Deep convolutional long short-term memory for forecasting wind speed and direction
| Autor: | Anggraini Puspita Sari, Hiroshi Suzuki, Takahiro Kitajima, Takashi Yasuno, Dwi Arman Prasetya, Abd. Rabi' |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | SICE Journal of Control, Measurement, and System Integration, Vol 14, Iss 2, Pp 30-38 (2021) |
| Druh dokumentu: | article |
| ISSN: | 1884-9970 18824889 |
| DOI: | 10.1080/18824889.2021.1894878 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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