한국태양에너지학회 논문집 (Journal of the Korean Solar Energy Society)

  • 제39권5호
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  • Pages.41-52
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  • 2019
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  • 1598-6411(pISSN)
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  • 2508-3562(eISSN)
한국태양에너지학회 (The Korean Solar Energy Society)
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건물 예측 제어용 LSTM 기반 일사 예측 모델

Development of a Prediction Model of Solar Irradiances Using LSTM for Use in Building Predictive Control

  • 전병기 (인하대학교 대학원 건축공학과) ;
  • 이경호 (한국에너지기술연구원) ;
  • 김의종 (인하대학교 건축공학과)
  • Jeon, Byung-Ki (Department of Architectural Engineering, Graduate School, Inha University) ;
  • Lee, Kyung-Ho (Department of Solar Thermal Convergence Lab, Korea Institute of Energy Research) ;
  • Kim, Eui-Jong (Department of Architectural Engineering, Inha University)
  • 투고 : 2019.10.14
  • 심사 : 2019.10.28
  • 발행 : 2019.10.30
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초록

The purpose of the work is to develop a simple solar irradiance prediction model using a deep learning method, the LSTM (long term short term memory). Other than existing prediction models, the proposed one uses only the cloudiness among the information forecasted from the national meterological forecast center. The future cloudiness is generally announced with four categories and for three-hour intervals. In this work, a daily irradiance pattern is used as an input vector to the LSTM together with that cloudiness information. The proposed model showed an error of 5% for learning and 30% for prediction. This level of error has lower influence on the load prediction in typical building cases.

키워드

참고문헌

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