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2022 Vol.42, Issue 3 Preview Page

Review

Trend Summary by SCOPUS Literature Analysis on Machine Learning Assisted BIPV Research

SCOPUS 문헌 정보 분석을 통한 머신 러닝 활용 BIPV 연구 동향

Jehyun Lee1†
,
Sihyun You2†
,
Chang Ki Kim3
,
Myeongchan Oh4
,
Boyoung Kim4
,
Yong-Heack Kang3
,
Hyun-Goo Kim3*
이 제현1†
,
유 시현2†
,
김 창기3
,
오 명찬4
,
김 보영4
,
강 용혁3
,
김 현구3*
1Senior Researcher, Computational Science & Engineering Laboratory, Korea Institute of Energy Research
2Research Intern, Department of French Language and Liturature, Chungnam National University
3Principal Researcher, New and Renewable Energy Resource Map Laboratory, Korea Institute of Energy Research
4Senior Researcher, New and Renewable Energy Resource Map Laboratory, Korea Institute of Energy Research
1한국에너지기술연구원 계산과학연구실, 선임연구원
2충남대학교 불어불문학과, 학부인턴
3한국에너지기술연구원 신재생자원지도연구실, 책임연구원
4한국에너지기술연구원 신재생자원지도연구실, 선임연구원
*Corresponding Author
†These authors contributed equally to this work.
30 June 2022. pp. 13-32
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Abstract

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.

Keywords
Literature data analysis
Library Science
Text Mining
BIPV
Machine Learning
키워드
문헌 정보 분석
문헌 정보학
텍스트 마이닝
건물일체형 태양광 발전
머신 러닝
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Information
  • Publisher :Korean Solar Energy Society
  • Publisher(Ko) :한국태양에너지학회
  • Journal Title :Journal of the Korean Solar Energy Society
  • Journal Title(Ko) :한국태양에너지학회 논문집
  • Volume : 42
  • No :3
  • Pages :13-32
  • Received Date : 2022-04-06
  • Revised Date : 2022-04-25
  • Accepted Date : 2022-04-26
  • DOI :https://doi.org/10.7836/kses.2022.42.3.013
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