@inproceedings{8bcaa9557f1341af9c5bc5e73fda1835,
title = "Short-Term Load Forecasting Using Application of Interval Type 2 Fuzzy Logic",
abstract = "This study investigates the development of Interval Type 2 Fuzzy Logic (IT2FL) for short-term load forecasting (STLF). Maintaining grid stability and assuring a steady supply of power is crucial, and hence the need for accurate STLF. The study's main objective is to assess IT2FL's accuracy and dependability by contrasting it with a benchmark Artificial Neural Network (ANN). The findings show that IT2FL performs quite effectively, producing load estimates that nearly match target values. Its ability to retain accuracy in a variety of settings and during periods of high load indicates its robustness. Comparative investigation demonstrates the possibility of IT2FL as a workable STLF solution. Even with more work to be done, IT2FL has the potential to fulfill the ever-evolving demands of the energy industry by providing reliable and accurate load forecasting.",
keywords = "Accuracy, Grid Stability, Interval Type 2 Fuzzy Logic, MATLAB, Precision, Short Term Load Forecasting",
author = "Moyo, {Edmund N.} and Gulshan Sharma and Bokoro, {Pitshou N.} and Vikash Rameshar and Lutendo Muremi",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 32nd Southern African Universities Power Engineering Conference, SAUPEC 2024 ; Conference date: 24-01-2024 Through 25-01-2024",
year = "2024",
doi = "10.1109/SAUPEC60914.2024.10445101",
language = "English",
series = "Proceedings of the 32nd Southern African Universities Power Engineering Conference, SAUPEC 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of the 32nd Southern African Universities Power Engineering Conference, SAUPEC 2024",
address = "United States",
}