반도체디스플레이기술학회지 (Journal of the Semiconductor & Display Technology)

한국반도체디스플레이기술학회 (The Korean Society Of Semiconductor & Display Technology)
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측정 데이터를 이용한 태양광 발전량 예측 모델

Prediction Model for Solar Power Generation Using Measured Data

  • 박영서 (상명대학교 시스템반도체공학부) ;
  • 강상민 (상명대학교 시스템반도체공학부) ;
  • 문주석 (상명대학교 시스템반도체공학부) ;
  • 조성준 (상명대학교 시스템반도체공학부) ;
  • 이종환 (상명대학교 시스템반도체공학부)
  • Yeongseo Park (Department of System Semiconductor Engineering, Sangmyung University) ;
  • Sangmin kang (Department of System Semiconductor Engineering, Sangmyung University) ;
  • Juseok Moon (Department of System Semiconductor Engineering, Sangmyung University) ;
  • Seongjun Cho (Department of System Semiconductor Engineering, Sangmyung University) ;
  • Jonghwan Lee (Department of System Semiconductor Engineering, Sangmyung University)
  • 투고 : 2024.08.20
  • 심사 : 2024.09.14
  • 발행 : 2024.09.30
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초록

Previous research on solar power generation forecasting has generally relied on meteorological data, leading to lower prediction accuracy. This study, in contrast, uses actual measured power generation data to train various ANN (Artificial Neural Network) models and compares their prediction performance. Additionally, it describes the characteristics and advantages of each ANN model. The paper defines the principles of solar power generation, the characteristics of solar panels, and the model equations, and it also explains the I-V characteristics of solar cells. The results include a comparison between calculated and actual measured power generation, along with an evaluation of the accuracy of power generation predictions using artificial intelligence. The findings confirm that the LSTM (Long Short-Term Memory) model performs better than the MLP (Multi- Layer Perceptron) model in handling time-series data.

키워드

과제정보

This work was supported by the International Science & Business Belt support program, through the Korea Innovation Foundation funded by the Ministry of Science and ICT. This research was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No.2022R1I1A3064285).

참고문헌

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