Article Per Year (5 Year)
p-Index From 2021 - 2026
0.61
P-Index
This Author published in this journals
All Journal TRANSIENT: Jurnal Ilmiah Teknik Elektro Dentin Journal of Indonesian Law (JIL) Journal of International Multidisciplinary Research Journal of Scientific Interdisciplinary
Karnoto Karnoto
Unknown Affiliation
Dentistry Electrical & Electronics Engineering Law, Crime, Criminology & Criminal Justice Other

Published : 5 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search
Journal : Journal of Scientific Interdisciplinary

 1 
Daily Peak Load Forecasting At PT. PLN Uses Anfis(Adaptive Neuro-Fuzzy Inference System) Susatyo Handoko; Karnoto Karnoto
Journal of Scientific Interdisciplinary Vol. 2 No. 3 (2025)
Publisher : PT. Banjarese Pacific Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62504/jis1250

Abstract

The demand for electrical energy continues to rise with the progression of time. This growth must be matched by a reliable and cost-effective supply of electricity, requiring power systems that are both dependable and economical. Since the amount of electricity consumed by users cannot be precisely predicted, balancing generation with consumption necessitates accurate electrical load forecasting. This study focuses on load forecasting using the Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The forecast developed targets daily peak loads, which fall under short-term load forecasting. The data used for this forecasting consists of historical daily peak loads from January 1, 2017, to June 9, 2022. The forecasting process involves parameters such as radius, squash factor, accept ratio, reject ratio, and epoch. The forecast accuracy is evaluated using the Mean Absolute Percentage Error (MAPE) metric. The results are then compared with PLN’s load forecasting, which employs the load coefficient method. The ANFIS-based forecasting achieved a MAPE of 1.879%, using networks Jaringan_24 and Jaringan_25. This MAPE value is slightly lower than PLN’s load forecasting MAPE of 1.917%, indicating better accuracy by the ANFIS method.
Co-Authors Ajub Ajulian ZM Amelia Az Zahra Bambang Winardi Berliana Devianti Putri Ero Wahjuningdiah Muhaimin Muhaimin Siti Zumrotun Khoiroh Susatyo Handoko Winda Kusumawardani