An Analysis on Wind Speed Forecasting Result with the Elman Recurrent Neural Network Method

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 th...

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Autor principal:	Bettiza Martaleli
Formato:	article
Lenguaje:	EN FR
Publicado:	EDP Sciences 2021
Materias:	Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/e2e14bf790354412b2e4d3410add42a2
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spelling	oai:doaj.org-article:e2e14bf790354412b2e4d3410add42a22021-12-02T17:11:45ZAn Analysis on Wind Speed Forecasting Result with the Elman Recurrent Neural Network Method2267-124210.1051/e3sconf/202132405002https://doaj.org/article/e2e14bf790354412b2e4d3410add42a22021-01-01T00:00:00Zhttps://www.e3s-conferences.org/articles/e3sconf/pdf/2021/100/e3sconf_macific2021_05002.pdfhttps://doaj.org/toc/2267-1242Weather 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.Bettiza MartaleliEDP SciencesarticleEnvironmental sciencesGE1-350ENFRE3S Web of Conferences, Vol 324, p 05002 (2021)
institution	DOAJ
collection	DOAJ
language	EN FR
topic	Environmental sciences GE1-350
spellingShingle	Environmental sciences GE1-350 Bettiza Martaleli An Analysis on Wind Speed Forecasting Result with the Elman Recurrent Neural Network Method
description	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.
format	article
author	Bettiza Martaleli
author_facet	Bettiza Martaleli
author_sort	Bettiza Martaleli
title	An Analysis on Wind Speed Forecasting Result with the Elman Recurrent Neural Network Method
title_short	An Analysis on Wind Speed Forecasting Result with the Elman Recurrent Neural Network Method
title_full	An Analysis on Wind Speed Forecasting Result with the Elman Recurrent Neural Network Method
title_fullStr	An Analysis on Wind Speed Forecasting Result with the Elman Recurrent Neural Network Method
title_full_unstemmed	An Analysis on Wind Speed Forecasting Result with the Elman Recurrent Neural Network Method
title_sort	analysis on wind speed forecasting result with the elman recurrent neural network method
publisher	EDP Sciences
publishDate	2021
url	https://doaj.org/article/e2e14bf790354412b2e4d3410add42a2
work_keys_str_mv	AT bettizamartaleli ananalysisonwindspeedforecastingresultwiththeelmanrecurrentneuralnetworkmethod AT bettizamartaleli analysisonwindspeedforecastingresultwiththeelmanrecurrentneuralnetworkmethod
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An Analysis on Wind Speed Forecasting Result with the Elman Recurrent Neural Network Method

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