Energy and AI最新文献

Predicting the thermal conductivity of polymer composites with one-dimensional oriented fillers using the combination of deep learning and ensemble learning 利用深度学习和集合学习相结合的方法预测带有一维定向填料的聚合物复合材料的导热性能

IF 9.6

Energy and AI Pub Date : 2024-11-08 DOI: 10.1016/j.egyai.2024.100445

Yinzhou Liu , Weidong Zheng , Haoqiang Ai , Lin Cheng , Ruiqiang Guo , Xiaohan Song

{"title":"Predicting the thermal conductivity of polymer composites with one-dimensional oriented fillers using the combination of deep learning and ensemble learning","authors":"Yinzhou Liu , Weidong Zheng , Haoqiang Ai , Lin Cheng , Ruiqiang Guo , Xiaohan Song","doi":"10.1016/j.egyai.2024.100445","DOIUrl":"10.1016/j.egyai.2024.100445","url":null,"abstract":"<div><div>Polymer composites with one-dimensional (1D) oriented fillers, recognized for their high thermal conductivity (TC), are extensively utilized in cooling electronic components. However, the prediction of the TC of composites with 1D oriented fillers poses a challenge due to the significant impact of filler orientation on composite TC. In this paper, we use a strategy that combines deep learning and ensemble learning to efficiently and quickly predict the TC of composites with 1D oriented fillers. First, as a control, we used convolutional neural network (CNN) model to predict the TC of 1D carbon fiber-epoxy composite, and the R-squared (R<sup>2</sup>) on the test set reached 0.924. However, for composites consist of different matrices and fillers, the CNN model needs to be retrained, which greatly wastes computing resources. Therefore, we define a descriptor ‘Orientation degree (<em>O<sub>d</sub></em>)’ to quantitatively describe the spatial distribution of the 1D fillers. CNN model was used to predict this structural parameter, the accuracy R<sup>2</sup> can reach 0.950 on the test set. Using <em>O<sub>d</sub></em> as a feature, random forest regression (RFR) was used to predict the TC, and the accuracy R<sup>2</sup> reached 0.954 on the test set, which was higher than that of CNN control group. We further successfully extended this strategy to composites consist of different 1D fillers and matrices, and only one CNN model and one RFR model needed to be trained to achieve fast and accurate TC prediction. This strategy provides valuable insights and guidance for machine learning-based material property prediction.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100445"},"PeriodicalIF":9.6,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659395","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 hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction 基于季节特征分解和增强特征提取的混合风能预测模型

IF 9.6

Energy and AI Pub Date : 2024-11-08 DOI: 10.1016/j.egyai.2024.100442

Weipeng Li , Yuting Chong , Xin Guo , Jun Liu

引用次数: 0

Integrating local knowledge with ChatGPT-like large-scale language models for enhanced societal comprehension of carbon neutrality 将地方知识与类似 ChatGPT 的大规模语言模型相结合，提高社会对碳中和的理解能力

IF 9.6

Energy and AI Pub Date : 2024-11-07 DOI: 10.1016/j.egyai.2024.100440

Te Han, Rong-Gang Cong, Biying Yu, Baojun Tang, Yi-Ming Wei

{"title":"Integrating local knowledge with ChatGPT-like large-scale language models for enhanced societal comprehension of carbon neutrality","authors":"Te Han, Rong-Gang Cong, Biying Yu, Baojun Tang, Yi-Ming Wei","doi":"10.1016/j.egyai.2024.100440","DOIUrl":"10.1016/j.egyai.2024.100440","url":null,"abstract":"<div><div>Addressing carbon neutrality presents a multifaceted challenge, necessitating collaboration across various disciplines, fields, and societal stakeholders. With the increasing urgency to mitigate climate change, there is a crucial need for innovative approaches in communication and education to enhance societal understanding and engagement. Large-scale language models like ChatGPT emerge as transformative tools in the AI era, offering potential to revolutionize how we approach economic, technological, social, and environmental issues of achieving carbon neutrality. However, the full potential of these models in carbon neutrality is yet to be realized, hindered by limitations in providing detailed, localized, and expert-level insights across an expansive spectrum of subjects. To bridge these gaps, this paper introduces an innovative framework that integrates local knowledge with LLMs, aiming to markedly enhance the depth, accuracy, and regional relevance of the information provided. The effectiveness of this framework is examined from government, corporations, and community perspectives. The integration of local knowledge with LLMs not only enriches the AI’s comprehension of local specificities but also guarantees an up-to-date information that is crucial for addressing the specific concerns and questions about carbon neutrality raised by a broad array of stakeholders. Overall, the proposed framework showcases significant potential in enhancing societal comprehension and participation towards carbon neutrality.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100440"},"PeriodicalIF":9.6,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659516","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

