Purpose

The purpose of this paper is to analyze the wind energy technologies using the social network analysis based on patent information. Analysis of patent documents with social network analysis is used to identify the most influential and connected technologies in the field of wind energy.

Design/methodology/approach

In the literature, patent data are often used to evaluate technologies. Patents related to wind energy technologies are obtained from the United States Patent and Trademark Office database and the relationships among sub-technologies based on Corporate Patent Classification (CPC) codes are analyzed in this study. The results of two-phase algorithm for mining high average-utility itemsets algorithm, which is one of the utility mining algorithm in data mining, is used to find associations among wind energy technologies for social network analysis.

Findings

The results of this study show that it is very important to focus on wind motors and technologies related to energy conversion or management systems reducing greenhouse gas emissions. The results of this study imply that Y02E, F03D and F05B CPC codes are the most influential CPC codes based on social network analysis.

Originality/value

Analysis of patent documents with social network analysis for technology evaluation is extremely limited in the literature. There is no research related to the analysis of patent documents with social network analysis, in particular CPC codes, for wind energy technology. This paper fills this gap in the literature. This study explores technologies related to wind energy technologies and identifies the most influential wind energy technologies in practice. This study also extracts useful information and knowledge to identify core corporate patent class (es) in the field of wind energy technology.

中文翻译：

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社会网络分析的风能技术发展

目的

本文的目的是使用基于专利信息的社交网络分析来分析风能技术。通过社交网络分析对专利文件进行分析，以识别风能领域中最具影响力和最紧密联系的技术。

设计/方法/方法

在文献中，专利数据通常用于评估技术。与风能技术相关的专利是从美国专利商标局的数据库中获得的，本研究分析了基于公司专利分类（CPC）代码的子技术之间的关系。数据挖掘中的实用挖掘算法之一，是用于挖掘高平均效用项集算法的两阶段算法的结果，用于查找风能技术之间的关联以进行社交网络分析。

发现

这项研究的结果表明，重点关注与降低温室气体排放量的能源转换或管理系统相关的风力发动机和技术非常重要。研究结果表明，根据社交网络分析，Y02E，F03D和F05B CPC代码是最具影响力的CPC代码。

创意/价值

在文献中，使用社交网络分析进行专利文献分析以进行技术评估非常有限。目前尚无与使用社交网络分析对专利文件进行分析有关的研究，尤其是针对风能技术的CPC代码。本文填补了文献中的空白。这项研究探索了与风能技术有关的技术，并确定了实践中最具影响力的风能技术。这项研究还提取了有用的信息和知识，以识别风能技术领域的核心公司专利类别。