Image-based Solar Irradiance Forecasting Using Recurrent Neural Networks

Tsai Ping Chu, Jian Hua Jhou, Yih Guang Leu*

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

1 引文 斯高帕斯(Scopus)

摘要

The solar power variability is due to the variability of solar irradiance. Several factors are involved in the situation, such as cloud thickness and air pollution. In this paper, we attempt to find a novel way to predict the amount of solar irradiance. A image-based forecasting method is developed, and Long Short-Term Memory (LSTM) neural network is applied for data training. Daily solar irradiance and sky images are record by the record system, and uploaded to the MySQL database for storage. Feature values obtained by analyzing sky images are used as the input of neural network with solar irradiance. After some performance evaluation indicators were demonstrated, we found that the proposed method has good predictive performance with 5 to 60 minutes in present.

原文英語
主出版物標題2020 International Conference on System Science and Engineering, ICSSE 2020
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728159607
DOIs
出版狀態已發佈 - 2020 八月
事件2020 International Conference on System Science and Engineering, ICSSE 2020 - Kagawa, 日本
持續時間: 2020 八月 312020 九月 3

出版系列

名字2020 International Conference on System Science and Engineering, ICSSE 2020

會議

會議2020 International Conference on System Science and Engineering, ICSSE 2020
國家/地區日本
城市Kagawa
期間2020/08/312020/09/03

ASJC Scopus subject areas

  • 人工智慧
  • 能源工程與電力技術
  • 可再生能源、永續發展與環境
  • 土木與結構工程
  • 電氣與電子工程
  • 控制和優化

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