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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...

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Main Authors:	Fadhilah A.R., Suriawati S., Amir H.H., Izham Z.A., Mahendran S.
Other Authors:	36988285400
Format:	Conference paper
Published:	2023
Subjects:	ANFIS ARMA Load forecasting MAPE RegARMA Fuzzy systems Neural networks Regression analysis Sustainable development Time series Time series analysis ANFIS model Appropriate models Auto regressive models Engineering problems Environmental phenomena Forecasting accuracy Hybrid model Malaysians Order 2 Regression model System loads Times series Electric load forecasting
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id	my.uniten.dspace-30822
record_format	dspace
spelling	my.uniten.dspace-308222023-12-29T15:53:59Z Malaysian day-type load forecasting Fadhilah A.R. Suriawati S. Amir H.H. Izham Z.A. Mahendran S. 36988285400 57224346174 24447656300 35606640500 23568523100 ANFIS ARMA Load forecasting MAPE RegARMA Fuzzy systems Neural networks Regression analysis Sustainable development Time series Time series analysis ANFIS model Appropriate models Auto regressive models Engineering problems Environmental phenomena Forecasting accuracy Hybrid model Load forecasting Malaysians Order 2 Regression model System loads Times series Electric 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. 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. �2009 IEEE. Final 2023-12-29T07:53:59Z 2023-12-29T07:53:59Z 2009 Conference paper 10.1109/ICEENVIRON.2009.5398613 2-s2.0-77949575977 https://www.scopus.com/inward/record.uri?eid=2-s2.0-77949575977&doi=10.1109%2fICEENVIRON.2009.5398613&partnerID=40&md5=3672ad8bb5a16deb0a506acc0592ddd1 https://irepository.uniten.edu.my/handle/123456789/30822 5398613 408 411 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	ANFIS ARMA Load forecasting MAPE RegARMA Fuzzy systems Neural networks Regression analysis Sustainable development Time series Time series analysis ANFIS model Appropriate models Auto regressive models Engineering problems Environmental phenomena Forecasting accuracy Hybrid model Load forecasting Malaysians Order 2 Regression model System loads Times series Electric load forecasting
spellingShingle	ANFIS ARMA Load forecasting MAPE RegARMA Fuzzy systems Neural networks Regression analysis Sustainable development Time series Time series analysis ANFIS model Appropriate models Auto regressive models Engineering problems Environmental phenomena Forecasting accuracy Hybrid model Load forecasting Malaysians Order 2 Regression model System loads Times series Electric load forecasting Fadhilah A.R. Suriawati S. Amir H.H. Izham Z.A. Mahendran S. Malaysian day-type load forecasting
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. �2009 IEEE.
author2	36988285400
author_facet	36988285400 Fadhilah A.R. Suriawati S. Amir H.H. Izham Z.A. Mahendran S.
format	Conference paper
author	Fadhilah A.R. Suriawati S. Amir H.H. Izham Z.A. Mahendran S.
author_sort	Fadhilah A.R.
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
publishDate	2023
_version_	1806426547705348096
score	13.222552

Malaysian day-type load forecasting

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