IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v212y2025ics0040162524007649.html

Identification of technology innovation path based on multi-feature vector fusion: The case of flywheel energy storage technology

Author

Listed:
  • Zhang, Ziye
  • Feng, Lijie
  • Wang, Jinfeng
  • Zhao, Weiyu
  • Yan, Jingbo

Abstract

Flywheel energy storage (FES) technology, as one of the most promising energy storage technologies, has rapidly developed. It is essential to analyze the evolution path of advanced technology in this field and to predict its development trend and direction. However, some limitations remain in the existing research, which only uses a single feature to analyze technological innovation, fails to consider the development characteristics of technological innovation, and disregards the whole process analysis of the development trend of FES technology and the prediction of future development trends. Therefore, this study proposes a framework for technology evolution path identification and analysis that uses multisource data and incorporates citation and text features to monitor the evolution trend of FES technology and predict the future development direction of this technology. First, text and citation feature vectors from multisource data are extracted using shallow neural network embedding technology and then fused and spliced to obtain high-dimensional vectors that represent documents. Second, the time series of academic papers and patents filed in the last two decades are divided by the change point detection algorithm. Third, the Latent Dirichlet Allocation (LDA) model is applied to identify the topics of academic papers and patent data in different periods, and the cosine similarity calculation method is employed to construct the technical evolution path based on academic papers and patent data. Last, the gap between science and technology is analyzed, and the future development direction of FES technology is clarified.

