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2019 Vol.39, Issue 5 Preview Page

Research Article

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

건물 예측 제어용 LSTM 기반 일사 예측 모델

Byung-Ki Jeon1
,
Kyung-Ho Lee2
,
Eui-Jong Kim3*
전 병기1
,
이 경호2
,
김 의종3*
1Ph.D. Candidate, Department of Architectural Engineering, Graduate School, Inha University
2Principal Researcher, Department of Solar Thermal Convergence Lab, Korea Institute of EnergyResearch
3Assistant Professor, Department of Architectural Engineering, Inha University
1인하대학교 대학원 건축공학과, 박사과정
2한국에너지기술연구원, 책임연구원
3인하대학교 건축공학과, 조교수
*Corresponding Author 
30 October 2019. pp. 41-52
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Abstract

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.

Keywords
Prediction of solar irradiance
Long-term short-term memory
Model Predictive control
Deep Neural Network

키워드
일사예측
LSTM
모델 예측제어
딥러닝
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Information
  • Publisher :Korean Solar Energy Society
  • Publisher(Ko) :한국태양에너지학회
  • Journal Title :Journal of the Korean Solar Energy Society
  • Journal Title(Ko) :한국태양에너지학회 논문집
  • Volume : 39
  • No :5
  • Pages :41-52
  • Received Date : 2019-10-14
  • Revised Date : 2019-10-24
  • Accepted Date : 2019-10-28
  • DOI :https://doi.org/10.7836/kses.2019.39.5.041
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