| Item type |
文献 / Documents(1) |
| 公開日 |
2024-03-11 |
| アクセス権 |
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|
アクセス権 |
open access |
| 資源タイプ |
|
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| 出版社版DOI |
|
|
|
関連識別子 |
https://doi.org/10.1080/18824889.2021.1894878 |
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|
関連名称 |
10.1080/18824889.2021.1894878 |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| タイトル |
|
|
タイトル |
Deep convolutional long short-term memory for forecasting wind speed and direction |
| 著者 |
Sari, Anggraini Puspita
鈴木, 浩司
北島, 孝弘
安野, 卓
Prasetya, Dwi Arman
Rabi’, Abd.
|
| 抄録 |
|
|
内容記述 |
This paper proposed deep learning to create an accurate forecasting system that uses a deep convolutional long short-term memory (DCLSTM) for forecasting wind speed and direction. In order to use the DCLSTM system, wind speed and direction are represented as an image in 2D coordinates and make it to time sequence data. The wind speed and direction data were obtained from AMeDAS (Automated Meteorological Data Acquisition System), Japan. The target of the proposed forecasting system was to improve forecasting accuracy compared to the system in SICE 2020 (The Society of Instrument and Control Engineers Annual Conference 2020) in all seasons. For verifying the efficiency of the forecasting system by comparison with persistent system, deep fully connected-LSTM (DFC-LSTM) and encoding-forecasting network with convolutional long short-term memory (CLSTM) systems were investigated. Forecasting performance of the system was evaluated by RMSE (root mean square error) between forecasted and measured data. |
| キーワード |
|
|
主題 |
Forecasting |
| キーワード |
|
|
主題 |
wind power |
| キーワード |
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主題 |
LSTM |
| キーワード |
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|
主題 |
CLSTM |
| キーワード |
|
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主題 |
machine learning |
| 書誌情報 |
en : SICE Journal of Control, Measurement, and System Integration
巻 14,
号 2,
p. 30-38,
発行日 2021-03-19
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| 収録物ID |
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収録物識別子タイプ |
ISSN |
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収録物識別子 |
18824889 |
| 収録物ID |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
18849970 |
| 収録物ID |
|
|
収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA12293218 |
| 出版者 |
|
|
出版者 |
Taylor & Francis |
| 権利情報 |
|
|
権利情報 |
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| EID |
|
|
識別子 |
380867 |
| 言語 |
|
|
言語 |
eng |
sicejcmsi_14_2_30.pdf (2.03 MB)
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