Membership Function Optimization of Fuzzy Inference System Using Cuckoo Search Algorithm for Peak Load Forecasting in National Holiday

Authors

  • Andi Imran Department of Electrical Engineering Education, Faculty of Engineering, Universitas Negeri Makassar
  • Imam Robandi Department of Electrical Engineering, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember
  • Firdaus Firdaus Department of Electrical Engineering Education, Faculty of Engineering, Universitas Negeri Makassar
  • Ruslan Ruslan Department of Electrical Engineering Education, Faculty of Engineering, Universitas Negeri Makassar
  • Muhammad Yusuf Mappeasse Department of Electrical Engineering Education, Faculty of Engineering, Universitas Negeri Makassar
  • Muhammad Ruswandi Djalal Department of Mechanical Engineering, Energy Generation Study Program, State Polytechnic of Ujung Pandang

DOI:

https://doi.org/10.18196/jet.v5i2.12889

Keywords:

Load Forecasting, Fuzzy Inference System, Cuckoo Search, MAPE

Abstract

This study aims to analysis peak load prediction of Indonesian national holidays for Jawa-Bali electricity system. Forecasting applied using the Fuzzy Logic System (FLS) method combined with the Cuckoo Search Algorithm (CSA). CSA is used to determine the optimal membership function in fuzzy logic. Cuckoo search algorithm has a very good performance in terms of optimization. This method is applied for short-term load estimates on holidays/special days on the Jawa-Bali electricity system, Indonesia. The study used data from daily peak loads during Indonesian national holidays in 2014 on the Jawa-Bali electricity system. The data analyzed is the daily peak load documentation data for 4 days before national holidays and during national holidays in 2014. Testing the simulation results, it was found that the Fuzzy Logic System - Cuckoo Search Algorithm (FLS-CSA) method gives good forecasting results, this is evidenced by using the mean absolute percentage error (MAPE). Forecasting results using the Cuckoo Search Algorithm (CSA) optimization method on fuzzy logic membership functions for peak loads on national holidays on the Java-Bali 500kV electrical system give satisfactory results with an average forecasting error of 1.511314562%.

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Published

2021-12-27

How to Cite

Imran, A., Robandi, I., Firdaus, F., Ruslan, R., Mappeasse, M. Y., & Djalal, M. R. (2021). Membership Function Optimization of Fuzzy Inference System Using Cuckoo Search Algorithm for Peak Load Forecasting in National Holiday. Journal of Electrical Technology UMY, 5(2), 74–79. https://doi.org/10.18196/jet.v5i2.12889

Issue

Vol. 5 No. 2 (2021): December

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