Prediction of Wind Speed and Direction using Encoding - forecasting Network with Convolutional Long Short-term Memory
| Autor: | Takashi Yasuno, Takahiro Kitajima, Hiroshi Suzuki, Anggraini Puspita Sari, Abd. Rabi, Dwi Arman Prasetya |
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| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Mean squared error Computer science 020208 electrical & electronic engineering 02 engineering and technology Wind direction Prediction system Wind speed Long short term memory 020901 industrial engineering & automation Automated Meteorological Data Acquisition System Encoding (memory) 0202 electrical engineering electronic engineering information engineering Algorithm Physics::Atmospheric and Oceanic Physics |
| Zdroj: | SICE Scopus-Elsevier |
| DOI: | 10.23919/sice48898.2020.9240261 |
| Popis: | This paper presents the prediction system of wind speed and direction one hour ahead using encoding-forecasting network with convolutional long short-term memory (ConvLSTM). The input of prediction system is wind speed and direction which are represented as image data on the 2D coordinate and provided by Automated Meteorological Data Acquisition System (AMeDAS) in Japan. Performances of the proposed prediction system are evaluated based on root mean square error (RMSE) between observed and predicted value. The goal of the proposed prediction system is to improve prediction accuracy and it is confirmed by comparing the result of the prediction system of four seasons. |
| Databáze: | OpenAIRE |
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