An Application of Radial Basis Function Neural Network for Short Term Load Forecasting Solution

An Application of Radial Basis Function Neural Network for Short Term Load Forecasting Solution

This paper proposes an approach to solve short term load forecasting (STLF) problem by using radial basis function neural network (RBFNN). STLF is one of the main issues for power system scheduling since it can help the utility company to manage the generation of power system economically and reliab...

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Main Authors: Nurul Faezah, Othman, Mohd Herwan, Sulaiman, Zuriani, Mustaffa
Format: Article
Language:English
Published: American Scientific Publisher 2018
Subjects:
QA76 Computer software
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://umpir.ump.edu.my/id/eprint/19950/
http://umpir.ump.edu.my/id/eprint/19950/
http://umpir.ump.edu.my/id/eprint/19950/
http://umpir.ump.edu.my/id/eprint/19950/1/39.%20An%20Application%20of%20Radial%20Basis%20Function%20Neural%20Network%20for%20Short%20Term%20Load%20Forecasting%20Solution1.pdf
id ump-19950
recordtype eprints
spelling ump-199502018-11-21T03:29:35Z http://umpir.ump.edu.my/id/eprint/19950/ An Application of Radial Basis Function Neural Network for Short Term Load Forecasting Solution Nurul Faezah, Othman Mohd Herwan, Sulaiman Zuriani, Mustaffa QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering This paper proposes an approach to solve short term load forecasting (STLF) problem by using radial basis function neural network (RBFNN). STLF is one of the main issues for power system scheduling since it can help the utility company to manage the generation of power system economically and reliably. For this purpose, electric load forecast needs to be as accurate as possible to meet the utilities’ need as well as it also can help to select the proper amount of reserve margin which can contribute to the efficiency improvement of the power supply. However, many factors can influence electric load such as day of the week, month of the year, and etc. which makes a complex process for obtaining accurate forecasting. In this paper, input of RBFNN are the real historical load data collected from local utility in Kuantan, Pahang that utilized together with Malaysia Meteorology Department (MetMalaysia) data such as weather, temperature, dew point and humidity and the output is load forecasting for the given day. The performances of RBFNN are analyzed by investigating the combination of input-output of mentioned data that contribute to the best result. Comparison with artificial neural network (ANN) also given in this paper. American Scientific Publisher 2018-11 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/19950/1/39.%20An%20Application%20of%20Radial%20Basis%20Function%20Neural%20Network%20for%20Short%20Term%20Load%20Forecasting%20Solution1.pdf Nurul Faezah, Othman and Mohd Herwan, Sulaiman and Zuriani, Mustaffa (2018) An Application of Radial Basis Function Neural Network for Short Term Load Forecasting Solution. Advanced Science Letters, 24 (10). pp. 7534-7538. ISSN 1936-6612 https://doi.org/10.1166/asl.2018.12973 doi: 10.1166/asl.2018.12973
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA76 Computer software
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle QA76 Computer software
TK Electrical engineering. Electronics Nuclear engineering
Nurul Faezah, Othman
Mohd Herwan, Sulaiman
Zuriani, Mustaffa
An Application of Radial Basis Function Neural Network for Short Term Load Forecasting Solution
description This paper proposes an approach to solve short term load forecasting (STLF) problem by using radial basis function neural network (RBFNN). STLF is one of the main issues for power system scheduling since it can help the utility company to manage the generation of power system economically and reliably. For this purpose, electric load forecast needs to be as accurate as possible to meet the utilities’ need as well as it also can help to select the proper amount of reserve margin which can contribute to the efficiency improvement of the power supply. However, many factors can influence electric load such as day of the week, month of the year, and etc. which makes a complex process for obtaining accurate forecasting. In this paper, input of RBFNN are the real historical load data collected from local utility in Kuantan, Pahang that utilized together with Malaysia Meteorology Department (MetMalaysia) data such as weather, temperature, dew point and humidity and the output is load forecasting for the given day. The performances of RBFNN are analyzed by investigating the combination of input-output of mentioned data that contribute to the best result. Comparison with artificial neural network (ANN) also given in this paper.
format Article
author Nurul Faezah, Othman
Mohd Herwan, Sulaiman
Zuriani, Mustaffa
author_facet Nurul Faezah, Othman
Mohd Herwan, Sulaiman
Zuriani, Mustaffa
author_sort Nurul Faezah, Othman
title An Application of Radial Basis Function Neural Network for Short Term Load Forecasting Solution
title_short An Application of Radial Basis Function Neural Network for Short Term Load Forecasting Solution
title_full An Application of Radial Basis Function Neural Network for Short Term Load Forecasting Solution
title_fullStr An Application of Radial Basis Function Neural Network for Short Term Load Forecasting Solution
title_full_unstemmed An Application of Radial Basis Function Neural Network for Short Term Load Forecasting Solution
title_sort application of radial basis function neural network for short term load forecasting solution
publisher American Scientific Publisher
publishDate 2018
url http://umpir.ump.edu.my/id/eprint/19950/
http://umpir.ump.edu.my/id/eprint/19950/
http://umpir.ump.edu.my/id/eprint/19950/
http://umpir.ump.edu.my/id/eprint/19950/1/39.%20An%20Application%20of%20Radial%20Basis%20Function%20Neural%20Network%20for%20Short%20Term%20Load%20Forecasting%20Solution1.pdf
first_indexed 2023-09-18T22:28:36Z
last_indexed 2023-09-18T22:28:36Z
_version_ 1777416133563908096

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