering a wide variety of grid topologies and contingency types.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"19 ","pages":"Article 100471"},"PeriodicalIF":9.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155419","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

Robust survival model for the prediction of Li-ion battery lifetime reliability and risk functions

IF 9.6

Energy and AI Pub Date : 2025-01-01 DOI: 10.1016/j.egyai.2024.100465

Rasheed Ibraheem , Timothy I. Cannings , Torben Sell , Gonçalo dos Reis

{"title":"Robust survival model for the prediction of Li-ion battery lifetime reliability and risk functions","authors":"Rasheed Ibraheem , Timothy I. Cannings , Torben Sell , Gonçalo dos Reis","doi":"10.1016/j.egyai.2024.100465","DOIUrl":"10.1016/j.egyai.2024.100465","url":null,"abstract":"<div><div>Single-value prediction such as the End of Life and Remaining Useful Life is a common method of estimating the lifetime of Li-ion batteries. Information from such prediction is limited when the entire degradation pattern is needed for practical applications such as dynamic adjustment of battery warranty, improved maintenance scheduling, and battery stock management. In this research, a predictive, semi-parametric survival model called the Cox Proportional Hazards is proposed for the prediction of cell degradation in the form of survival probability (battery reliability) and cumulative hazard (battery risk) functions. Once this model is trained, the two functions can be obtained directly for a new cell without having to predict several cogent points. The model is trained on the first 50 cycles of only the voltage profile from either the charge or discharge data regime, implying that our methodology is data region agnostic. The signature method with both desirable mathematical and machine learning properties was adopted as a feature extraction technique.</div><div>The developed models are tested rigorously using application-driven strategies involving model robustness to the number of cycles of data required for model training and prediction, different fractions of training samples, and systematic data sparsity. The codes for modeling and testing are publicly available.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"19 ","pages":"Article 100465"},"PeriodicalIF":9.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155963","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

Novel intelligent adaptive sliding mode control for marine fuel cell system via hybrid algorithm

IF 9.6

Energy and AI Pub Date : 2025-01-01 DOI: 10.1016/j.egyai.2024.100464

Shiyi Fang, Daifen Chen, Xinyu Fan

引用次数: 0