Suggested Citation

  • Zhang, Ziye & Feng, Lijie & Wang, Jinfeng & Zhao, Weiyu & Yan, Jingbo, 2025. "Identification of technology innovation path based on multi-feature vector fusion: The case of flywheel energy storage technology," Technological Forecasting and Social Change, Elsevier, vol. 212(C).
    • Handle: RePEc:eee:tefoso:v:212:y:2025:i:c:s0040162524007649
    DOI: 10.1016/j.techfore.2024.123966
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162524007649
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2024.123966?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Huailan Liu & Zhiwang Chen & Jie Tang & Yuan Zhou & Sheng Liu, 2020. "Mapping the technology evolution path: a novel model for dynamic topic detection and tracking," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2043-2090, December.
    2. Grimmer, Justin, 2010. "A Bayesian Hierarchical Topic Model for Political Texts: Measuring Expressed Agendas in Senate Press Releases," Political Analysis, Cambridge University Press, vol. 18(1), pages 1-35, January.
    3. Coccia, Mario, 2022. "Probability of discoveries between research fields to explain scientific and technological change," Technology in Society, Elsevier, vol. 68(C).
    4. Siraj Sabihuddin & Aristides E. Kiprakis & Markus Mueller, 2014. "A Numerical and Graphical Review of Energy Storage Technologies," Energies, MDPI, vol. 8(1), pages 1-45, December.
    5. Bruno Van Pottelsberghe & Dominique Guellec, 2008. "Patents and academic research: a state of the art," ULB Institutional Repository 2013/6187, ULB -- Universite Libre de Bruxelles.
    6. Rehman, Shafiqur & Al-Hadhrami, Luai M. & Alam, Md. Mahbub, 2015. "Pumped hydro energy storage system: A technological review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 586-598.
    7. Khishigsuren Davagdorj & Ling Wang & Meijing Li & Van-Huy Pham & Keun Ho Ryu & Nipon Theera-Umpon, 2022. "Discovering Thematically Coherent Biomedical Documents Using Contextualized Bidirectional Encoder Representations from Transformers-Based Clustering," IJERPH, MDPI, vol. 19(10), pages 1-21, May.
    8. Kai Xu & Youguang Guo & Gang Lei & Jianguo Zhu, 2023. "A Review of Flywheel Energy Storage System Technologies," Energies, MDPI, vol. 16(18), pages 1-32, September.
    9. Yuan Zhou & Fang Dong & Yufei Liu & Zhaofu Li & JunFei Du & Li Zhang, 2020. "Forecasting emerging technologies using data augmentation and deep learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(1), pages 1-29, April.
    10. Sebastián, R. & Peña Alzola, R., 2012. "Flywheel energy storage systems: Review and simulation for an isolated wind power system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(9), pages 6803-6813.
    11. Gustaf Bellstam & Sanjai Bhagat & J. Anthony Cookson, 2021. "A Text-Based Analysis of Corporate Innovation," Management Science, INFORMS, vol. 67(7), pages 4004-4031, July.
    12. Xin Xu & Cheng-Cai Yang & Yang Xiao & Jian-Lei Kong, 2023. "A Fine-Grained Recognition Neural Network with High-Order Feature Maps via Graph-Based Embedding for Natural Bird Diversity Conservation," IJERPH, MDPI, vol. 20(6), pages 1-20, March.
    13. Morgan, Robert & Nelmes, Stuart & Gibson, Emma & Brett, Gareth, 2015. "Liquid air energy storage – Analysis and first results from a pilot scale demonstration plant," Applied Energy, Elsevier, vol. 137(C), pages 845-853.
    14. Momeni, Abdolreza & Rost, Katja, 2016. "Identification and monitoring of possible disruptive technologies by patent-development paths and topic modeling," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 16-29.
    15. Ghaffari, Mohsen & Aliahmadi, Alireza & Khalkhali, Abolfazl & Zakery, Amir & Daim, Tugrul U. & Yalcin, Haydar, 2023. "Topic-based technology mapping using patent data analysis: A case study of vehicle tires," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    16. Chen, Hongshu & Zhang, Guangquan & Zhu, Donghua & Lu, Jie, 2017. "Topic-based technological forecasting based on patent data: A case study of Australian patents from 2000 to 2014," Technological Forecasting and Social Change, Elsevier, vol. 119(C), pages 39-52.
    17. Silvestrini, Paolo & Amato, Umberto & Vettoliere, Antonio & Silvestrini, Stefano & Ruggiero, Berardo, 2017. "Rate equation leading to hype-type evolution curves: A mathematical approach in view of analysing technology development," Technological Forecasting and Social Change, Elsevier, vol. 116(C), pages 1-12.
    18. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    19. Nourelhouda Yahi & Hacene Belhadef & Mathieu Roche, 2021. "Investigating the impact of preprocessing on document embedding: an empirical comparison," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 13(4), pages 351-363.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Chen, Fan & Wang, Delu & Li, Chunxiao & Mao, Jinqi & Wang, Yadong & Yu, Lan, 2025. "Evolution analysis and trend forecasting for low-carbon technologies in coal power based on a three-layer text mining framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 217(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fan, Xiaoyu & Xu, Hao & Li, Yihong & Li, Junxian & Wang, Zhikang & Gao, Zhaozhao & Ji, Wei & Chen, Liubiao & Wang, Junjie, 2024. "A novel liquid air energy storage system with efficient thermal storage: Comprehensive evaluation of optimal configuration," Applied Energy, Elsevier, vol. 371(C).
    2. Wang, Xiaoguang & He, Jing & Huang, Han & Wang, Hongyu, 2022. "MatrixSim: A new method for detecting the evolution paths of research topics," Journal of Informetrics, Elsevier, vol. 16(4).
    3. Li, Xin & Xie, Qianqian & Jiang, Jiaojiao & Zhou, Yuan & Huang, Lucheng, 2019. "Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 687-705.
    4. Mario Coccia & Saeed Roshani, 2025. "Path-Breaking Directions in Quantum Computing Technology: A Patent Analysis with Multiple Techniques," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 4991-5024, March.
    5. Farhan H. Malik & Ghulam A. Hussain & Yahia M. S. Alsmadi & Zunaib M. Haider & Wathiq Mansoor & Matti Lehtonen, 2025. "Integrating Energy Storage Technologies with Renewable Energy Sources: A Pathway Toward Sustainable Power Grids," Sustainability, MDPI, vol. 17(9), pages 1-32, May.
    6. Mousavi G, S.M. & Faraji, Faramarz & Majazi, Abbas & Al-Haddad, Kamal, 2017. "A comprehensive review of Flywheel Energy Storage System technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 477-490.
    7. She, Xiaohui & Wang, Huiru & Zhang, Tongtong & Li, Yongliang & Zhao, Xuemin & Ding, Yulong & Wang, Chen, 2025. "Liquid air energy storage – A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 208(C).
    8. Fan, Xiaoyu & Guo, Luna & Ji, Wei & Chen, Liubiao & Wang, Junjie, 2023. "Liquid air energy storage system based on fluidized bed heat transfer," Renewable Energy, Elsevier, vol. 215(C).
    9. Qiang Gao & Man Jiang, 2024. "Exploring technology fusion by combining latent Dirichlet allocation with Doc2vec: a case of digital medicine and machine learning," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4043-4070, July.
    10. Gozuacik, Necip & Sakar, C. Okan & Ozcan, Sercan, 2023. "Technological forecasting based on estimation of word embedding matrix using LSTM networks," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    11. Maas, Pascal & Schiemann, Martin & Scherer, Viktor & Fischer, Peter & Taroata, Dan & Schmid, Günther, 2018. "Lithium as energy carrier: CFD simulations of LI combustion in a 100MW slag tap furnace," Applied Energy, Elsevier, vol. 227(C), pages 506-515.
    12. Ding, Xingqi & Duan, Liqiang & Zheng, Nan & Desideri, Umberto & Zhou, Yufei & Wang, Qiushi & Wang, Yuanhui & Jiao, Weijia, 2025. "A systematic review on liquid air energy storage system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).
    13. Cárdenas, Bruno & Swinfen-Styles, Lawrie & Rouse, James & Hoskin, Adam & Xu, Weiqing & Garvey, S.D., 2021. "Energy storage capacity vs. renewable penetration: A study for the UK," Renewable Energy, Elsevier, vol. 171(C), pages 849-867.
    14. O'Callaghan, O. & Donnellan, P., 2021. "Liquid air energy storage systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    15. Xie, Chunping & Hong, Yan & Ding, Yulong & Li, Yongliang & Radcliffe, Jonathan, 2018. "An economic feasibility assessment of decoupled energy storage in the UK: With liquid air energy storage as a case study," Applied Energy, Elsevier, vol. 225(C), pages 244-257.
    16. Kim, Juwon & Noh, Yeelyong & Chang, Daejun, 2018. "Storage system for distributed-energy generation using liquid air combined with liquefied natural gas," Applied Energy, Elsevier, vol. 212(C), pages 1417-1432.
    17. Yin, Libo & Xin, Yu, 2024. "China's cognitive bias premium: An exploration of innovation information," International Review of Economics & Finance, Elsevier, vol. 96(PA).
    18. Makhdoomi, Sina & Askarzadeh, Alireza, 2020. "Daily performance optimization of a grid-connected hybrid system composed of photovoltaic and pumped hydro storage (PV/PHS)," Renewable Energy, Elsevier, vol. 159(C), pages 272-285.
    19. Qi, Meng & Park, Jinwoo & Lee, Inkyu & Moon, Il, 2022. "Liquid air as an emerging energy vector towards carbon neutrality: A multi-scale systems perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    20. Zhichao Ba & Kai Meng & Leilei Liu & Yujie Zhang, 2025. "How science-technology interactions affect technological innovation: the moderating role of topic divergence," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-20, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:tefoso:v:212:y:2025:i:c:s0040162524007649. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.