Optimization of a Bayesian game for Peer-to-Peer trading among prosumers under incomplete information via a CNN-LSTM-ATT 通过 CNN-LSTM-ATT，优化不完全信息条件下的贝叶斯博弈，促进消费者之间的点对点交易

IF 9.6

Energy and AI Pub Date : 2024-11-06 DOI: 10.1016/j.egyai.2024.100437

Hongjie Jia , Wanxin Tang , Xiaolong Jin , Yunfei Mu , Dengxin Ai , Xiaodan Yu , Wei Wei

{"title":"Optimization of a Bayesian game for Peer-to-Peer trading among prosumers under incomplete information via a CNN-LSTM-ATT","authors":"Hongjie Jia , Wanxin Tang , Xiaolong Jin , Yunfei Mu , Dengxin Ai , Xiaodan Yu , Wei Wei","doi":"10.1016/j.egyai.2024.100437","DOIUrl":"10.1016/j.egyai.2024.100437","url":null,"abstract":"<div><div>In modern low-carbon industrial parks, various distributed renewable energy resources are employed to fulfill production needs. Despite the growing capacity of renewable energy generation, a significant portion of the power produced by these renewable resources remains unconsumed, resulting in a waste of resources. Within an industrial park, microgrids that both generate and consume energy resources act as energy prosumers. Peer-to-peer (P2P) trading provides an efficient means of utilizing renewable energy among these energy prosumers, who possess both power generation and consumption capabilities. However, within the current market mechanism, each prosumer retains private information that is not disclosed on the network. To address the issue of incomplete information among multiple prosumers during the decision-making process, we develop a Bayesian game model based on the CNN-LSTM-ATT prediction method for P2P electricity transactions among multiple prosumers. The energy prosumers in each industrial park aim to minimize their energy consumption costs by adjusting strategies that include P2P energy trading and managing thermal loads. Prosumers make decisions on the basis of their own characteristics and estimates of other prosumer characteristics, which are obtained from the joint probability distribution predicted by the CNN-LSTM-ATT method. These decisions are aimed at minimizing each prosumer's electricity costs. The simulation results demonstrate the effectiveness of the Bayesian game model proposed in this study.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100437"},"PeriodicalIF":9.6,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659391","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

Parameter sensitivity analysis for diesel spray penetration prediction based on GA-BP neural network 基于 GA-BP 神经网络的柴油机喷雾渗透率预测参数敏感性分析

IF 9.6

Energy and AI Pub Date : 2024-11-05 DOI: 10.1016/j.egyai.2024.100443

Yifei Zhang , Gengxin Zhang , Dawei Wu , Qian Wang , Ebrahim Nadimi , Penghua Shi , Hongming Xu

{"title":"Parameter sensitivity analysis for diesel spray penetration prediction based on GA-BP neural network","authors":"Yifei Zhang , Gengxin Zhang , Dawei Wu , Qian Wang , Ebrahim Nadimi , Penghua Shi , Hongming Xu","doi":"10.1016/j.egyai.2024.100443","DOIUrl":"10.1016/j.egyai.2024.100443","url":null,"abstract":"<div><div>Machine learning has started to be used in engine research to optimize combustion and predict fuel spray characteristics. This paper presents the development of a machine learning model using a Genetic Algorithm-Backpropagation (GA-BP) neural network to predict spray penetration. The GA-BP neural network was selected for its ability to optimize neural network weights and thresholds, thereby improving model convergence and avoiding local minima, which are common challenges in complex, non-linear problems such as spray prediction. The model was trained using experimental data from diesel injector spray tests, and its accuracy was evaluated through parametric sensitivity analysis, examining the influence of various input factors. A comparison between the machine learning model and the traditional empirical formulas of spray penetration revealed that the machine learning model achieved greater accuracy. In terms of the sensitivity to inputs, it is interesting to find that the cognition of machines is different from that of humans. When an input parameter does not have any functional relationship with other input parameters, the absence of this input parameter will lead to a significant decrease in the accuracy of the output result. The results demonstrate that the machine learning approach offers higher accuracy and better generalizability compared to traditional empirical methods. This study recommends the ways to get better results of penetration prediction with BP neural networks, which is efficient in training and utilizing Artificial Neural Networks (ANNs).</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100443"},"PeriodicalIF":9.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659394","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

Optimizing catalyst layer composition of PEM fuel cell via machine learning: Insights from in-house experimental data 通过机器学习优化 PEM 燃料电池催化剂层的组成：内部实验数据的启示

