LSTM 모델을 활용한 태양광 발전량 예측 모델의 예측 정확성 확보를 위한 적정 학습 기간에 관한 연구 = The Examination of Appropriate Learning Periods to Improve the Prediction Accuracy of Solar Power Generation with a LSTM Model

다국어 초록 (Multilingual Abstract)

The purpose of this study is to develop a PV power generation machine learning model using the LSTM model, verify its accuracy, and derive an appropriate learning period to ensure the appropriateness of the solar power generation prediction model. A comparison of short-term power generation data predicted by the machine learning model with data measured at the corresponding forecast time showed R² = 0.9499, indicating sufficient accuracy. Finally, the minimum learning period to secure a certain level of prediction through a review of the prediction result was reviewed using the power generation data already secured and the weather information at the same point during the period; 30 days were calculated as an appropriate learning period to ensure the accuracy of the solar power generation prediction model.