Paper Abstract and Keywords |
Presentation |
2021-05-28 15:15
Local-location and short-term solar power forecast system for marine environment observation devices Tetsuo Imai (Hiroshima City Univ.), Satoshi Ichimaru, Kenichi Arai, Toru Kobayashi (Nagasaki Univ.) SC2021-8 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Marine environmental observatories for environmental assessment offshore are powered by renewable energy sources such as solar power. However, the amount of electricity generated by renewable energy sources is unstable, and weather forecasts are still not sufficiently accurate, resulting in frequent refueling of offshore instruments due to depletion of fuel cells. To reduce this problem, it is necessary to improve the accuracy of local and short-term forecasts of solar power generation. In this article, by commercial weather forecasts for multiple locations and multiple times, we propose an LSTM-based solar power forecasting method that can follow the "positional shift" and "time shift" of forecasts. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
LSTM / recurrent neural network / solar power generation / solar radiation forecast / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 121, no. 51, SC2021-8, pp. 39-43, May 2021. |
Paper # |
SC2021-8 |
Date of Issue |
2021-05-21 (SC) |
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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SC2021-8 |
Conference Information |
Committee |
SC |
Conference Date |
2021-05-28 - 2021-05-28 |
Place (in Japanese) |
(See Japanese page) |
Place (in English) |
Online |
Topics (in Japanese) |
(See Japanese page) |
Topics (in English) |
Cyber-Physical-Social System、IOT, Machine Learning Application, etc general |
Paper Information |
Registration To |
SC |
Conference Code |
2021-05-SC |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Local-location and short-term solar power forecast system for marine environment observation devices |
Sub Title (in English) |
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Keyword(1) |
LSTM |
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recurrent neural network |
Keyword(3) |
solar power generation |
Keyword(4) |
solar radiation forecast |
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1st Author's Name |
Tetsuo Imai |
1st Author's Affiliation |
Hiroshima City University (Hiroshima City Univ.) |
2nd Author's Name |
Satoshi Ichimaru |
2nd Author's Affiliation |
Nagasaki University (Nagasaki Univ.) |
3rd Author's Name |
Kenichi Arai |
3rd Author's Affiliation |
Nagasaki University (Nagasaki Univ.) |
4th Author's Name |
Toru Kobayashi |
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Nagasaki University (Nagasaki Univ.) |
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Speaker |
Author-1 |
Date Time |
2021-05-28 15:15:00 |
Presentation Time |
25 minutes |
Registration for |
SC |
Paper # |
SC2021-8 |
Volume (vol) |
vol.121 |
Number (no) |
no.51 |
Page |
pp.39-43 |
#Pages |
5 |
Date of Issue |
2021-05-21 (SC) |
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