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SCOPUS 문헌 정보 분석을 통한 머신 러닝 활용 BIPV 연구 동향
이제현(Lee Jehyun),유시현(You Sihyun),김창기(Kim Chang Ki),오명찬(Oh Myeongchan),김보영(Kim Boyoung),강용혁(Kang Yong-Heack),김현구(Kim Hyun-Goo) 한국태양에너지학회 2022 한국태양에너지학회 논문집 Vol.42 No.3
With the accelerated development of science and technology across fields, publications in almost all fields have increased exponentially every year. According to the Scopus search, the number of papers related to buildings and solar energy that used machine learning has grown at an average annual rate of 16.1%, exceeding 3000 publications since 2019. Review papers have been published consistently since 2010, demonstrating that they are on a trajectory of initial growth. Because there is a limit to reading and analyzing large quantities of papers every year, we developed a methodology that reads, analyzes, and visualizes published literature information. In this method, we provide a query to the Scopus database to retrieve data and then visualize the number of publications by year, journal, and keyword along with other analyses results. The relationship and frequency analyses results can also be shown among words in titles, keywords, and abstracts. Analysis of research on building-integrated photovoltaics yielded the result that publishing on Energy and Buildings (impact factor [IF] 4.067, modified rank normalized impact factor [mrnIF] top 1.7%) and Solar Energy (IF 4.018, mrnIF top 1.7%) is active. Over the past 10 years, approximately 1–1.5% of machine learning-related research has been published, with a gradual increase.