Earticle

초록

영어

The integration of solar energy with a power system brings great economic and environmental benefits. However, the high penetration of solar power challenges the operation and planning of the existing power system owing to the intermittence and randomicity of solar power generation. Achieving accurate prediction for power generation is important to provide balanced electric energy for end-users. Therefore, in this paper, we introduce a deep learning-based dual stream Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network to learn spatial patterns using CNN and temporal features via the LSTM network. These features are then fused via a concatenation layer and then feed forward to Dense layers for optimal features selection and future solar power prediction. The performance of the proposed model is evaluated on benchmark datasets and achieved a new state-of-the-art on these datasets.

목차

Abstract
1. Introduction
2. The Proposed Method
3. Experimental Results
3.1. Comparative Analysis
3.2. Comparison with state-of-the-art
4. Conclusion
Acknowledgment
References

키워드

저자

• Zulfiqar Ahmad Khan [ Sejong University ]
• Noman Khan [ Sejong University ]
• Su Min Lee [ Sejong University ]
• Sang Il Yoon [ Sejong University ]
• Mi Young Lee [ Sejong University ]
• Sung Wook Baik [ Sejong University ] Corresponding author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004