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Neural-Fuzzy Approach for Power Load Forecasting Analysis

by J. Kumaran, G. Ravi, R. Rajkumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 69 - Number 16
Year of Publication: 2013
Authors: J. Kumaran, G. Ravi, R. Rajkumar
10.5120/12048-8116

J. Kumaran, G. Ravi, R. Rajkumar . Neural-Fuzzy Approach for Power Load Forecasting Analysis. International Journal of Computer Applications. 69, 16 ( May 2013), 31-35. DOI=10.5120/12048-8116

@article{ 10.5120/12048-8116,
author = { J. Kumaran, G. Ravi, R. Rajkumar },
title = { Neural-Fuzzy Approach for Power Load Forecasting Analysis },
journal = { International Journal of Computer Applications },
issue_date = { May 2013 },
volume = { 69 },
number = { 16 },
month = { May },
year = { 2013 },
issn = { 0975-8887 },
pages = { 31-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume69/number16/12048-8116/ },
doi = { 10.5120/12048-8116 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:30:27.417770+05:30
%A J. Kumaran
%A G. Ravi
%A R. Rajkumar
%T Neural-Fuzzy Approach for Power Load Forecasting Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 69
%N 16
%P 31-35
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents Neuro-Fuzzy approach for forecasting analysis in power load. Forecasting the power load is a difficult task for a country and both positive and negative load forecasting makes a big problem for the country. An approach that Neuro-Fuzzy model is proposed for forecast power load in this paper. The proposed model a fuzzy back propagation network is constructed and then a fuzzy intersection is applied and after that de-fuzzify the result to generate a crisp value by using Radial Basis Function network (RBF). The proposed model improves the accuracy of power load forecasting. The forecasted results obtained by neuro-fuzzy method were compared with the Artificial Neural Network by using Mean Absolute Percentage Error (MAPE) to measure accuracy of the result. The experimental result shows that the neuro-fuzzy implementations have more accuracy.

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Index Terms

Computer Science
Information Sciences

Keywords

Artificial Neural Network Load forecasting Neuro-fuzzy model Radial basis function network

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