Cover Image

Malaysian day-type load forecasting

Time series analysis has been applied intensively and sophisticatedly to model and forecast many problems in the biological, physical and environmental phenomena of interest. This fact accounts for the basic engineering problem in forecasting the daily peak system load to use time series analysis. A...

Full description

Saved in:
Bibliographic Details
Main Authors: Abd. Razak, Fadhilah, S., Suriawati, Hashim, Amir Hisham, Zainal Abidin, Izham, Shitan, Mahendran
Format: Conference or Workshop Item
Language:English
Published: IEEE 2009
Online Access:http://psasir.upm.edu.my/id/eprint/69586/1/Malaysian%20day-type%20load%20forecasting.pdf
http://psasir.upm.edu.my/id/eprint/69586/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.upm.eprints.69586
record_format eprints
spelling my.upm.eprints.695862019-07-04T05:01:47Z http://psasir.upm.edu.my/id/eprint/69586/ Malaysian day-type load forecasting Abd. Razak, Fadhilah S., Suriawati Hashim, Amir Hisham Zainal Abidin, Izham Shitan, Mahendran Time series analysis has been applied intensively and sophisticatedly to model and forecast many problems in the biological, physical and environmental phenomena of interest. This fact accounts for the basic engineering problem in forecasting the daily peak system load to use time series analysis. ARMA and REgARMA models are among the times series models considered. ANFIS, a hybrid model from neural network is also discussed as for comparison purposes. The main interest of the forecasts consists of three days up to five days ahead predictions for daily data. The pure autoregressive model with an order 2, or AR (2) with a MAPE value of 1.27% is found to be an appropriate model for forecasting the Malaysian peak daily load for the 3 days ahead prediction. ANFIS model gives a better MAPE value when weekends' data were excluded. Regression models with ARMA errors are found to be good models for forecasting different day types. The selection of these models is depended on the smallest value of AIC statistic and the forecasting accuracy criteria. IEEE 2009 Conference or Workshop Item PeerReviewed text en http://psasir.upm.edu.my/id/eprint/69586/1/Malaysian%20day-type%20load%20forecasting.pdf Abd. Razak, Fadhilah and S., Suriawati and Hashim, Amir Hisham and Zainal Abidin, Izham and Shitan, Mahendran (2009) Malaysian day-type load forecasting. In: 3rd International Conference on Energy and Environment (ICEE 2009), 7-8 Dec. 2009, Malacca, Malaysia. (pp. 408-411). 10.1109/ICEENVIRON.2009.5398613
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Time series analysis has been applied intensively and sophisticatedly to model and forecast many problems in the biological, physical and environmental phenomena of interest. This fact accounts for the basic engineering problem in forecasting the daily peak system load to use time series analysis. ARMA and REgARMA models are among the times series models considered. ANFIS, a hybrid model from neural network is also discussed as for comparison purposes. The main interest of the forecasts consists of three days up to five days ahead predictions for daily data. The pure autoregressive model with an order 2, or AR (2) with a MAPE value of 1.27% is found to be an appropriate model for forecasting the Malaysian peak daily load for the 3 days ahead prediction. ANFIS model gives a better MAPE value when weekends' data were excluded. Regression models with ARMA errors are found to be good models for forecasting different day types. The selection of these models is depended on the smallest value of AIC statistic and the forecasting accuracy criteria.
format Conference or Workshop Item
author Abd. Razak, Fadhilah
S., Suriawati
Hashim, Amir Hisham
Zainal Abidin, Izham
Shitan, Mahendran
spellingShingle Abd. Razak, Fadhilah
S., Suriawati
Hashim, Amir Hisham
Zainal Abidin, Izham
Shitan, Mahendran
Malaysian day-type load forecasting
author_facet Abd. Razak, Fadhilah
S., Suriawati
Hashim, Amir Hisham
Zainal Abidin, Izham
Shitan, Mahendran
author_sort Abd. Razak, Fadhilah
title Malaysian day-type load forecasting
title_short Malaysian day-type load forecasting
title_full Malaysian day-type load forecasting
title_fullStr Malaysian day-type load forecasting
title_full_unstemmed Malaysian day-type load forecasting
title_sort malaysian day-type load forecasting
publisher IEEE
publishDate 2009
url http://psasir.upm.edu.my/id/eprint/69586/1/Malaysian%20day-type%20load%20forecasting.pdf
http://psasir.upm.edu.my/id/eprint/69586/
_version_ 1643839530592108544
score 13.211869

Similar Items