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Paper Abstract and Keywords
Presentation 2021-05-27 09:25
LSTM-based Neural Network Model for Predicting Solar Power Generation
Kundjanasith Thonglek, Kohei Ichikawa (NAIST), Kazufumi Yuasa, Tadatoshi Babasaki (NTT-F) EE2021-2
Abstract (in Japanese) (See Japanese page) 
(in English) Currently, the most popular renewable energy is solar power which reduces pollution consequences from using conventional fossil fuels. Solar power converts sunlight either directly or indirectly into electricity. However, using solar power generation as a stable power supply is still challenging since the amount of solar power generated in a day is difficult to be predicted. Accurately predicting solar power generation enables controlling the amount of stored electricity in batteries to produce stable electricity. This paper aims to improve controlling the amount of stored electricity in batteries by predicting future solar power generation. We designed and implemented a neural network model based on Long Short-Term Memory (LSTM) to predict the future solar power generation using the past solar power generation and weather forecasts. Moreover, stratified K-fold cross-validation is applied to eliminate learning deviation during the training process. Through hyperparameter tuning, we have built a neural network model with one LSTM layer. As a result, the proposed model has achieved an R2 score of around 0.78 with cross-validation.
Keyword (in Japanese) (See Japanese page) 
(in English) Time-Series Forecasting / Long Short-Term Memory / Solar Power Generation / / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 40, EE2021-2, pp. 7-12, May 2021.
Paper # EE2021-2 
Date of Issue 2021-05-20 (EE) 
ISSN Online edition: ISSN 2432-6380
Copyright
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reproduction
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee EE IEE-HCA  
Conference Date 2021-05-27 - 2021-05-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Switching power supply, New industrial and home appliance for power, others 
Paper Information
Registration To EE 
Conference Code 2021-05-EE-HCA 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) LSTM-based Neural Network Model for Predicting Solar Power Generation 
Sub Title (in English)  
Keyword(1) Time-Series Forecasting  
Keyword(2) Long Short-Term Memory  
Keyword(3) Solar Power Generation  
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1st Author's Name Kundjanasith Thonglek  
1st Author's Affiliation Nara Institute of Science and Technology (NAIST)
2nd Author's Name Kohei Ichikawa  
2nd Author's Affiliation Nara Institute of Science and Technology (NAIST)
3rd Author's Name Kazufumi Yuasa  
3rd Author's Affiliation NTT Facilities, INC. (NTT-F)
4th Author's Name Tadatoshi Babasaki  
4th Author's Affiliation NTT Facilities, INC. (NTT-F)
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Speaker Author-1 
Date Time 2021-05-27 09:25:00 
Presentation Time 25 minutes 
Registration for EE 
Paper # EE2021-2 
Volume (vol) vol.121 
Number (no) no.40 
Page pp.7-12 
#Pages
Date of Issue 2021-05-20 (EE) 


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