Current Photovoltaic Research

  • 제12권3호
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  • Pages.49-54
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  • 2024
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  • 2288-3274(pISSN)
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  • 2508-125X(eISSN)
한국태양광발전학회 (Korea Photovoltaic Society)
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ANN/RNN 기반 태양광 발전량 예측에 관한 연구

A Study on ANN/RNN-based Photovoltaic Generation Forecasting

  • 백수웅 (전기공학과, 목포대학교 / 기업지원실, 에너지밸리기업개발원) ;
  • 권성기 (전기공학과, 목포대학교) ;
  • 김창헌 (AI에너지연구센터, 한국광기술원) ;
  • 박계춘 (전기공학과, 목포대학교)
  • Su Wung Baek (Department of Electrical Engineering, Mokpo National University/Enterprise Support Division, Energy Valley Enterprise Development Institute) ;
  • Sung Gi Kwon (Department of Electrical Engineering, Mokpo National University) ;
  • Chang Heon Kim (Artificial Intelligence & Energy Research Center, Korea Photonics Technology Institute) ;
  • Gye Choon Park (Department of Electrical Engineering, Mokpo National University)
  • 투고 : 2024.08.02
  • 심사 : 2024.09.02
  • 발행 : 2024.09.30
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초록

This study proposed a forecasting model that combines ANNs and RNNs to address the intermittency and fluidity of solar power generation. Four prediction models were trained separately based on sky conditions provided by the Korea Meteorological Administration, and insolation was estimated using the ASHRAE Clear-Sky model. The proposed model showed an error rate of 6.5-7.7% based on NMAE, which meets the requirements of power generation prediction. As a result, this study can improve the accuracy of solar power generation forecasting, which can contribute to the stability of power operation and the profitability of power operators.

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과제정보

본 결과물은 2024년도 교육부의 재원으로 한국연구재단의 지원을 받아 수행된 지자체-대학협력 기반 지역혁신 사업의 결과입니다(2021RIS-002). 본 연구는 산업통상자원부(MOTIE)와 한국에너지기술평가원(KETEP)의 지원을 받아 수행한 연구 과제입니다(RS-2024-00358809).

참고문헌

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