Very Short-Term Load Forecasting Of Peak Load Time Using Fuzzy Type-2 And Big Bang Big Crunch (Bbbc) Algorithm

Jamaaluddin Jamaaluddin*, Imam Robandi, Izza Anshory, Ahmad Fudholi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Very short-term energy forecasting is done on a day before the day of operation. Forecasting of short-term load is done every 30 minutes of forecasting hours. Very short-term load forecasting is carried out continuing forecasting short-term load with daily intervals, load on a given day or weekly load to be more detailed with very short-term load forecasting. This weight forecasting is done to improve operational effectiveness and efficiency. Forecasting short -term loads have been done using artificial intelligence, nowadays artificial intelligence is attempted to predict a very short term load. In this study, investigated the use of the Fuzzy type-2 (FT-2) and big bang big crunch (BBBC) algorithm for the very short-term load forecasting. Results shows that with the use of FT-2-BBBC, it will get a small error because it uses low computation cost and high convergent speed. In addition, FT-2 also optimizes the foot of uncertainty of the Fuzzy type-1. The results are in the use of FT-2-BBBC hence the very short term load forecasting error value of 0.7278%. This suggests that FT-2-BBBC can be used to perform electrical load forecasting and other forecasting.

Original languageEnglish
Pages (from-to)854-861
Number of pages8
JournalARPN Journal of Engineering and Applied Sciences
Issue number7
Publication statusPublished - Apr 2020


  • electrical
  • energy
  • load.
  • power


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