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Forecasting short term load demand using double seasol arima model

Load demand is a time series data and it is one of the major input factors in economic development especially in a developing COlUltry such as Malaysia. Forecasting load demand with high accuracy is hoped to help the cOlmtry, especially the Malaysian electricity utility company to generate an approp...

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Main Authors: Mohamed, Norizan, Ahmad, Maizah Hura, Suhartono, Suhartono
Format: Article
Published: International Digital Organization for Scientific Information (I D O S I) 2011
Subjects:
CB History of civilization
Online Access:http://eprints.utm.my/id/eprint/44931/
http://www.idosi.org/wasj/wasj13(1)2011.htm
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spelling my.utm.449312017-01-31T06:53:29Z http://eprints.utm.my/id/eprint/44931/ Forecasting short term load demand using double seasol arima model Mohamed, Norizan Ahmad, Maizah Hura Suhartono, Suhartono CB History of civilization Load demand is a time series data and it is one of the major input factors in economic development especially in a developing COlUltry such as Malaysia. Forecasting load demand with high accuracy is hoped to help the cOlmtry, especially the Malaysian electricity utility company to generate an appropriate load of required power supply which can avoid energy wasting and prevent system failure. A half hourly load demand of Malaysia for one year, from September 01, 2005 to August 31, 2006 measured in Megawatt (MW) is used for this study with the mean absolute percentage error (1.1APE) as a forecasting accuracy. Statistical Analysis System, SAS package was used to analyze the data. The best model was selected based on the mean absolute percentage error (1.1APE) and the theoretical autocorrelation fWlction (ACF) was presented to prove that the best model satisfies the load data. The ARIMA(O,I,1 XO, 1,1 )48(0,1,1 )336 with in-sample MAPE of 0.9906% was selected as the best model for this study. Comparing the forecasting performances by using k-step ahead outsample forecasts and one-step ahead forecasts, we fOWld that the 1.1APE for the one-step ahead out-sample forecasts from any horizon were all less than 1%. In other words it can be concluded that the one-step ahead out-sample forecasts was more accurate. There was a reduction in 1.1APE percentages for all lead time horizons considered, ranging between 89% to 96%. Furthermore a time series plot of out-samples of actual load data, kstep ahead and one-step ahead out-sample forecasts showed that one-step ahead out-sample forecasts followed the actual load data more closely than k-step ahead out-sample forecasts. Therefore we propose that the theoretical ACF must be considered in proving the best model satisfies load demand and that the one-step ahead out-sample forecasts must also be considered in forecasting load, especially in Malaysia load data. International Digital Organization for Scientific Information (I D O S I) 2011 Article PeerReviewed Mohamed, Norizan and Ahmad, Maizah Hura and Suhartono, Suhartono (2011) Forecasting short term load demand using double seasol arima model. World Applied Sciences Journal, 13 (1). pp. 27-35. ISSN 1818-4952 http://www.idosi.org/wasj/wasj13(1)2011.htm
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic CB History of civilization
spellingShingle CB History of civilization
Mohamed, Norizan
Ahmad, Maizah Hura
Suhartono, Suhartono
Forecasting short term load demand using double seasol arima model
description Load demand is a time series data and it is one of the major input factors in economic development especially in a developing COlUltry such as Malaysia. Forecasting load demand with high accuracy is hoped to help the cOlmtry, especially the Malaysian electricity utility company to generate an appropriate load of required power supply which can avoid energy wasting and prevent system failure. A half hourly load demand of Malaysia for one year, from September 01, 2005 to August 31, 2006 measured in Megawatt (MW) is used for this study with the mean absolute percentage error (1.1APE) as a forecasting accuracy. Statistical Analysis System, SAS package was used to analyze the data. The best model was selected based on the mean absolute percentage error (1.1APE) and the theoretical autocorrelation fWlction (ACF) was presented to prove that the best model satisfies the load data. The ARIMA(O,I,1 XO, 1,1 )48(0,1,1 )336 with in-sample MAPE of 0.9906% was selected as the best model for this study. Comparing the forecasting performances by using k-step ahead outsample forecasts and one-step ahead forecasts, we fOWld that the 1.1APE for the one-step ahead out-sample forecasts from any horizon were all less than 1%. In other words it can be concluded that the one-step ahead out-sample forecasts was more accurate. There was a reduction in 1.1APE percentages for all lead time horizons considered, ranging between 89% to 96%. Furthermore a time series plot of out-samples of actual load data, kstep ahead and one-step ahead out-sample forecasts showed that one-step ahead out-sample forecasts followed the actual load data more closely than k-step ahead out-sample forecasts. Therefore we propose that the theoretical ACF must be considered in proving the best model satisfies load demand and that the one-step ahead out-sample forecasts must also be considered in forecasting load, especially in Malaysia load data.
format Article
author Mohamed, Norizan
Ahmad, Maizah Hura
Suhartono, Suhartono
author_facet Mohamed, Norizan
Ahmad, Maizah Hura
Suhartono, Suhartono
author_sort Mohamed, Norizan
title Forecasting short term load demand using double seasol arima model
title_short Forecasting short term load demand using double seasol arima model
title_full Forecasting short term load demand using double seasol arima model
title_fullStr Forecasting short term load demand using double seasol arima model
title_full_unstemmed Forecasting short term load demand using double seasol arima model
title_sort forecasting short term load demand using double seasol arima model
publisher International Digital Organization for Scientific Information (I D O S I)
publishDate 2011
url http://eprints.utm.my/id/eprint/44931/
http://www.idosi.org/wasj/wasj13(1)2011.htm
_version_ 1643651590829113344
score 13.211869

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