ETRI-Knowledge Sharing Plaform

ENGLISH

성과물

논문 검색
구분 SCI
연도 ~ 키워드

상세정보

학술대회 Multi-Site Photovoltaic Power Generation Forecasts based on Deep-learning Algorithm
Cited 6 time in scopus Download 1 time Share share facebook twitter linkedin kakaostory
저자
이정인, 이일우, 김상하
발행일
201710
출처
International Conference on Information and Communication Technology Convergence (ICTC) 2017, pp.1119-1121
DOI
https://dx.doi.org/10.1109/ICTC.2017.8190872
협약과제
17ZH1400, 제로에너지 커뮤니티 실현을 위한 에너지 공유 네트워킹 핵심 기술 개발, 이일우
초록
This paper presents the multi-site photovoltaic (PV) power generation forecast using the deep-learning algorithms. South Korea PV power generation is the most popular among renewable energy sources as government policy. Form of PV power business is small scale which need to forecast the generation of energy. This paper present the forecast model for multi-site PV power plant using deep-learning algorithm connected with renewable energy management system.
키워드
deep-learning, forecast, machine learning, Photovoltaic power, Solar
KSP 제안 키워드
Energy management system, Forecast model, Government policy, Multi-site, PV power plant, Photovoltaic (PV) power generation, Power business, Renewable energy management, Renewable energy sources(RES), Small-scale, South Korea