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기상데이터를 활용한 딥러닝 기반의 태양광 발전 단기 예측
이호준,이기혁,유지윤,박찬호,김병호,김홍래 순천향대학교 산업기술연구소 2022 순천향 산업기술연구소논문집 Vol.28 No.2
Renewable energy generation facilities are rapidly increasing in preparation for climate change. Renewable energy generation has uncertainties due to intermittency and variability, which can seriously affect the stable operation of the system. Accordingly, a renewable energy generation prediction model is devised for uncertainty analysis, and solar power generation is the highest among renewable energy, so predicting solar power generation is very helpful in planning power generation plans reasonably. This paper predicted 2021 solar power generation through solar power data traded in Gwangju Metropolitan City from 2015 to 2020, and predicted solar power generation in the same period as the result obtained through correlation analysis between weather data and solar power generation As a result, it was found that the prediction considering the weather data had a more accurate result than when only the amount of power generated was considered.