Tracking and predicting technological knowledge interactions between artificial intelligence and wind power: Multimethod patent analysis
参考中译:跟踪和预测人工智能与风电之间的技术知识互动：多方法专利分析


          

刊名：Advanced engineering informatics
作者：Jinfeng Wang（China Institute of FTZ Supply Chain, Shanghai Maritime University）
Lu Cheng（School of Economics & Management, Shanghai Maritime University）
Lijie Feng（Logistics Engineering College, Shanghai Maritime University）
Kuo-Yi Lin（School of Business, Guilin University of Electronic Technology）
Luyao Zhang（Henan University of Economics and Law）
Weiyu Zhao（Institute of Logistics Science and Engineering, Shanghai Maritime University）
刊号：738C0037
ISSN：1474-0346
出版年：2023
年卷期：2023, vol.58
页码：102177-1--102177-22
总页数：22
分类号：TP18; TP3
关键词：Wind power；Artificial intelligence；Patent data analysis；Co-occurrence network analysis；Link prediction
参考中译：风电;人工智能;专利数据分析;共生网络分析;环节预测
语种：eng
文摘：To track the dynamics of AI and wind power technology knowledge interaction and predict future interaction directions, this study proposes a multiview and multilayer patent analysis framework based on three data-driven methods: DMC co-occurrence networks, LDA, and link prediction. The framework is applied to collate and analyse patents related to wind power technologies using artificial intelligence from 2010 to 2021. We find that the number of AI and wind power technology knowledge interactions increases significantly over time, but the network is sparse overall and still has much room for improvement. Second, the AI and wind power technology knowledge interaction patterns show a shift from machine learning models (generation-side wind power technology) to deep learning models (generation-side and transmission- and distribution-side wind power technology) to hybrid AI models (generation, transmission, distribution, and power consumption in the whole process of wind power technology). Finally, possible future directions of interaction between AI and wind power are predicted. The proposed framework is expected to yield a new empirical perspective on green energy technology development. Additionally, the obtained results provide a comprehensive understanding of AI application research in wind power generation.
参考中译：为了跟踪人工智能和风电技术知识交互的动态，预测未来交互的方向，本研究提出了一个基于DMC共现网络、LDA和链接预测三种数据驱动方法的多视图、多层次专利分析框架。该框架用于整理和分析2010年至2021年与使用人工智能的风电技术相关的专利。我们发现，随着时间的推移，AI和风电技术知识互动的数量大幅增加，但网络总体上是稀疏的，仍有很大的改进空间。第二，AI与风电技术知识交互模式呈现出从机器学习模型(发电侧风电技术)向深度学习模型(发电侧和输变电侧风电技术)向混合型AI模型(风电技术全过程发、输、配、耗)的转变。最后，对未来人工智能与风电互动的可能方向进行了展望。预计拟议的框架将为绿色能源技术发展提供一个新的经验视角。此外，所获得的结果为人工智能在风力发电中的应用研究提供了全面的理解。



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