Long-term Solar Radiation Forecasting using a Deep Learning Approach-GRUs
ID:203 View Protection:ATTENDEE Updated Time:2020-11-11 12:10:03 Hits:312 Oral Presentation

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Abstract
A long-term solar generation forecasting is an important issue in a microgrid design. Solar generation forecasting mainly depends on solar radiation forecasting. In this paper, Deep Learning approach-GRUs (Gated Recurrent Units) is proposed for forecasting of a year-ahead hourly and daily solar radiation. The proposed GRU model is compared with the state of the art methods like Long Short Term Memory (LSTM) model and numerical model. Its effectiveness for long-term solar radiation forecasting over other methods is verified.
Keywords
Deep Learning,Renewable Energy,Solar Radiation Forecasting,gated recurrent unit,Long-short term memory,Microgrids
Speaker
Muhammad Aslam
Myongji University

Submission Author
Muhammad Aslam Myongji University
Hyung Seung Kim Myongji University
Seung Jae Lee Myongji University
Jae-Myeong Lee Myongji University
Sugwon Hong Myongji University
Eui Hyang Lee Next-Generation Power Technology Center (NPTC)
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Important Date
  • Conference Date

    Oct 21

    2019

    to

    Oct 24

    2019

  • Oct 13 2019

    Abstract Notification of Acceptance

  • Oct 13 2019

    Draft paper submission deadline

  • Oct 14 2019

    Draft Paper Acceptance Notification

  • Oct 24 2019

    Registration deadline

  • Oct 29 2019

    Final Paper Deadline

Organized By
Xi'an Jiaotong University
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