A modeling approach for short-term load forcasting using fuzzy logic type-2 in sulselrabar system

International Journal of artificial intelligence research

View Publication Info
 
 
Field Value
 
Title A modeling approach for short-term load forcasting using fuzzy logic type-2 in sulselrabar system
 
Creator Djalal, Muhammad Ruswandi
 
Subject
Load Forecasting; Fuzzy Logic Type-2; Mean Average Percentage; Error; Fuzzy Rules;
 
Description This research proposed a modeling approach for 24-hour short-term load forcasting based on fuzzy logic type-2. In this research we get an approach in designing load forecasting model, where previously still using conventional fuzzy logic. Implementation of load forecasting in this research is done on electrical system 150 kV Sulselrabar. Sulselrabar electrical system in its development has grown rapidly, therefore needed a study that to improve system performance, one of which is the study of short-term load forcasting. As the input data used load data from 2010 to 2016 on the same day that is January 8th. To see the accuracy of the results, two approaches are performed, ie fuzzy logic type-1 modeled using Simulink and fuzzy logic type-2 modeled using m-file Matlab. From the analysis results obtained, Mean Percentage Error (MAPE) is the smallest by using Fuzzy Logic Type-2 method, compared with Fuzzy Logic Type-1 method.. Where, MAPE for fuzzy logic type-1 method is 2.133371219%, and by using fuzzy logic type-2 method, MAPE is 1.729778866%.
 
Publisher STMIK Dharma Wacana
 
Contributor
 
Date 2018-12-07
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Identifier http://ijair.id/index.php/ijair/article/view/68
10.29099/ijair.v3i1.68
 
Source International Journal of Artificial Intelligence Research; Vol 3, No 1 (2019): In Pres
2579-7298
10.29099/ijair.v3i1
 
Language en
 
Rights Copyright (c) 2018 International Journal of Artificial Intelligence Research
https://creativecommons.org/licenses/by-sa/4.0
 

Contact Us

The PKP Index is an initiative of the Public Knowledge Project.

For PKP Publishing Services please use the PKP|PS contact form.

For support with PKP software we encourage users to consult our wiki for documentation and search our support forums.

For any other correspondence feel free to contact us using the PKP contact form.

Find Us

Twitter

Copyright © 2015-2018 Simon Fraser University Library