| Title |
Research Trend Analysis for Smart Grids Using Dynamic Topic Modeling |
| Authors |
나상태(Na, Sang-Tae) ; 안주언(Ahn, Joo-Eon) ; 정민호(Jung, Min-Ho) ; 김자희(Kim, Ja-Hee) |
| DOI |
https://doi.org/10.5370/KIEE.2017.66.4.613 |
| Keywords |
DTM (Dynamic topic modeling) ; Topic analysis ; LDA (Latent Dirichlet Allocation) ; Smart Grid |
| Abstract |
The power grid has been changed to a smart grid system to satisfy the growing need for power grid complexity, demand, reliability, security, and efficiency with a combination of existing power and ICT technology. This study analyzes the research trends in smart grid technology in the period since the introduction of the smart grid system and compares it with industrial trends to grasp the progress and characteristics of Smart Grid technology and look for ways to innovate the technology. To do this, we analyze the research trends using dynamic topic modeling, which is capable of time-series research topic analysis. Next, we compare the results of research trends with industrial trends analyzed by Gartner's experts to demonstrate that smart grid research is evolving to the level of industrialization. The results of this study are quantitative analysis through data mining, and it is expected that it will be used in many fields such as companies that want to participate in industry and government agencies that need to establish policies by showing more objective analysis results. |