Issue |
E3S Web Conf.
Volume 324, 2021
Maritime Continent Fulcrum International Conference (MaCiFIC 2021)
|
|
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Article Number | 05002 | |
Number of page(s) | 4 | |
Section | Innovative Technology for Sustainbale Development Goals (SGDs) | |
DOI | https://doi.org/10.1051/e3sconf/202132405002 | |
Published online | 16 November 2021 |
An Analysis on Wind Speed Forecasting Result with the Elman Recurrent Neural Network Method
Department of Informatics, Maritime University of Raja Ali Haji, Tanjungpinang, Indonesia
* Corresponding author: mbettiza@umrah.ac.id
Weather factors in the archipelago have an important role in sea transportation. Weather factors, especially wind speed and wave height, become the determinants of sailing permits besides transportation’s availability, routes, and fuel. Wind speed is also a potential source of renewable energy in the archipelago. Accurate wind speed forecasting is very useful for marine transportation and development of wind power technology. One of the methods in the artificial neural network field, Elman Recurrent Neural Network (ERNN), is used in this study to forecast wind speed. Wind speed data in 2019 from measurements at the Badan Meteorolog Klimatologi dan Geofisika (BMKG) at Hang Nadim Batam station were used in the training and testing process. The forecasting results showed an accuracy rate of 88.28% on training data and 71.38% on test data. The wide data range with the randomness and uncertainty of wind speed is the cause of low accuracy. The data set is divided into the training set and the testing set in several ratio schemas. The division of this data set considered to have contributed to the MAPE value. The observation data and data division carried out in different seasons, with varying types of wind cycles. Therefore, the forecasting results obtained in the training process are 17% better than the testing data.
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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