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

Author

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  • 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
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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. Nicolas van Zeebroeck & Bruno Van Pottelsberghe & Dominique Guellec, 2008. "Patents and Academic Research: A State of the Art," Working Papers CEB 08-013.RS, ULB -- Universite Libre de Bruxelles.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Coccia, Mario, 2022. "Probability of discoveries between research fields to explain scientific and technological change," Technology in Society, Elsevier, vol. 68(C).
    12. 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.
    13. 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.
    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. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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).
    Full references (including those not matched with items on IDEAS)

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