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

다국어 초록 (Multilingual Abstract)

Prediction of solar irradiance that influences the building load and production of new andrenewable energy is crucial in the predictive control of buildings. However, most solarirradiance prediction models are developed using weather data measured over a long term in thetarget region. This study proposed a solar irradiance prediction model based on a hybrid layerthat combined a long short-term memory layer and a bi-directional long short-term memorylayer. Next, the solar irradiance of the following day was predicted using only the hourlyirradiance measured on site one day before the prediction day using a model trained with theweather data of another region. The proposed solar irradiance prediction model showed anRMSE 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 modelwas similar to that of a model trained with long-term data measured in the target region.