Energy and AI最新文献_第6页

Law of conservation-guided neural network with gradient aggregation for improved energy efficiency optimization in industrial processes 基于梯度聚集的守恒导向神经网络改进工业过程能效优化规律

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 基于数据驱动建模的PPCI柴油机燃烧过程多目标控制研究

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 利用分布式GPU计算实现广义图卷积网络在电网可靠性评估中的应用

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 锂离子电池寿命可靠性和风险函数预测的鲁棒生存模型

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 基于混合算法的船舶燃料电池系统智能自适应滑模控制

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

Topologically consistent regression modeling exemplified for laminar burning velocity of ammonia-hydrogen flames 以氨氢火焰层流燃烧速度的拓扑一致回归模型为例

IF 9.6

Energy and AI Pub Date : 2025-01-01 DOI: 10.1016/j.egyai.2024.100456

Hui Du , Tianyu Wang , Haogang Wei , Guy Y. Cornejo Maceda , Bernd R. Noack , Lei Zhou

{"title":"Topologically consistent regression modeling exemplified for laminar burning velocity of ammonia-hydrogen flames","authors":"Hui Du , Tianyu Wang , Haogang Wei , Guy Y. Cornejo Maceda , Bernd R. Noack , Lei Zhou","doi":"10.1016/j.egyai.2024.100456","DOIUrl":"10.1016/j.egyai.2024.100456","url":null,"abstract":"<div><div>Data-driven regression models are generally calibrated by minimizing a representation error. However, optimizing the model accuracy may create nonphysical wiggles. In this study, we propose topological consistency as a new metric to mitigate these wiggles. The key enabler is Persistent Data Topology (PDT) which extracts a topological skeleton from discrete scalar field data. PDT identifies the extrema of the model based on a neighborhood analysis. The topological error is defined as the mismatch of extrema between the data and the model. The methodology is exemplified for the modeling of the Laminar Burning Velocity (<span><math><mrow><mi>L</mi><mi>B</mi><mi>V</mi></mrow></math></span>) of ammonia-hydrogen flames. Four regression models, Multi-layer Perceptron (MLP), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Light Gradient Boosting Machine (Light GBM), are trained using the data generated by a modified GRI3.0 mechanism. In comparison, MLP builds a model that achieves the highest accuracy and preserves the topological structure of the data. We expect that the proposed topologically consistent regression modeling will enjoy many more applications in model calibration, model selection and optimization algorithms.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"19 ","pages":"Article 100456"},"PeriodicalIF":9.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155831","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

Probabilistic forecasting of multiple plant day-ahead renewable power generation sequences with data privacy preserving 具有数据隐私保护的多电厂日前可再生能源发电序列概率预测

IF 9.6

Energy and AI Pub Date : 2025-01-01 DOI: 10.1016/j.egyai.2024.100463

Hong Liu , Zijun Zhang

{"title":"Probabilistic forecasting of multiple plant day-ahead renewable power generation sequences with data privacy preserving","authors":"Hong Liu , Zijun Zhang","doi":"10.1016/j.egyai.2024.100463","DOIUrl":"10.1016/j.egyai.2024.100463","url":null,"abstract":"<div><div>This paper studies the renewable power forecasting task with a more advanced formulation, the probabilistic forecasts of day-ahead power generation sequences of multiple renewable power plants without breaching the privacy of data in each plant. To realize such a task, an advanced domain-invariant feature learning embedded federated learning (DIFL) framework is proposed to coordinate the development of a system of deep network-based models serving as multiple clients and one server. In DIFL, each client, which serves each local renewable power plant, maps its raw data input into latent features via a local feature extractor and generates power output sequence probabilistic forecasts via a locally hosted forecasting model. The cloud-hosted server first aggregates the knowledge from models of clients and next dispatches the aggregated model back to each client for facilitating each local feature extractor to identify domain-invariant features via interacting with a server-side discriminator. Therefore, only desensitized data, such as parameters of the models, are allowed to be transmitted among end users for preserving local data privacy of power plants. To verify the advantages of the DIFL, a preliminary exploration of its theoretical property is first conducted. Next, computational studies are performed to benchmark the DIFL against famous baselines based on datasets collected from commercial renewable power plants. Results further confirm that, in terms of the averaged performance, the DIFL consistently realizes improvements against all benchmarks based on both real wind farm and solar power plant datasets.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"19 ","pages":"Article 100463"},"PeriodicalIF":9.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155836","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

Using crafted features and polar bear optimization algorithm for short-term electric load forecast system 利用精构特征和北极熊优化算法进行短期电力负荷预测系统

