Earticle

초록

영어

In this study, we develop a time series based solar power failure determination algorithm that predicts its own generation amount by sharing power generation information with neighboring sites without relying on meteorological data of the Meteorological Agency and compares it with the power generation amount obtained in real time to determine the presence or absence of a failure. For the implementation of the algorithm, we design a prediction model based on deep learning using LSTM function and implement a model that predicts the amount of solar power that changes in real time. After the development of LSTM model, the RMSE was 93.85 as a result of preliminary test by comparing the predicted and the measured photovoltaic power generation. As the data learning process progresses and as the optimization process is continued, the prediction performance is expected to be further improved.

목차

Abstract
1. 서론
2. 연구방법
2.1. 딥러닝 기반 예측 모델
2.2. 딥러닝 기반 예측 모델
3. 실험 및 분석
4. 결론
Acknowledgement
References

키워드

저자

• Tae Won Choi [ CEO, U-energy Co. Ltd ]
• Young Suk Song [ Rearch Director, U-energy Co. Ltd ]
• Heon Jeong [ Department of Fire Service Administration Chodang University ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004