Abstract
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.