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Forecasting of Electricity Demand in Malaysia with Seasonal Highly Volatile Characteristics using SARIMA – GARCH Model

Developing an accurate forecasting model for electricity demand plays a vital role in maximising the efficiency of the planning process in the power generation industries. The time series data of electricity demand in Malaysia is highly volatile with seasonal characteristics. This study aims to eval...

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Main Authors: Syarranur, Zaim, Wan Nur Syahidah, Wan Yusoff, Nurul Najihah, Mohamad, Noor Fadhilah, Ahmad Radi, Siti Roslindar, Yaziz
Format: Article
Language:English
Published: Penerbit UTM 2023
Subjects:
QA Mathematics
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/40160/1/Forecasting%20of%20Electricity%20Demand%20in%20Malaysia%20with%20Seasonal%20Highly%20Volatile.pdf
http://umpir.ump.edu.my/id/eprint/40160/
https://doi.org/10.11113/matematika.v39.n3.1512
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spelling my.ump.umpir.401602024-01-24T00:25:44Z http://umpir.ump.edu.my/id/eprint/40160/ Forecasting of Electricity Demand in Malaysia with Seasonal Highly Volatile Characteristics using SARIMA – GARCH Model Syarranur, Zaim Wan Nur Syahidah, Wan Yusoff Nurul Najihah, Mohamad Noor Fadhilah, Ahmad Radi Siti Roslindar, Yaziz QA Mathematics QA75 Electronic computers. Computer science Developing an accurate forecasting model for electricity demand plays a vital role in maximising the efficiency of the planning process in the power generation industries. The time series data of electricity demand in Malaysia is highly volatile with seasonal characteristics. This study aims to evaluate the forecasting performance of the seasonal autoregressive integrated moving average (SARIMA) model with GARCH for weekly maximum electricity demand. The weekly maximum electricity demand data (in megawatt, MW) from 2005 to 2016 has been used for this study. The results show that SARIMA(1, 1, 0)(0, 1, 0)52−GARCH(1, 2) with generalized error distribution (GED) is the most appropriate model for forecasting electricity demand due to its parsimonious characteristic with low values of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) which are 644.1828, 523.8380 and 3.13%, respectively. The MAPE value of the proposed model which is less than 5% indicates that the SARIMA − GARCH model is relatively good in forecasting electricity demand for the case of Malaysia data. In conclusion, the proposed model of SARIMA with GARCH has great potential and provides a promising performance in forecasting electricity demand with seasonal highly volatile characteristics. Penerbit UTM 2023 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/40160/1/Forecasting%20of%20Electricity%20Demand%20in%20Malaysia%20with%20Seasonal%20Highly%20Volatile.pdf Syarranur, Zaim and Wan Nur Syahidah, Wan Yusoff and Nurul Najihah, Mohamad and Noor Fadhilah, Ahmad Radi and Siti Roslindar, Yaziz (2023) Forecasting of Electricity Demand in Malaysia with Seasonal Highly Volatile Characteristics using SARIMA – GARCH Model. MATEMATIKA, 39 (3). pp. 293-313. ISSN 0127-8274. (Published) https://doi.org/10.11113/matematika.v39.n3.1512 10.11113/matematika.v39.n3.1512
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA Mathematics
QA75 Electronic computers. Computer science
spellingShingle QA Mathematics
QA75 Electronic computers. Computer science
Syarranur, Zaim
Wan Nur Syahidah, Wan Yusoff
Nurul Najihah, Mohamad
Noor Fadhilah, Ahmad Radi
Siti Roslindar, Yaziz
Forecasting of Electricity Demand in Malaysia with Seasonal Highly Volatile Characteristics using SARIMA – GARCH Model
description Developing an accurate forecasting model for electricity demand plays a vital role in maximising the efficiency of the planning process in the power generation industries. The time series data of electricity demand in Malaysia is highly volatile with seasonal characteristics. This study aims to evaluate the forecasting performance of the seasonal autoregressive integrated moving average (SARIMA) model with GARCH for weekly maximum electricity demand. The weekly maximum electricity demand data (in megawatt, MW) from 2005 to 2016 has been used for this study. The results show that SARIMA(1, 1, 0)(0, 1, 0)52−GARCH(1, 2) with generalized error distribution (GED) is the most appropriate model for forecasting electricity demand due to its parsimonious characteristic with low values of root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) which are 644.1828, 523.8380 and 3.13%, respectively. The MAPE value of the proposed model which is less than 5% indicates that the SARIMA − GARCH model is relatively good in forecasting electricity demand for the case of Malaysia data. In conclusion, the proposed model of SARIMA with GARCH has great potential and provides a promising performance in forecasting electricity demand with seasonal highly volatile characteristics.
format Article
author Syarranur, Zaim
Wan Nur Syahidah, Wan Yusoff
Nurul Najihah, Mohamad
Noor Fadhilah, Ahmad Radi
Siti Roslindar, Yaziz
author_facet Syarranur, Zaim
Wan Nur Syahidah, Wan Yusoff
Nurul Najihah, Mohamad
Noor Fadhilah, Ahmad Radi
Siti Roslindar, Yaziz
author_sort Syarranur, Zaim
title Forecasting of Electricity Demand in Malaysia with Seasonal Highly Volatile Characteristics using SARIMA – GARCH Model
title_short Forecasting of Electricity Demand in Malaysia with Seasonal Highly Volatile Characteristics using SARIMA – GARCH Model
title_full Forecasting of Electricity Demand in Malaysia with Seasonal Highly Volatile Characteristics using SARIMA – GARCH Model
title_fullStr Forecasting of Electricity Demand in Malaysia with Seasonal Highly Volatile Characteristics using SARIMA – GARCH Model
title_full_unstemmed Forecasting of Electricity Demand in Malaysia with Seasonal Highly Volatile Characteristics using SARIMA – GARCH Model
title_sort forecasting of electricity demand in malaysia with seasonal highly volatile characteristics using sarima – garch model
publisher Penerbit UTM
publishDate 2023
url http://umpir.ump.edu.my/id/eprint/40160/1/Forecasting%20of%20Electricity%20Demand%20in%20Malaysia%20with%20Seasonal%20Highly%20Volatile.pdf
http://umpir.ump.edu.my/id/eprint/40160/
https://doi.org/10.11113/matematika.v39.n3.1512
_version_ 1822924103103807488
score 13.232432

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