Moving holidays' effects on the Malaysian peak daily load
Malaysia's yearly steady growth in electricity consumption as a result of fast development in various sectors of the Malaysian economy have increased the need to have a more robust, reliable and accurate load forecasting for short -, medium-, or long-term. A reliable method for short term load...
Saved in:
| Main Authors: | , , , |
|---|---|
| Other Authors: | |
| Format: | Conference paper |
| Published: |
2023
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my.uniten.dspace-30576 |
|---|---|
| record_format |
dspace |
| spelling |
my.uniten.dspace-305762023-12-29T15:49:41Z Moving holidays' effects on the Malaysian peak daily load Razak F.Abd. Hashim A.H. Abidin I.Z. Shitan M. 36988285400 24447656300 35606640500 23568523100 ARMA Dynamic regression MAPE SARIMA Transfer function Autocorrelation Regression analysis Time series analysis Transfer functions Akaike's information criterions Appropriate models ARMA Autocorrelation functions Chinese New Year Christmas Decision makers Electricity-consumption Forecasting accuracy Forecasting error Function modelling Load forecasting Malaysia Malaysians MAPE Mean absolute percentage error Partial autocorrelation function Power utility Public holidays SARIMA Short term load forecasting Electric load forecasting Malaysia's yearly steady growth in electricity consumption as a result of fast development in various sectors of the Malaysian economy have increased the need to have a more robust, reliable and accurate load forecasting for short -, medium-, or long-term. A reliable method for short term load forecasting is crucial to any decision maker in a power utility company. Many studies have been made to improve the forecasting accuracy using various methods. The forecasting errors for the holiday seasons are known to be higher than those for weekends. This paper aims to determine which model would be a better model to estimate the holiday effects and therefore give a better forecasting accuracy for the peak daily load in Malaysia. Some of the holiday effects in Malaysia are from Eid ul-Fitr, Christmas, Independence Day and Chinese New Year. The seasonal ARIMA (SARIMA) and Dynamic Regression (DR) or Transfer function modelling are considered. Furthermore, the final selection of the models depends on the Mean Absolute Percentage Error (MAPE) and others such as the sample autocorrelation function (ACF), the sample partial autocorrelation function (PACF) and a bias-corrected version of the Akaike's information criterion (AICC) statistic. The Dynamic Regression (DR) model recorded 2.22% as the lowest MAPE value for the 2004 New Year's Eve and 2.39% for the seven days ahead forecasting. And therefore, DR model is the most appropriate model to be considered for forecasting any public holidays in Malaysia. �2010 IEEE. Final 2023-12-29T07:49:41Z 2023-12-29T07:49:41Z 2010 Conference paper 10.1109/PECON.2010.5697708 2-s2.0-79951793862 https://www.scopus.com/inward/record.uri?eid=2-s2.0-79951793862&doi=10.1109%2fPECON.2010.5697708&partnerID=40&md5=44287780036eda39103bc70d853af05e https://irepository.uniten.edu.my/handle/123456789/30576 5697708 906 910 All Open Access; Green Open Access Scopus |
| institution |
Universiti Tenaga Nasional |
| building |
UNITEN Library |
| collection |
Institutional Repository |
| continent |
Asia |
| country |
Malaysia |
| content_provider |
Universiti Tenaga Nasional |
| content_source |
UNITEN Institutional Repository |
| url_provider |
http://dspace.uniten.edu.my/ |
| topic |
ARMA Dynamic regression MAPE SARIMA Transfer function Autocorrelation Regression analysis Time series analysis Transfer functions Akaike's information criterions Appropriate models ARMA Autocorrelation functions Chinese New Year Christmas Decision makers Electricity-consumption Forecasting accuracy Forecasting error Function modelling Load forecasting Malaysia Malaysians MAPE Mean absolute percentage error Partial autocorrelation function Power utility Public holidays SARIMA Short term load forecasting Electric load forecasting |
| spellingShingle |
ARMA Dynamic regression MAPE SARIMA Transfer function Autocorrelation Regression analysis Time series analysis Transfer functions Akaike's information criterions Appropriate models ARMA Autocorrelation functions Chinese New Year Christmas Decision makers Electricity-consumption Forecasting accuracy Forecasting error Function modelling Load forecasting Malaysia Malaysians MAPE Mean absolute percentage error Partial autocorrelation function Power utility Public holidays SARIMA Short term load forecasting Electric load forecasting Razak F.Abd. Hashim A.H. Abidin I.Z. Shitan M. Moving holidays' effects on the Malaysian peak daily load |
| description |
Malaysia's yearly steady growth in electricity consumption as a result of fast development in various sectors of the Malaysian economy have increased the need to have a more robust, reliable and accurate load forecasting for short -, medium-, or long-term. A reliable method for short term load forecasting is crucial to any decision maker in a power utility company. Many studies have been made to improve the forecasting accuracy using various methods. The forecasting errors for the holiday seasons are known to be higher than those for weekends. This paper aims to determine which model would be a better model to estimate the holiday effects and therefore give a better forecasting accuracy for the peak daily load in Malaysia. Some of the holiday effects in Malaysia are from Eid ul-Fitr, Christmas, Independence Day and Chinese New Year. The seasonal ARIMA (SARIMA) and Dynamic Regression (DR) or Transfer function modelling are considered. Furthermore, the final selection of the models depends on the Mean Absolute Percentage Error (MAPE) and others such as the sample autocorrelation function (ACF), the sample partial autocorrelation function (PACF) and a bias-corrected version of the Akaike's information criterion (AICC) statistic. The Dynamic Regression (DR) model recorded 2.22% as the lowest MAPE value for the 2004 New Year's Eve and 2.39% for the seven days ahead forecasting. And therefore, DR model is the most appropriate model to be considered for forecasting any public holidays in Malaysia. �2010 IEEE. |
| author2 |
36988285400 |
| author_facet |
36988285400 Razak F.Abd. Hashim A.H. Abidin I.Z. Shitan M. |
| format |
Conference paper |
| author |
Razak F.Abd. Hashim A.H. Abidin I.Z. Shitan M. |
| author_sort |
Razak F.Abd. |
| title |
Moving holidays' effects on the Malaysian peak daily load |
| title_short |
Moving holidays' effects on the Malaysian peak daily load |
| title_full |
Moving holidays' effects on the Malaysian peak daily load |
| title_fullStr |
Moving holidays' effects on the Malaysian peak daily load |
| title_full_unstemmed |
Moving holidays' effects on the Malaysian peak daily load |
| title_sort |
moving holidays' effects on the malaysian peak daily load |
| publishDate |
2023 |
| _version_ |
1806426401781317632 |
| score |
13.211869 |