IF 9.6

Energy and AI Pub Date : 2024-11-01 DOI: 10.1016/j.egyai.2024.100439

Yuze Hou, Patrick Schneider, Linda Ney, Nada Zamel

{"title":"Optimizing catalyst layer composition of PEM fuel cell via machine learning: Insights from in-house experimental data","authors":"Yuze Hou, Patrick Schneider, Linda Ney, Nada Zamel","doi":"10.1016/j.egyai.2024.100439","DOIUrl":"10.1016/j.egyai.2024.100439","url":null,"abstract":"<div><div>The catalyst layer (CL) is a pivotal component of Proton Exchange Membrane (PEM) fuel cells, exerting a significant impact on both performance and durability. Its ink composition can be succinctly characterized by platinum (Pt) loading, Pt/carbon ratio, and ionomer/carbon ratio. The amount of each substance within the CL must be meticulously balanced to achieve optimal operation. In this work, we apply an Artificial Neural Network (ANN) model to forecast the performance and durability of a PEM fuel cell based on its cathode CL composition. The model is trained and validated based on experimental data measured at our laboratories, which consist of data from 49 fuel cells, detailing their cathode CL composition, operating conditions, accelerated stress test conditions, polarization curves and ECSA measurements throughout their lifespan. The presented ANN model demonstrates exceptional reliability in predicting PEM fuel cell behavior for both beginning and end of life. This allows for a deeper understanding of the influence of each input on performance and durability. Furthermore, the model can be effectively applied to optimize the CL composition. This paper demonstrates the immense potential of AI, combined with a high-quality database, to advance fuel cell research.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100439"},"PeriodicalIF":9.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142592934","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 based parameter estimation for an adapted finite element model of a blade bearing test bench 基于机器学习的叶片轴承试验台改良有限元模型参数估计

IF 9.6

Energy and AI Pub Date : 2024-10-29 DOI: 10.1016/j.egyai.2024.100436

Luca Faller , Matthis Graßmann , Timo Lichtenstein

{"title":"Machine learning based parameter estimation for an adapted finite element model of a blade bearing test bench","authors":"Luca Faller , Matthis Graßmann , Timo Lichtenstein","doi":"10.1016/j.egyai.2024.100436","DOIUrl":"10.1016/j.egyai.2024.100436","url":null,"abstract":"<div><div>Improving the reliability of blade bearings is essential for the safe operation of wind turbines. This challenge can be met with the help of virtual testing and digital-twin driven condition monitoring. For such approaches, a precise digital representation of the blade bearing and its test bench is an essential prerequisite. However, various factors prevent the capture of all parameters of the blade bearing and the associated test bench. Parameters such as bearing preload, rolling element and raceway dimensions, and bolt preload during assembly vary with each bearing and test bench setup. As these parameters cannot be measured directly, an alternative solution is required. This article presents a methodology to efficiently estimate non-measurable parameters of the test bench using a combination of model-based and data-driven approaches, improving the detailed and accurate virtual testing of blade bearings. It must be ensured to enable the fastest possible, most computationally efficient estimation of parameters during virtual testing or condition monitoring. The developed methodology is evaluated using the example of bolt preload on the test bench. By employing a random forest model and the strain gauge measurements attached to the blade bearing, the bolt preload parameters are estimated. The results demonstrate that the accuracy of the digital model of the blade bearing test bench is improved by up to 11 % in three out of four test bench setups. The great improvement in the accuracy of the digital model highlights the effectiveness of the proposed methodology in enhancing virtual blade bearing testing and digital-twin driven condition monitoring.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100436"},"PeriodicalIF":9.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659397","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 novel optimization framework for natural gas transportation pipeline networks based on deep reinforcement learning 基于深度强化学习的天然气运输管道网络新型优化框架

IF 9.6

Energy and AI Pub Date : 2024-10-29 DOI: 10.1016/j.egyai.2024.100434

Zemin Eitan Liu , Wennan Long , Zhenlin Chen , James Littlefield , Liang Jing , Bo Ren , Hassan M. El-Houjeiri , Amjaad S. Qahtani , Muhammad Y. Jabbar , Mohammad S. Masnadi

