Open AccessForecasting Wind Speed Data by Using a Combination of ARIMA Model with Single Exponential Smoothing
Author(s) -
Nur Arina Bazilah Kamisan,
Muhammad Hisyam Lee,
Siti Fatimah Hassan,
Siti Mariam Norrulashikin,
Maria Eleor,
Nur Haizum Abd Rahman
Publication year - 2021
Publication title -
mathematical modelling and engineering problems/mathematical modelling of engineering problems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.26
H-Index - 11
eISSN - 2369-0747
pISSN - 2369-0739
DOI - 10.18280/mmep.080206
Subject(s) - autoregressive integrated moving average , exponential smoothing , wind speed , mean absolute percentage error , mean squared error , wind power , autoregressive model , statistics , moving average , exponential function , econometrics , mathematics , meteorology , computer science , time series , engineering , geography , mathematical analysis , electrical engineering
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.