Mapping the Academic Landscape of the Renewable Energy Field in Electrical and Electronic Disciplines
| Autor: | Likai Liang, Liang Guo, Wenxiu Xu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Topic model
Computer science 020209 energy engineering Context (language use) 02 engineering and technology Latent Dirichlet allocation lcsh:Technology Field (computer science) lcsh:Chemistry symbols.namesake 0202 electrical engineering electronic engineering information engineering autoregressive integrated moving average (ARIMA) General Materials Science Autoregressive integrated moving average Instrumentation lcsh:QH301-705.5 Fluid Flow and Transfer Processes latent dirichlet allocation (LDA) business.industry lcsh:T Process Chemistry and Technology 020208 electrical & electronic engineering General Engineering electrical and electronic Data science renewable energy lcsh:QC1-999 Computer Science Applications Renewable energy lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 symbols Trajectory Organizational structure coherence score business lcsh:Engineering (General). Civil engineering (General) lcsh:Physics |
| Zdroj: | Applied Sciences Volume 10 Issue 8 Applied Sciences, Vol 10, Iss 2879, p 2879 (2020) |
| ISSN: | 2076-3417 |
| DOI: | 10.3390/app10082879 |
| Popis: | Research on renewable energy fields of electrical and electronic disciplines is key to promoting the efficient production and utilization of renewable energy, but its branches are numerous and development is uneven. This article examines the topic distribution and future development trajectory of this field, and aims to provide academic researchers with the clearest development context and organizational structure in this field. This study obtained all fields from 3743 articles in the field of renewable energy from the Web of Science (WoS) database, with a time span of 1992&ndash 2018. We applied statistical analysis, the latent dirichlet allocation (LDA) topic model, and the autoregressive integrated moving average (ARIMA) model to map topic landscapes in the field of renewable energy. By analyzing these fields, we discovered the digital characteristics of the field and divided the field into 29 different topics, such as &ldquo Power conversion technology&rdquo &ldquo Micro-grid&rdquo and &ldquo Electric vehicles and hybrid electric vehicles&rdquo and analyzed the growth characteristics of topics in two time periods&mdash 1992&ndash 2005 and 2005&ndash 2018. Finally, based on the development trajectory of each topic, we predicted their future development enthusiasm, which was divided into cold, hot, and stable. We compiled statistics on the most popular outlets and citations for each topic, making it easy for researchers and journal editors to appreciate and apply them. |
| Databáze: | OpenAIRE |
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