What is it about?
We discover actionable insights and knowledge from news articles on wind turbine accidents, using text mining and machine learning techniques.
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Why is it important?
This is the first study in the literature that applies text mining to a wind turbine accident news collection. The results of our research can be used by wind turbine manufacturers, engineering companies, insurance companies, and government institutions to address problem areas and enhance systems and processes throughout the wind energy value chain.
Perspectives
Text mining and machine learning can help stakeholders in wind turbine industry to address problem areas and enhance systems and processes throughout the wind energy value chain.
Gurdal Ertek
Read the Original
This page is a summary of: Text mining analysis of wind turbine accidents: An ontology-based framework, December 2017, Institute of Electrical & Electronics Engineers (IEEE),
DOI: 10.1109/bigdata.2017.8258305.
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Resources
Text mining analysis of wind turbine accidents: An ontology-based framework
As the global energy demand is increasing, the share of renewable energy and specifically wind energy in the supply is growing. While vast literature exists on the design and operation of wind turbines, there exists a gap in the literature with regards to the investigation and analysis of wind turbine accidents. This paper describes the application of text mining and machine learning techniques for discovering actionable insights and knowledge from news articles on wind turbine accidents. The applied analysis methods are text processing, clustering, and multidimensional scaling (MDS). These methods have been combined under a single analysis framework, and new insights have been discovered for the domain. The results of our research can be used by wind turbine manufacturers, engineering companies, insurance companies, and government institutions to address problem areas and enhance systems and processes throughout the wind energy value chain.
Text mining analysis of wind turbine accidents [DOWNLOAD the final draft of the paper]
As the global energy demand is increasing, the share of renewable energy and specifically wind energy in the supply is growing. While vast literature exists on the design and operation of wind turbines, there exists a gap in the literature with regards to the investigation and analysis of wind turbine accidents. This paper describes the application of text mining and machine learning techniques for discovering actionable insights and knowledge from news articles on wind turbine accidents. The applied analysis methods are text processing, clustering, and multidimensional scaling (MDS). These methods have been combined under a single analysis framework, and new insights have been discovered for the domain. The results of our research can be used by wind turbine manufacturers, engineering companies, insurance companies, and government institutions to address problem areas and enhance systems and processes throughout the wind energy value chain.
Text mining analysis of wind turbine accidents [DOWNLOAD the dataset for the research]
Dataset for the research. The dataset includes 216 news on 240 wind turbine accidents between the years 1980 and 2013. The analysis of this data set and the insights obtained are reported in the following research papers: Ertek, G., Chi, X., Zhang, A. N., & Asian, S. (2017, December). Text mining analysis of wind turbine accidents: An ontology-based framework. In Big Data (Big Data), 2017 IEEE International Conference on (pp. 3233-3241). IEEE. Asian, S., Ertek, G., Haksoz, C., Pakter, S. and Ulun, S., 2017. Wind turbine accidents: A data mining study. IEEE Systems Journal, 11(3), pp.1567-1578.
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