A data-driven analysis of renewable energy management: a case study of wind energy technology

Renewable energy management is critical for obtaining a significant number of practical benefits. Wind energy is one of the most important sources of renewable energy. It is extremely valuable to manage this type of energy well and monitor its development. Data-driven analysis of wind energy technology provides essential clues for energy management. Patent documents are extensively used to follow technology development and find exciting patterns. Patent analysis is an excellent way to conduct a data-driven analysis of the technology under concern. This study aims to define concepts related to wind energy technologies and cluster these concepts to manage wind energy well in practice. Although many efforts have been made in the literature on wind energy, no study defines the concepts related to wind energy technologies and clusters these concepts. This study proposes a text mining and clustering-based patent analysis approach to overcome the limitations of previous studies. Data-driven analysis collects and assesses patent documents related to wind energy technologies. Patent documents are collected from the United States Patent and Trademark Office. Text mining is applied to the abstracts of patent documents, and the k-means clustering algorithm is utilized to determine the distribution of the keywords among the clusters. The results of this study show that the contents of the patent documents are mostly related to the tower, and the propeller blades placed at the top of the tower should rotate smoothly with the wind speed for better energy production.