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 and 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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EE2021-2 |
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) |
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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 |
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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 |
6 |
Date of Issue |
2021-05-20 (EE) |
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