Energy and AI最新文献

{"title":"A novel optimization framework for natural gas transportation pipeline networks based on deep reinforcement learning","authors":"Zemin Eitan Liu ,&nbsp;Wennan Long ,&nbsp;Zhenlin Chen ,&nbsp;James Littlefield ,&nbsp;Liang Jing ,&nbsp;Bo Ren ,&nbsp;Hassan M. El-Houjeiri ,&nbsp;Amjaad S. Qahtani ,&nbsp;Muhammad Y. Jabbar ,&nbsp;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}

{"title":"A two-layer low-carbon economic planning method for park-level integrated energy systems with carbon-energy synergistic hub","authors":"Yunfei Mu ,&nbsp;Haochen Guo ,&nbsp;Zhijun Wu ,&nbsp;Hongjie Jia ,&nbsp;Xiaolong Jin ,&nbsp;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}

{"title":"Exploring public attention in the circular economy through topic modelling with twin hyperparameter optimisation","authors":"Junhao Song ,&nbsp;Yingfang Yuan ,&nbsp;Kaiwen Chang ,&nbsp;Bing Xu ,&nbsp;Jin Xuan ,&nbsp;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}

{"title":"Adaptive control systems for dual axis tracker using clear sky index and output power forecasting based on ML in overcast weather conditions","authors":"Nursultan Koshkarbay ,&nbsp;Saad Mekhilef ,&nbsp;Ahmet Saymbetov ,&nbsp;Nurzhigit Kuttybay ,&nbsp;Madiyar Nurgaliyev ,&nbsp;Gulbakhar Dosymbetova ,&nbsp;Sayat Orynbassar ,&nbsp;Evan Yershov ,&nbsp;Ainur Kapparova ,&nbsp;Batyrbek Zholamanov ,&nbsp;Askhat Bolatbek","doi":"10.1016/j.egyai.2024.100432","DOIUrl":"10.1016/j.egyai.2024.100432","url":null,"abstract":"<div><div>The use of artificial intelligence in renewable energy systems increases energy generation and improves energy system management. The control system of many solar trackers is designed for maximum radiation power conditions and shows decent performance indicators, but during rapidly changing weather conditions or cloudy days, the performance of the solar trackers is reduced due to moving parts and low irradiance. Some studies show that the horizontal configuration produces more energy with scattered solar radiation than solar tracking systems. This work shows the possibility of using solar tracking systems under different weather conditions and cloudy days. To achieve the goals, a new adaptive control system for dual-axis solar trackers with astronomical tracking was developed, which differs from traditional controls in the use of horizontal configurations under certain weather conditions. The assessment of spatio-temporal weather conditions was carried out using the Clear Sky Index (CSI) and was complemented by forecasting the panel's power output. The study found that at 0.4 CSI values, the horizontal configuration exhibits higher power output than solar tracking systems, providing the potential to use the threshold for adaptive control. The developed system is more efficient by 18.3 %, 14.9 %, and 10.01 % than the horizontal configuration, single-axis, and dual-axis solar trackers.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100432"},"PeriodicalIF":9.6,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142534495","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}

{"title":"Supporting energy policy research with large language models: A case study in wind energy siting ordinances","authors":"Grant Buster,&nbsp;Pavlo Pinchuk,&nbsp;Jacob Barrons,&nbsp;Ryan McKeever,&nbsp;Aaron Levine,&nbsp;Anthony Lopez","doi":"10.1016/j.egyai.2024.100431","DOIUrl":"10.1016/j.egyai.2024.100431","url":null,"abstract":"<div><div>The recent growth in renewable energy development in the United States has been accompanied by a simultaneous surge in renewable energy siting ordinances. These zoning laws play a critical role in dictating the placement of wind and solar resources that are critical for achieving low-carbon energy futures. In this context, efficient access to and management of siting ordinance data becomes imperative. The National Renewable Energy Laboratory (NREL) recently introduced a public wind and solar siting database to fill this need. This paper presents a method for harnessing Large Language Models (LLMs) to automate the extraction of these siting ordinances from legal documents, enabling this database to maintain accurate up-to-date information in the rapidly changing energy policy landscape. A novel contribution of this research is the integration of a decision tree framework with LLMs. Our results show that this approach is 85 to 90 % accurate with outputs that can be used directly in downstream quantitative modeling. We discuss opportunities to use this work to support similar large-scale policy research in the energy sector. By unlocking new efficiencies in the extraction and analysis of legal documents using LLMs, this study enables a path forward for automated large-scale energy policy research.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100431"},"PeriodicalIF":9.6,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142434515","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}

