Malaysian day-type load forecasting

Fadhilah Abd Razak, S. Suriawati, H. H. Amir, Izham Zainal Abidin, S. Mahendran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationICEE 2009 - Proceeding 2009 3rd International Conference on Energy and Environment
Subtitle of host publicationAdvancement Towards Global Sustainability
Pages408-411
Number of pages4
DOIs
Publication statusPublished - 01 Dec 2009
Event2009 3rd International Conference on Energy and Environment: Advancement Towards Global Sustainability, ICEE 2009 - Malacca, Malaysia
Duration: 07 Dec 200908 Dec 2009

Publication series

NameICEE 2009 - Proceeding 2009 3rd International Conference on Energy and Environment: Advancement Towards Global Sustainability

Other

Other2009 3rd International Conference on Energy and Environment: Advancement Towards Global Sustainability, ICEE 2009
CountryMalaysia
CityMalacca
Period07/12/0908/12/09

Fingerprint

Time series analysis
Time series
Statistics
Neural networks

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Environmental Engineering

Cite this

Abd Razak, F., Suriawati, S., Amir, H. H., Zainal Abidin, I., & Mahendran, S. (2009). Malaysian day-type load forecasting. In ICEE 2009 - Proceeding 2009 3rd International Conference on Energy and Environment: Advancement Towards Global Sustainability (pp. 408-411). [5398613] (ICEE 2009 - Proceeding 2009 3rd International Conference on Energy and Environment: Advancement Towards Global Sustainability). https://doi.org/10.1109/ICEENVIRON.2009.5398613
Abd Razak, Fadhilah ; Suriawati, S. ; Amir, H. H. ; Zainal Abidin, Izham ; Mahendran, S. / Malaysian day-type load forecasting. ICEE 2009 - Proceeding 2009 3rd International Conference on Energy and Environment: Advancement Towards Global Sustainability. 2009. pp. 408-411 (ICEE 2009 - Proceeding 2009 3rd International Conference on Energy and Environment: Advancement Towards Global Sustainability).
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Abd Razak, F, Suriawati, S, Amir, HH, Zainal Abidin, I & Mahendran, S 2009, Malaysian day-type load forecasting. in ICEE 2009 - Proceeding 2009 3rd International Conference on Energy and Environment: Advancement Towards Global Sustainability., 5398613, ICEE 2009 - Proceeding 2009 3rd International Conference on Energy and Environment: Advancement Towards Global Sustainability, pp. 408-411, 2009 3rd International Conference on Energy and Environment: Advancement Towards Global Sustainability, ICEE 2009, Malacca, Malaysia, 07/12/09. https://doi.org/10.1109/ICEENVIRON.2009.5398613

Malaysian day-type load forecasting. / Abd Razak, Fadhilah; Suriawati, S.; Amir, H. H.; Zainal Abidin, Izham; Mahendran, S.

ICEE 2009 - Proceeding 2009 3rd International Conference on Energy and Environment: Advancement Towards Global Sustainability. 2009. p. 408-411 5398613 (ICEE 2009 - Proceeding 2009 3rd International Conference on Energy and Environment: Advancement Towards Global Sustainability).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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AB - 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.

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Abd Razak F, Suriawati S, Amir HH, Zainal Abidin I, Mahendran S. Malaysian day-type load forecasting. In ICEE 2009 - Proceeding 2009 3rd International Conference on Energy and Environment: Advancement Towards Global Sustainability. 2009. p. 408-411. 5398613. (ICEE 2009 - Proceeding 2009 3rd International Conference on Energy and Environment: Advancement Towards Global Sustainability). https://doi.org/10.1109/ICEENVIRON.2009.5398613