| 학회 |
한국화학공학회 |
| 학술대회 |
2021년 봄 (04/21 ~ 04/23, 부산 BEXCO) |
| 권호 |
27권 1호, p.298 |
| 발표분야 |
공정시스템 |
| 제목 |
A text mining survey to obtain the most likely smart optimization algorithms in renewable energies |
| 초록 |
Two main hindrances to adopt climate change by establishing 100 % green energies are high costs and low efficiencies of renewable energy systems. Hence, system optimization has gained special significance in technology improvements. A significant share of the literature is devoted to extensive applications of various smart optimization algorithms in renewable energies. Here, a text mining algorithm is employed to survey the state-of-the-art literature with a logical combination of categorized keywords to obtain the most abundant optimization algorithms in renewable energies. The results showed that the meta-heuristic algorithms were frequently updated and applied in recent years. Accordingly, genetic and particle swarm optimization algorithms have the greatest shares among all categories, respectively.This work was supported by Brain Pool Program through the National Research Foundation of Korea (KRF) funded by the Ministry of Science and ICT (2019H1D3A1A02071051), Korea Ministry of Environment (MOE) as Graduate School specialized in Climate Change and Korea Ministry of Environment as "Prospective green technology innovation project (2020003160009 )". |
| 저자 |
POUYA IFAEI, Amir Saman Tayerani Charmchi, 남기전, 유창규
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| 소속 |
경희대 |
| 키워드 |
인공지능 기반 공정기술 |
| E-Mail |
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| 원문파일 |
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