Forecasting Wind Speed Data by Using a Combination of ARIMA Model with Single Exponential Smoothing

Nur A.B. Kamisan; Muhammad H. Lee; Siti F. Hassan; Siti M. Norrulashikin; Maria E. Nor; Nur H.A. Rahman

doi:10.18280/mmep.080206

Forecasting Wind Speed Data by Using a Combination of ARIMA Model with Single Exponential Smoothing

Nur A.B. Kamisan^*, Muhammad H. Lee, Siti F. Hassan, Siti M. Norrulashikin, Maria E. Nor, Nur H.A. Rahman

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

Wind serves as natural resources as the solution to minimize global warming and has been commonly used to produce electricity. Because of their uncontrollable wind characteristics, wind speed forecasting is considered one of the best challenges in developing power generation. The Autoregressive Integrated Moving Average (ARIMA), Simple Exponential Smoothing (SES) and a hybrid model combination of ARIMA and SES will be used in this study to predict the wind speed. The mean absolute percentage error (MAPE) and the root mean square error (RMSE) are used as measurement of efficiency. The hybrid model provides a positive outcome for predicting wind speed compare to the single model of ARIMA and SES.

Original language	English
Pages (from-to)	207-212
Number of pages	6
Journal	Mathematical Modelling of Engineering Problems
Volume	8
Issue number	2
DOIs	https://doi.org/10.18280/mmep.080206
Publication status	Published - Apr 2021
Externally published	Yes

Keywords

ARIMA model
hybrid time series model
wind energy
wind speed forecasting

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10.18280/mmep.080206

Cite this

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abstract = "Wind serves as natural resources as the solution to minimize global warming and has been commonly used to produce electricity. Because of their uncontrollable wind characteristics, wind speed forecasting is considered one of the best challenges in developing power generation. The Autoregressive Integrated Moving Average (ARIMA), Simple Exponential Smoothing (SES) and a hybrid model combination of ARIMA and SES will be used in this study to predict the wind speed. The mean absolute percentage error (MAPE) and the root mean square error (RMSE) are used as measurement of efficiency. The hybrid model provides a positive outcome for predicting wind speed compare to the single model of ARIMA and SES.",

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author = "Kamisan, {Nur A.B.} and Lee, {Muhammad H.} and Hassan, {Siti F.} and Norrulashikin, {Siti M.} and Nor, {Maria E.} and Rahman, {Nur H.A.}",

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T1 - Forecasting Wind Speed Data by Using a Combination of ARIMA Model with Single Exponential Smoothing

AU - Kamisan, Nur A.B.

AU - Lee, Muhammad H.

AU - Hassan, Siti F.

AU - Norrulashikin, Siti M.

AU - Nor, Maria E.

AU - Rahman, Nur H.A.

PY - 2021/4

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AB - Wind serves as natural resources as the solution to minimize global warming and has been commonly used to produce electricity. Because of their uncontrollable wind characteristics, wind speed forecasting is considered one of the best challenges in developing power generation. The Autoregressive Integrated Moving Average (ARIMA), Simple Exponential Smoothing (SES) and a hybrid model combination of ARIMA and SES will be used in this study to predict the wind speed. The mean absolute percentage error (MAPE) and the root mean square error (RMSE) are used as measurement of efficiency. The hybrid model provides a positive outcome for predicting wind speed compare to the single model of ARIMA and SES.

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Forecasting Wind Speed Data by Using a Combination of ARIMA Model with Single Exponential Smoothing

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