{"title":"A novel optimization framework for natural gas transportation pipeline networks based on deep reinforcement learning","authors":"Zemin Eitan Liu , Wennan Long , Zhenlin Chen , James Littlefield , Liang Jing , Bo Ren , Hassan M. El-Houjeiri , Amjaad S. Qahtani , Muhammad Y. Jabbar , Mohammad S. Masnadi","doi":"10.1016/j.egyai.2024.100434","DOIUrl":"10.1016/j.egyai.2024.100434","url":null,"abstract":"<div><div>Natural gas is an emerging and reliable energy source in transition to a low-carbon economy. The natural gas transportation pipeline network systems are crucial when transporting natural gas from the production endpoints to processing or consuming endpoints. Optimizing the operational efficiency of compressor stations within pipeline networks is an effective way to reduce energy consumption and carbon emissions during transportation. This paper proposes an optimization framework for natural gas transportation pipeline networks based on deep reinforcement learning (DRL). The mathematical simulation model is derived from mass balance, hydrodynamics principles of gas flow, and compressor characteristics. The optimization control problem in steady state is formulated into a one-step Markov decision process (MDP) and solved by DRL. The decision variables are selected as the discharge ratio of each compressor. By the comprehensive comparison with dynamic programming (DP) and genetic algorithm (GA) in three typical element topologies (a linear topology with gun-barrel structure, a linear topology with branch structure, and a tree topology), the proposed method can obtain 4.60% lower power consumption than GA, and the time consumption is reduced by 97.5% compared with DP. The proposed framework could be further utilized for future large-scale network optimization practices.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100434"},"PeriodicalIF":9.6,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573484","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 two-layer low-carbon economic planning method for park-level integrated energy systems with carbon-energy synergistic hub 具有碳-能协同枢纽的园区级综合能源系统的双层低碳经济规划方法

IF 9.6

Energy and AI Pub Date : 2024-10-28 DOI: 10.1016/j.egyai.2024.100435

Yunfei Mu , Haochen Guo , Zhijun Wu , Hongjie Jia , Xiaolong Jin , Yan Qi

{"title":"A two-layer low-carbon economic planning method for park-level integrated energy systems with carbon-energy synergistic hub","authors":"Yunfei Mu , Haochen Guo , Zhijun Wu , Hongjie Jia , Xiaolong Jin , Yan Qi","doi":"10.1016/j.egyai.2024.100435","DOIUrl":"10.1016/j.egyai.2024.100435","url":null,"abstract":"<div><div>Building a low-carbon park is crucial for achieving the carbon neutrality goals. However, most research on low-carbon economic planning methods for park-level integrated energy systems (PIES) has focused on multi-energy flow interactions, neglecting the “carbon perspective” and the impact of the dynamic coupling characteristics between multi-energy flows and carbon emission flow (CEF) on carbon reduction and planning schemes. Therefore, this paper proposes a two-layer low-carbon economic planning method for park-level integrated energy systems with carbon-energy synergistic hub (CESH). Firstly, this paper establishes a CESH model for PIES to describe the synergistic relationship between CEF and multi-energy flows from input, conversion, storage, to output. Secondly, a PIES two-layer low-carbon economic planning model with CESH is proposed. The upper model determines the optimal device types and capacities during the planning cycle. The lower model employs the CESH model to promote carbon energy friendly interactions, optimize the daily operation scheme of PIES. The iterative process between the two layers, initiated by a genetic algorithm (GA), ensures the speed and accuracy. Finally, case studies show that, compared to planning methods without the CESH model, the proposed method is effective in reducing carbon emissions and total costs during the planning cycle. From a dual “carbon-energy” perspective, it enhances investment effectiveness and carbon reduction sensitivity by deeply exploring the energy conservation and carbon reduction potential of PIES.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100435"},"PeriodicalIF":9.6,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142659393","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

Exploring public attention in the circular economy through topic modelling with twin hyperparameter optimisation 通过双参数优化的主题建模探索循环经济中的公众关注度

IF 9.6

Energy and AI Pub Date : 2024-10-22 DOI: 10.1016/j.egyai.2024.100433

Junhao Song , Yingfang Yuan , Kaiwen Chang , Bing Xu , Jin Xuan , Wei Pang

{"title":"Exploring public attention in the circular economy through topic modelling with twin hyperparameter optimisation","authors":"Junhao Song , Yingfang Yuan , Kaiwen Chang , Bing Xu , Jin Xuan , Wei Pang","doi":"10.1016/j.egyai.2024.100433","DOIUrl":"10.1016/j.egyai.2024.100433","url":null,"abstract":"<div><div>To advance the circular economy (CE), it is crucial to gain insights into the evolution of public attention, cognitive pathways related to circular products, and key public concerns. To achieve these objectives, we collected data from diverse platforms, including Twitter, Reddit, and The Guardian, and utilised three topic models to analyse the data. Given the performance of topic modelling may vary depending on hyperparameter settings, we proposed a novel framework that integrates twin (single- and multi-objective) hyperparameter optimisation for CE analysis. Systematic experiments were conducted to determine appropriate hyperparameters under different constraints, providing valuable insights into the correlations between CE and public attention. Our findings reveal that economic implications of sustainability and circular practices, particularly around recyclable materials and environmentally sustainable technologies, remain a significant public concern. Topics related to sustainable development and environmental protection technologies are particularly prominent on The Guardian, while Twitter discussions are comparatively sparse. These insights highlight the importance of targeted education programmes, business incentives adopt CE practices, and stringent waste management policies alongside improved recycling processes.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100433"},"PeriodicalIF":9.6,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573483","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