Current Photovoltaic Research

Korea Photovoltaic Society (한국태양광발전학회)
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Estimation of Power Using PV System Model Formula and Machine Learning

태양광시스템 모델식과 기계학습을 이용한 발전성능 추정

  • Hyun Gyu Oh (Graduate School of Energy Science & Technology, Chungnam National University) ;
  • Woo Gyun Shin (Photovoltaics Research Department, Korea Institute of Energy Research) ;
  • Young Chul Ju (Photovoltaics Research Department, Korea Institute of Energy Research) ;
  • Soo Hyun Bae (Photovoltaics Research Department, Korea Institute of Energy Research) ;
  • Hye Mi Hwang (Photovoltaics Research Department, Korea Institute of Energy Research) ;
  • Gi Hwan Kang (Photovoltaics Research Department, Korea Institute of Energy Research) ;
  • Suk Whan Ko (Photovoltaics Research Department, Korea Institute of Energy Research) ;
  • Hyo Sik Chang (Graduate School of Energy Science & Technology, Chungnam National University)
  • 오현규 (에너지과학기술대학원, 충남대학교) ;
  • 신우균 (태양광연구단, 재생에너지연구소, 한국에너지기술연구원) ;
  • 주영철 (태양광연구단, 재생에너지연구소, 한국에너지기술연구원) ;
  • 배수현 (태양광연구단, 재생에너지연구소, 한국에너지기술연구원) ;
  • 황혜미 (태양광연구단, 재생에너지연구소, 한국에너지기술연구원) ;
  • 강기환 (태양광연구단, 재생에너지연구소, 한국에너지기술연구원) ;
  • 고석환 (태양광연구단, 재생에너지연구소, 한국에너지기술연구원) ;
  • 장효식 (에너지과학기술대학원, 충남대학교)
  • Received : 2023.02.15
  • Accepted : 2023.03.06
  • Published : 2023.03.31
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Abstract

In this paper, a machine learning model by using a regression algorithm is proposed to estimate the power generation performance of the BIPV system. The physical model formula for estimating the generation performance and the proposed model were compared and analyzed. For the physical model formula, simple efficiency model, temperature correction model, and regressive physics model for changing an irradiance were used. As a result, when comparing the regressive physics model for changing an irradiance and the proposed model with the actual generation measured data, the respective RMSE values are 0.1497 kW, 0.0451 kW and the accuracy values are 86.44%, and 96.56%. Therefore, the proposed model implemented in this experiment can be useful in estimating power generation.

Keywords

Acknowledgement

본 연구는 산업통상자원부와 한국에너지기술평가원의 지원을 받아 수행한 연구 과제입니다(No:20223030010200).

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