UPM Institutional Repository

Malaysian day-type load forecasting


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).


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.

Download File

Text (Abstract)
Malaysian day-type load forecasting.pdf

Download (33kB) | Preview

Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Institute for Mathematical Research
DOI Number: https://doi.org/10.1109/ICEENVIRON.2009.5398613
Publisher: IEEE
Keywords: ARMA; ANFIS; RegARMA; MAPE; Load forecasting
Depositing User: Nabilah Mustapa
Date Deposited: 04 Jul 2019 05:01
Last Modified: 04 Jul 2019 05:01
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ICEENVIRON.2009.5398613
URI: http://psasir.upm.edu.my/id/eprint/69586
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item