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A comparative study of ARIMA-GARCH model and artificial neural network model for wind speed forecasting

Wind energy is a noteworthy alternate energy in times of energy crisis. An accurate wind speed forecasting model are essential in determining the suitable location for wind energy harvesting. However, the intermittency and nonlinearity of a wind speed make it difficult to obtain an accurate predic...

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Main Authors: Hussin, Nor Hafizah, Yusof, Fadhilah, Norrulashikin, Siti Mariam
Format: Conference or Workshop Item
Language:English
Published: 2023
Online Access:http://eprints.utem.edu.my/id/eprint/28028/1/A%20comparative%20study%20of%20ARIMA-GARCH%20model%20and%20artificial%20neural%20network%20model%20for%20wind%20speed%20forecasting.pdf
http://eprints.utem.edu.my/id/eprint/28028/
https://pubs.aip.org/aip/acp/article-abstract/2500/1/020009/2875195/A-comparative-study-of-ARIMA-GARCH-model-and?redirectedFrom=fulltext
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spelling my.utem.eprints.280282024-10-17T08:38:14Z http://eprints.utem.edu.my/id/eprint/28028/ A comparative study of ARIMA-GARCH model and artificial neural network model for wind speed forecasting Hussin, Nor Hafizah Yusof, Fadhilah Norrulashikin, Siti Mariam Wind energy is a noteworthy alternate energy in times of energy crisis. An accurate wind speed forecasting model are essential in determining the suitable location for wind energy harvesting. However, the intermittency and nonlinearity of a wind speed make it difficult to obtain an accurate prediction and may cause several operational challenges to grid interfaced the wind energy system. In this study, the time series model and artificial neural network (ANN) model was applied on the daily wind speed data in Senai and Mersing, Johor to forecast future wind speed series. For the time series model, the daily wind speed data was initially been modelled using theARIMA model. However, due to the presence of heteroscedastic effect in the residuals of ARIMA model, GARCH model was introduced to handle the nonlinearity criteria. On the other hand,the Multilayer Perceptron (MLP)model which is in the class of feed-forward ANN was developed with different configurations based on selected hyperparameters. The MLP model configuration with the lowest RMSE value was selected as the best MLP model. A comparison has been made between the ARIMA- GARCH model and theMLPmodel. Results indicate that the MLP model was found to outperform the ARIMA-GARCH model by providing the lowest value of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) in both training and testing data sets. Thus, the artificial neural network can be concluded as the best method to provide a good forecasting model in predicting the daily wind speed data. 2023 Conference or Workshop Item PeerReviewed text en http://eprints.utem.edu.my/id/eprint/28028/1/A%20comparative%20study%20of%20ARIMA-GARCH%20model%20and%20artificial%20neural%20network%20model%20for%20wind%20speed%20forecasting.pdf Hussin, Nor Hafizah and Yusof, Fadhilah and Norrulashikin, Siti Mariam (2023) A comparative study of ARIMA-GARCH model and artificial neural network model for wind speed forecasting. In: 5th ISM International Statistical Conference 2021: Statistics in the Spotlight: Navigating the New Norm, ISM 2021, 17 August 2021 through 19 August 2021, Virtual, Online. https://pubs.aip.org/aip/acp/article-abstract/2500/1/020009/2875195/A-comparative-study-of-ARIMA-GARCH-model-and?redirectedFrom=fulltext
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Wind energy is a noteworthy alternate energy in times of energy crisis. An accurate wind speed forecasting model are essential in determining the suitable location for wind energy harvesting. However, the intermittency and nonlinearity of a wind speed make it difficult to obtain an accurate prediction and may cause several operational challenges to grid interfaced the wind energy system. In this study, the time series model and artificial neural network (ANN) model was applied on the daily wind speed data in Senai and Mersing, Johor to forecast future wind speed series. For the time series model, the daily wind speed data was initially been modelled using theARIMA model. However, due to the presence of heteroscedastic effect in the residuals of ARIMA model, GARCH model was introduced to handle the nonlinearity criteria. On the other hand,the Multilayer Perceptron (MLP)model which is in the class of feed-forward ANN was developed with different configurations based on selected hyperparameters. The MLP model configuration with the lowest RMSE value was selected as the best MLP model. A comparison has been made between the ARIMA- GARCH model and theMLPmodel. Results indicate that the MLP model was found to outperform the ARIMA-GARCH model by providing the lowest value of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) in both training and testing data sets. Thus, the artificial neural network can be concluded as the best method to provide a good forecasting model in predicting the daily wind speed data.
format Conference or Workshop Item
author Hussin, Nor Hafizah
Yusof, Fadhilah
Norrulashikin, Siti Mariam
spellingShingle Hussin, Nor Hafizah
Yusof, Fadhilah
Norrulashikin, Siti Mariam
A comparative study of ARIMA-GARCH model and artificial neural network model for wind speed forecasting
author_facet Hussin, Nor Hafizah
Yusof, Fadhilah
Norrulashikin, Siti Mariam
author_sort Hussin, Nor Hafizah
title A comparative study of ARIMA-GARCH model and artificial neural network model for wind speed forecasting
title_short A comparative study of ARIMA-GARCH model and artificial neural network model for wind speed forecasting
title_full A comparative study of ARIMA-GARCH model and artificial neural network model for wind speed forecasting
title_fullStr A comparative study of ARIMA-GARCH model and artificial neural network model for wind speed forecasting
title_full_unstemmed A comparative study of ARIMA-GARCH model and artificial neural network model for wind speed forecasting
title_sort comparative study of arima-garch model and artificial neural network model for wind speed forecasting
publishDate 2023
url http://eprints.utem.edu.my/id/eprint/28028/1/A%20comparative%20study%20of%20ARIMA-GARCH%20model%20and%20artificial%20neural%20network%20model%20for%20wind%20speed%20forecasting.pdf
http://eprints.utem.edu.my/id/eprint/28028/
https://pubs.aip.org/aip/acp/article-abstract/2500/1/020009/2875195/A-comparative-study-of-ARIMA-GARCH-model-and?redirectedFrom=fulltext
_version_ 1814061450769989632
score 13.211869

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