{"title":"Optimization of modeling and temperature control of air-cooled PEMFC based on TLBO-DE","authors":"Pu He ,&nbsp;Jun-Hong Chen ,&nbsp;Chen-Zi Zhang ,&nbsp;Zi-Yan Yu ,&nbsp;Ming-Yang Wang ,&nbsp;Jun-Yu Chen ,&nbsp;Jia-Le Song ,&nbsp;Yu-Tong Mu ,&nbsp;Kun-Ying Gong ,&nbsp;Wen-Quan Tao","doi":"10.1016/j.egyai.2024.100430","DOIUrl":"10.1016/j.egyai.2024.100430","url":null,"abstract":"<div><div>The temperature control of the air-cooled proton exchange membrane fuel cell (PEMFC) is important for effective and safe operation. To develop a practical and precise controller, this study combines the Radial Basis Function (RBF) neural network with Back Propagation neural network adaptive Proportion Integration Differentiation (BP-PID), and then a metaheuristic algorithm is used to optimize the parameters of RBF-BP-PID for further improvement in temperature control. First, an air-cooled PEMFC system model is established. To match the simulation data with the experimental data, Teaching Learning Based Optimization–Differential Evolution (TLBO-DE) is proposed to identify the unknown parameters, and the maximum relative error is &lt;3.5 %. Second, RBF neural network is introduced to identify the stack temperature and provide the accurate <span><math><mfrac><mrow><mi>∂</mi><mi>y</mi><mo>(</mo><mi>k</mi><mo>)</mo></mrow><mrow><mi>∂</mi><mi>u</mi><mo>(</mo><mi>k</mi><mo>)</mo></mrow></mfrac></math></span> for BP-PID, which solves the problem of using sign function sgn(<span><math><mfrac><mrow><mi>∂</mi><mi>y</mi><mo>(</mo><mi>k</mi><mo>)</mo></mrow><mrow><mi>∂</mi><mi>u</mi><mo>(</mo><mi>k</mi><mo>)</mo></mrow></mfrac></math></span>) to approximate the <span><math><mfrac><mrow><mi>∂</mi><mi>y</mi><mo>(</mo><mi>k</mi><mo>)</mo></mrow><mrow><mi>∂</mi><mi>u</mi><mo>(</mo><mi>k</mi><mo>)</mo></mrow></mfrac></math></span> in BP-PID. Regarding the temperature control of air-cooled PEMFC, several controllers are compared, including PID, Fuzzy-PID, BP-PID and RBF-BP-PID. The proposed RBF-BP-PID achieves the best control effect, which reduces the integrated time and absolute error (ITAE) by 3.4 % and 15.8 % based on BP-PID in the startup and steady phases, respectively. Since the <span><math><mfrac><mrow><mi>∂</mi><mi>y</mi><mo>(</mo><mi>k</mi><mo>)</mo></mrow><mrow><mi>∂</mi><mi>u</mi><mo>(</mo><mi>k</mi><mo>)</mo></mrow></mfrac></math></span> provided by RBF changes softly and continuously during the control process, the parameters self-tuning ability of RBF-BP-PID is better than BP-PID. Third, to improve the control effect of RBF-BP-PID further, TLBO-DE is adopted to optimize the parameters of RBF neural network and BP neural network.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100430"},"PeriodicalIF":9.6,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418642","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}