IF 9.6

Energy and AI Pub Date : 2025-01-01 DOI: 10.1016/j.egyai.2025.100470

Mansi Bhatnagar, Gregor Rozinaj, Radoslav Vargic

{"title":"Using crafted features and polar bear optimization algorithm for short-term electric load forecast system","authors":"Mansi Bhatnagar, Gregor Rozinaj, Radoslav Vargic","doi":"10.1016/j.egyai.2025.100470","DOIUrl":"10.1016/j.egyai.2025.100470","url":null,"abstract":"<div><div>Short-term load forecasting (STLF) can be utilized to predict usage fluctuation in a short time period and accurate forecasting can save a big chunk of a country's economic loss. This paper introduces the crafting of various features for hourly electric load forecasting on three different datasets using four different models XGBoost, LightGBM, Bi-LSTM, and Random Forest. The importance of crafted features over basic features was analysed by different evaluation metrics MAE, RMSE, R-squared, and MAPE. Evaluation metrics showed that prediction accuracy increased significantly with crafted features in comparison to basic features for all four models. We also showcased the ability of the Polar Bear Optimisation (PBO) algorithm for hyperparameter tuning of the machine learning models in STLF. Optimized hyperparameters with PBO effectively decreased RMSE, MAE, and MAPE and improved the model prediction, showcasing the capability of the PBO in hyperparameter tuning for STLF. PBO was compared with commonly used optimization algorithms like particle swarm optimization (PSO) and genetic algorithm (GA). GA was the least performing with XGBoost, LightGBM, and Random Forest. PSO and PBO were comparable with XGBoost LightGBM and Random Forest while PBO highly surpassed PSO with the Bi-LSTM model. Hence PBO was proved to be highly effective for hyperparameter tuning for implementation in short-term electric load forecasting.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"19 ","pages":"Article 100470"},"PeriodicalIF":9.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155421","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

Emission prediction and optimization of methanol/diesel dual-fuel engines based on ITransformer-BiGRU and NSGA-III 基于ittransformer - bigru和NSGA-III的甲醇/柴油双燃料发动机排放预测与优化

IF 9.6

Energy and AI Pub Date : 2025-01-01 DOI: 10.1016/j.egyai.2024.100466

Mingzhang Pan , Xinxin Cao , Changcheng Fu , Shengyou Liao , Xiaorong Zhou , Wei Guan

{"title":"Emission prediction and optimization of methanol/diesel dual-fuel engines based on ITransformer-BiGRU and NSGA-III","authors":"Mingzhang Pan , Xinxin Cao , Changcheng Fu , Shengyou Liao , Xiaorong Zhou , Wei Guan","doi":"10.1016/j.egyai.2024.100466","DOIUrl":"10.1016/j.egyai.2024.100466","url":null,"abstract":"<div><div>To reduce engine pollutant emissions, an emission modeling and optimization scheme based on a hybrid artificial intelligence scheme is proposed in this study to reduce pollutant emissions of methanol/diesel dual-fuel engines under low load. Firstly, a data cleaning method based on isolated forest and correlation analysis is designed to improve the stability of the system. Secondly, a hybrid emission prediction model based on improved Transformer (ITransformer) and Bidirectional Gated Recurrent Unit (BiGRU) is built to obtain an accurate mathematical model between control parameters and emissions. Finally, based on the obtained mathematical model, the 3rd Non-dominated Sorting Genetic Algorithm (NGSA-III) is used to adjust and optimize the control parameters. Using engine bench test data to evaluate the proposed hybrid emission prediction model, the R<sup>2</sup> of CO, HC, and NO<sub>x</sub> prediction is 0.9969, 0.9973, and 0.9982, respectively, which is higher than the accuracy of the seven existing modeling methods. Compared with the unoptimized MESR46, the CO, HC, and NO<sub>x</sub> emissions of the optimized scheme are reduced by at least 45.17 %, 15.30 %, and 17.32 % respectively, which can significantly reduce the CO, HC, and NO<sub>x</sub> emissions, and comparison and analysis with the most advanced optimization technologies show a competitive optimization effect.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"19 ","pages":"Article 100466"},"PeriodicalIF":9.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155962","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 co-estimation framework of state of health and remaining useful life for lithium-ion batteries using the semi-supervised learning algorithm 基于半监督学习算法的锂离子电池健康状态和剩余使用寿命联合估计框架

IF 9.6

Energy and AI Pub Date : 2025-01-01 DOI: 10.1016/j.egyai.2024.100458

Xiaoyu Li , Mohan Lyv , Xiao Gao , Kuo Li , Yanli Zhu

{"title":"A co-estimation framework of state of health and remaining useful life for lithium-ion batteries using the semi-supervised learning algorithm","authors":"Xiaoyu Li , Mohan Lyv , Xiao Gao , Kuo Li , Yanli Zhu","doi":"10.1016/j.egyai.2024.100458","DOIUrl":"10.1016/j.egyai.2024.100458","url":null,"abstract":"<div><div>To ensure the safe operation of lithium-ion batteries, it is crucial to accurately predict their state of health (SOH) and remaining useful life (RUL). Addressing the issue of high costs and time consumption due to the reliance on large amounts of labeled data in existing models, this paper proposes a co-estimation framework that combines semi-supervised learning (SSL) with long short-term memory networks (LSTM), effectively utilizing unlabeled data. By selecting the most strongly correlated battery health features and constructing a degradation model using a hybrid dataset, the need for labeling is reduced. The verification results indicate SOH estimated error is reduced to 4 % and the maximum root mean square error (RMSE) is 1.58 %. When utilizing 75 % SOH as the end-of-life criterion for battery cycle life, the mean absolute error (MAE) of the RUL predictions for the two tested batteries are 2.5281 and 0.0562 cycles, respectively. The results prove the framework enables accurate prediction and has wide practicability and universal applicability.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"19 ","pages":"Article 100458"},"PeriodicalIF":9.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143155834","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