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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
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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
일사예측(Prediction of solar irradiance); LSTM(Long-term short-term memory); 모델 예측제어(Model Predictive control); 딥러닝(Deep Neural Network)
- 제목
- 건물 예측 제어용 LSTM 기반 일사 예측 모델
- 제목 (타언어)
- Development of a Prediction Model of Solar Irradiances Using LSTM for use in Building Predictive Control
- 저자
- 전병기; 이경호; 김의종
- 발행일
- 2019-10
- 유형
- Y
- 저널명
- 한국태양에너지학회 논문집
- 권
- 39
- 호
- 5
- 페이지
- 41 ~ 52