Earticle

초록

영어

Sustainable power systems should include solar energy generation. However, for effective grid management and the integration of renewable energy sources, accurate solar power generation predictions are essential. Therefore, this study compares the prediction of solar power forecasting in Italy and Bulgaria. These are two countries that have alike latitudes but different populations and solar energy production. The historical solar power generation and meteorological data from these countries are preprocessed and then used to apply four different deep learning models including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The results are analyzed to gain insights into how the proximity of geographical locations and the quality and quantity of data impact the precision of prediction algorithms.

목차

Abstract
I. INTRODUCTION
II. METHODOLOGY
A. Data Collection and Preprocessing
B. Model Training and Selection
C. Model Testing and Evaluation
III. RESULTS
A. Italy Data Results
B. Bulgaria Data Results
IV. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Noman Khan [ Intelligent Media Laboratory, Digital Contents Research Institute Sejong University ]
• Waseem Ullah [ Intelligent Media Laboratory, Digital Contents Research Institute Sejong University ]
• Zulfiqar Ahmad Khan [ Intelligent Media Laboratory, Digital Contents Research Institute Sejong University ]
• Adnan Hussain [ Intelligent Media Laboratory, Digital Contents Research Institute Sejong University ]
• Min Je Kim [ Intelligent Media Laboratory, Digital Contents Research Institute Sejong University ]
• Sang Il Yoon [ Intelligent Media Laboratory, Digital Contents Research Institute Sejong University ]
• Sung Wook Baik [ Intelligent Media Laboratory, Digital Contents Research Institute Sejong University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004