ID | 119017 |
Author |
Sari, Anggraini Puspita
Tokushima University|University of Merdeka Malang
Kitajima, Takahiro
Tokushima University
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Yasuno, Takashi
Tokushima University
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Prasetya, Dwi Arman
University of Merdeka Malang
Rabi’, Abd.
University of Merdeka Malang
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Keywords | Forecasting
wind power
LSTM
CLSTM
machine learning
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Content Type |
Journal Article
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Description | 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.
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Journal Title |
SICE Journal of Control, Measurement, and System Integration
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ISSN | 18824889
18849970
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NCID | AA12293218
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Publisher | Taylor & Francis
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Volume | 14
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Issue | 2
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Start Page | 30
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End Page | 38
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Published Date | 2021-03-19
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Rights | 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.
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EDB ID | |
DOI (Published Version) | |
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language |
eng
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TextVersion |
Publisher
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departments |
Science and Technology
Technical Support Department
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