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DOI: 10.14569/IJACSA.2020.0111226
PDF

Industrial Energy Load Profile Forecasting under Enhanced Time of Use Tariff (ETOU) using Artificial Neural Network

Author 1: Mohamad Fani Sulaima
Author 2: Siti Aishah Abu Hanipah
Author 3: Nur Rafiqah Abdul Razif
Author 4: Intan Azmira Wan Abdul Razak
Author 5: Aida Fazliana Abdul Kadir
Author 6: Zul Hasrizal Bohari

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 12, 2020.

  • Abstract and Keywords
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Abstract: The demand response program involves consumers to mitigate peak demand and reducing global CO2 emission. In sustaining this effort, energy provider such as Tenaga Nasional Berhad (TNB) in Peninsular Malaysia has introduced Enhance Time of Use (ETOU) tariff. However, since 2015, small numbers join the ETOU program due to less confidence in managing their energy consumption profile. Thus, this study provides an optimum forecasting load profile model for TOU and ETOU tariffs using Artificial Neural Network (ANN). An industry's average energy profile has been used as a case study, while the forecasting technique has been conducted to find the optimum energy load profile congruently. The load shifting technique has been adopted under ETOU tariff price while integrating to the ANN procedure. A significant comparison in terms of cost reduction between TOU and ETOU electricity tariffs has been made. In contrast, ANN performance results in searching for the best-shifted load profile have been analyzed accordingly. From the proposed method, the total electricity cost saving has been founded to be saved for about 7.9% monthly. It is hoped that this work will benefit the energy authority and consumers in future action, respectively.

Keywords: Time of use; artificial neural network; energy forecasting; load profile

Mohamad Fani Sulaima, Siti Aishah Abu Hanipah, Nur Rafiqah Abdul Razif, Intan Azmira Wan Abdul Razak, Aida Fazliana Abdul Kadir and Zul Hasrizal Bohari, “Industrial Energy Load Profile Forecasting under Enhanced Time of Use Tariff (ETOU) using Artificial Neural Network” International Journal of Advanced Computer Science and Applications(IJACSA), 11(12), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0111226

@article{Sulaima2020,
title = {Industrial Energy Load Profile Forecasting under Enhanced Time of Use Tariff (ETOU) using Artificial Neural Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0111226},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0111226},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {12},
author = {Mohamad Fani Sulaima and Siti Aishah Abu Hanipah and Nur Rafiqah Abdul Razif and Intan Azmira Wan Abdul Razak and Aida Fazliana Abdul Kadir and Zul Hasrizal Bohari}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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