Handling volatility and nonlinearity in wind speed data: A comparative analysis between ARIMA-GARCH and ARIMA-MLP
One of the notable features of wind speed is its volatility and nonlinearity. Thorough assessment on the presence of these features is crucial to obtain a wind speed forecasting model with higher accuracy. In this study, the conventional time series linear model; ARIMA model was applied to assess th...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | en |
| Published: |
Semarak Ilmu Publishing
2024
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| Online Access: | http://eprints.utem.edu.my/id/eprint/28246/2/026860309202416229.pdf http://eprints.utem.edu.my/id/eprint/28246/ https://semarakilmu.com.my/journals/index.php/appl_mech/article/view/7077 https://doi.org/10.37934/aram.121.1.4457 |
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| Summary: | One of the notable features of wind speed is its volatility and nonlinearity. Thorough assessment on the presence of these features is crucial to obtain a wind speed forecasting model with higher accuracy. In this study, the conventional time series linear model; ARIMA model was applied to assess the internal structure of the wind speed daily data in two stations in Johor; Senai and Mersing. Due to the existence of
some nonlinearity features in the residuals part of ARIMA modelling, two nonlinear models were introduced to capture the internal structure of the residual data. Both
conventional time series models; GARCH, and machine learning model; MLP was applied to model the residuals of ARIMA model. The out-sample performance in
forecasting accuracy was compared between the ARIMA-GARCH model and the ARIMAMLP model. The findings proves that MLP model has outperformed GARCH model in
capturing the dynamics in the residual data by providing the lowest error measurements. Thus, the machine learning approaches has proven its superiority against the conventional time series nonlinear model in handling the nonlinearity in the daily wind speed series for wind speed forecasting. |
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