Performance Evaluation of LSTM Based Solar Irradiance Prediction Models without Long-term Local Weather Data

Byung-ki Jeon; Eui-Jong Kim

doi:10.7836/kses.2023.43.5.043

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2023 Vol.43, Issue 5 Preview Page Next Page

Research Article

Performance Evaluation of LSTM Based Solar Irradiance Prediction Models without Long-term Local Weather Data 누적 데이터 부족 시 활용 가능한 로컬 일사 예측 모델 개발 및 평가

30 October 2023. pp. 43-59

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Abstract

Prediction of solar irradiance that influences the building load and production of new and renewable energy is crucial in the predictive control of buildings. However, most solar irradiance prediction models are developed using weather data measured over a long term in the target region. This study proposed a solar irradiance prediction model based on a hybrid layer that combined a long short-term memory layer and a bi-directional long short-term memory layer. Next, the solar irradiance of the following day was predicted using only the hourly irradiance measured on site one day before the prediction day using a model trained with the weather data of another region. The proposed solar irradiance prediction model showed an RMSE of 69.5 W/m², a level suitable for the predictive control of buildings. Moreover, although training data from a different region was used, the performance of the proposed model was similar to that of a model trained with long-term data measured in the target region.

Keywords

Solar irradiance

Long term short term memory

Weather prediction

Model predictive control

키워드

태양 일사

LSTM

기상예측

모델예측제어

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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 : 43
No :5
Pages :43-59
Received Date : 2023-03-30
Revised Date : 2023-08-03
Accepted Date : 2023-10-10
DOI :https://doi.org/10.7836/kses.2023.43.5.043

Journal of the Korean Solar Energy Society ISSN:1598-6411(Print) 2508-3562(Online) 한국태양에너지학회 논문집

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