{"title":"Distributed decision making for unmanned aerial vehicle inspection with limited energy constraint","authors":"Qi Wang ,&nbsp;Haomin Zhu ,&nbsp;Gang Pan ,&nbsp;Jianguo Wei ,&nbsp;Chen Zhang ,&nbsp;Zhu Huang ,&nbsp;Guowei Ling","doi":"10.1016/j.egyai.2024.100429","DOIUrl":"10.1016/j.egyai.2024.100429","url":null,"abstract":"<div><div>The unsatisfactory energy density of the state-of-art batteries imposes constraints on the practical application of unmanned aerial vehicles (UAVs). Establishing a UAV airport network that integrates energy supply and information exchange functionalities represents an ideal solution for enabling synergistic UAV operations. However, devising efficient distribution protocols for these airports remains a challenge. By leveraging modeling and analysis of the energy density of existing UAV batteries, we can forecast the flight range and distances achievable by UAVs. Here, we propose a distribution protocol for UAV airport platforms aimed at enhancing distribution accuracy by the use of AI principles. Furthermore, considering the possibility of emergency UAV stop, we introduce an emergency stop system in conjunction with standard stopping procedures to optimize distribution efficiency and enhance UAV inspection safety. Moreover, existing UAV airports usually provide energy to UAVs without harnessing UAVs to facilitate interconnection and interoperability among different airports. This inefficiency leads to significant resource wastage in energy distribution. To address this, we introduce a shared energy network that allows different companies to operate according to energy distribution needs. This network not only supplies energy to UAVs but also employs UAVs for energy collection and transportation, facilitating energy trading, business collaboration, and data transmission among diverse organizations. By enabling ubiquitous energy trading, this study provides us an ideal strategy for the future construction of energy network with interconnection and interoperability, which can be extended to other applications calling for energy distribution.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100429"},"PeriodicalIF":9.6,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142418643","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}

{"title":"VA-Creator—A Virtual Appliance Creator based on adaptive Neural Networks to generate synthetic power consumption patterns","authors":"Michael Meiser ,&nbsp;Benjamin Duppe ,&nbsp;Ingo Zinnikus ,&nbsp;Alexander Anisimov","doi":"10.1016/j.egyai.2024.100427","DOIUrl":"10.1016/j.egyai.2024.100427","url":null,"abstract":"<div><p>With the advent of the Smart Home domain and the increasingly widespread application of Machine Learning (ML), obtaining power consumption data is becoming more and more important. Collecting real-world energy data using sensors is time consuming, expensive, error-prone and in some situations not possible. Therefore, we present the VA-Creator, a framework to create Virtual Appliances (VAs). These VAs synthesize power consumption patterns (PCPs) based on Neural Networks (NNs) which adapt their architecture to the training data structure to simplify the creation of new VAs. To be able to generate all appliance types available in a typical household we use various kinds of NN, including Multilayer Perceptrons (MLPs), Long Short-Term Memorys (LSTMs) and a specific Generative Adversarial Network (GAN) as well as different ML techniques such as XGBoost, selecting the appropriate technique depending on each appliance’s characteristics. We then compare the results of the ML models against real data and evaluate them by using Dynamic time Warping (DTW) as well as the classification performance of an MLP discriminator as metrics. Additionally, to ensure that the VAs allow to meaningfully train ML models, we use them to generate synthetic data and then train Non intrusive Load Monitoring (NILM) models in an extensive evaluation. The presented evaluation provides evidence that the VA models produce realistic and meaningful results.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100427"},"PeriodicalIF":9.6,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000934/pdfft?md5=7a1899b5d91ed06095525435800ee68a&pid=1-s2.0-S2666546824000934-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142273969","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}

