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All Journal International Journal of Advances in Applied Sciences PROtek : Jurnal Ilmiah Teknik Elektro Patria Artha Technological Journal Patria Artha Journal of Community AMMA : Jurnal Pengabdian Masyarakat Jurnal Testing dan Implementasi Sistem Informasi
Syarif, Irwan
Fakultas Teknik Dan Informatika Universitas Patria Artha
Humanities Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Nursing Physics Public Health Social Sciences

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Novel bat algorithm for short-term peak load forecasting in Sulselrabar electricity system Muhammad Rais; Rosihan Aminudin; Asnefi Asnefi; Andi Nur Putri; Irwan Syarif; Muhammad Ruswandi Djalal
International Journal of Advances in Applied Sciences Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i3.pp1334-1346

Abstract

This study addresses short-term load forecasting (STLF) in the South, Southeast, and West Sulawesi (Sulselrabar) power system, Indonesia, using interval type-2 fuzzy logic (IT2FL) optimized by the proposed novel bat algorithm (NBA). The NBA is employed to optimize the footprint of uncertainty (FOU) of the fuzzy membership functions for both antecedent (X and Y) and consequent (Z) variables. The forecasting model utilizes daily peak load data from the previous four days (d−4 to d−1) to predict the peak load of the forecast day (d). To evaluate the effectiveness of the proposed approach, NBA is benchmarked against particle swarm optimization (PSO), firefly algorithm (FA), and cuckoo search algorithm (CSA). The results demonstrate that the proposed IT2FL–NBA model provides the highest forecasting accuracy among the evaluated methods, achieving a mean absolute percentage error (MAPE) of 1.5650%. In comparison, IT2FL–PSO, IT2FL–FA, and IT2FL–CSA achieve MAPEs of 1.6353%, 1.6451%, and 1.6353%, respectively. For type-1 fuzzy logic (IT1FL), the NBA, PSO, FA, and CSA optimization methods produce MAPEs of 1.6668%, 1.6842%, 1.6805%, and 1.6768%, respectively. These findings demonstrate that optimizing the FOU of IT2FL using the proposed NBA significantly improves forecasting accuracy and provides a robust and reliable approach for STLF in power systems.
Co-Authors Akhsan Hamka Asnefi Asnefi Asnefi Dayanti Dayanti Essy Malays Sari Sakti Fina Diana Fina Diana Hikmatul Amri Husni Mubarak Ilham Ilham Indrabulan, Tantri Jumiati Usman Mochammad Apriyadi Hadi Sirad Muh Rais Muh Zainal Muh. Rais Muhammad Arham Muhammad Facheruddin BJ Muhammad Rais Muhammad Ruswandi Djalal Murbayani Murbayani Nurmiati Irvan Nurmiati Irvan Putri, Andi Nur Ridwan Ridwan Rosihan Aminuddin Rosihan Aminudin Sirad, Mochammad Apriyadi Hadi Sri Nurindasari Syamsumarlin Taha Tahir, Tamus Bin Wahyudi, Erik