{"title":"Deep learning from three-dimensional lithium-ion battery multiphysics model part I: Data development","authors":"Yiheng Pang ,&nbsp;Yun Wang ,&nbsp;Zhiqiang Niu","doi":"10.1016/j.egyai.2024.100428","DOIUrl":"10.1016/j.egyai.2024.100428","url":null,"abstract":"<div><div>Fast growing demands for electric vehicles require better longevity, safety and reliability for next-generation high-energy battery technologies. A data-centered battery management system is thus desired to interpret complex battery data and make decisions for properly managing multi-physics battery dynamics. Nowadays, Battery informatics are emerging as promising solutions by leveraging advanced machine learning tools to deliver accurate prediction of battery performance, health and safety, but is hurdled by a scarcity of data. To mitigate this issue, this study presents one of the first studies for data development through both experimental studies and three-dimensional (3-D) multi-physics modeling to underpin a deep learning framework with in-depth examination for battery performance and thermal risk prediction. Specifically, Part I focused on the development of the battery model which was thoroughly validated and analyzed to guarantee the model accuracy by two steps: firstly, we validated the multi-physics model against two commercial Lithium-ion batteries, i.e., Panasonic NCR18650B and 18650BD; Then, the coupling between thermal and electrochemical battery behaviors were analyzed deeply to demonstrate insights obtained from the model, such as voltage evolution and maximum local temperature (hot spot). The developed model proves to be capable of providing insightful and reliable data for the training of convolutional neural network and long short-term memory (CNN-LSTM) in part II.</div></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100428"},"PeriodicalIF":9.6,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000946/pdfft?md5=2dba62c12bcdcee726bf78d19f8b94e2&pid=1-s2.0-S2666546824000946-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142310540","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}

{"title":"Application of artificial intelligence in the materials science, with a special focus on fuel cells and electrolyzers","authors":"Mariah Batool ,&nbsp;Oluwafemi Sanumi ,&nbsp;Jasna Jankovic","doi":"10.1016/j.egyai.2024.100424","DOIUrl":"10.1016/j.egyai.2024.100424","url":null,"abstract":"<div><p>Artificial Intelligence (AI) has revolutionized technological development globally, delivering relatively more accurate and reliable solutions to critical challenges across various research domains. This impact is particularly notable within the field of materials science and engineering, where artificial intelligence has catalyzed the discovery of new materials, enhanced design simulations, influenced process controls, and facilitated operational analysis and predictions of material properties and behaviors. Consequently, these advancements have streamlined the synthesis, simulation, and processing procedures, leading to material optimization for diverse applications. A key area of interest within materials science is the development of hydrogen-based electrochemical systems, such as fuel cells and electrolyzers, as clean energy solutions, known for their promising high energy density and zero-emission operations. While artificial intelligence shows great potential in studying both fuel cells and electrolyzers, existing literature often separates them, with a clear gap in comprehensive studies on electrolyzers despite their similarities. This review aims to bridge that gap by providing an integrated overview of artificial intelligence's role in both technologies. This review begins by explaining the fundamental concepts of artificial intelligence and introducing commonly used artificial intelligence-based algorithms in a simplified and clearly comprehensible way, establishing a foundational knowledge base for further discussion. Subsequently, it explores the role of artificial intelligence in materials science, highlighting the critical applications and drawing on examples from recent literature to build on the discussion. The paper then examines how artificial intelligence has propelled significant advancements in studying various types of fuel cells and electrolyzers, specifically emphasizing proton exchange membrane (PEM) based systems. It thoroughly explores the artificial intelligence tools and techniques for characterizing, manufacturing, testing, analyzing, and optimizing these systems. Additionally, the review critically evaluates the current research landscape, pinpointing progress and prevailing challenges. Through this thorough analysis, the review underscores the fundamental role of artificial intelligence in advancing the generation and utilization of clean energy, illustrating its transformative potential in this area of research.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"18 ","pages":"Article 100424"},"PeriodicalIF":9.6,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000909/pdfft?md5=b83f9a182a85a5f48c45be65e082c851&pid=1-s2.0-S2666546824000909-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142242848","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}