Optimization of neural network architecture using genetic algorithm for load forecasting

In this paper, a computational intelligent technique genetic algorithm (GA) is implemented for the optimization of artificial neural network (ANN) architecture. The network structures are normally selected on the basis of the developer's prior knowledge or hit and trial approach is used for thi...

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Main Authors: Islam, B.U., Baharudin, Z., Raza, M.Q., Nallagownden, P.
Format: Conference or Workshop Item
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.32092 /
Published: IEEE Computer Society 2014
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906351337&doi=10.1109%2fICIAS.2014.6869528&partnerID=40&md5=6a8e6d25720013a075c72cc181885672
http://eprints.utp.edu.my/32092/
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spelling utp-eprints.320922022-03-29T04:34:27Z Optimization of neural network architecture using genetic algorithm for load forecasting Islam, B.U. Baharudin, Z. Raza, M.Q. Nallagownden, P. In this paper, a computational intelligent technique genetic algorithm (GA) is implemented for the optimization of artificial neural network (ANN) architecture. The network structures are normally selected on the basis of the developer's prior knowledge or hit and trial approach is used for this purpose. ANN based models are frequently used for the prediction of future load, because of their learning and mapping ability to address the non linear nature of electrical load. The proposed technique provides a pathway to determine the best ANN architecture, prior to the training and learning process of neural network. Multi-objective algorithm is proposed in this research which optimizes the ANN architecture that leads to enhancement in load forecast accuracy and reduction in the computational cost. The results of several experiment conducted during this work, exhibits that forecast accuracy is considerably enhanced by using an optimized and reduced ANN structure. © 2014 IEEE. IEEE Computer Society 2014 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906351337&doi=10.1109%2fICIAS.2014.6869528&partnerID=40&md5=6a8e6d25720013a075c72cc181885672 Islam, B.U. and Baharudin, Z. and Raza, M.Q. and Nallagownden, P. (2014) Optimization of neural network architecture using genetic algorithm for load forecasting. In: UNSPECIFIED. http://eprints.utp.edu.my/32092/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description In this paper, a computational intelligent technique genetic algorithm (GA) is implemented for the optimization of artificial neural network (ANN) architecture. The network structures are normally selected on the basis of the developer's prior knowledge or hit and trial approach is used for this purpose. ANN based models are frequently used for the prediction of future load, because of their learning and mapping ability to address the non linear nature of electrical load. The proposed technique provides a pathway to determine the best ANN architecture, prior to the training and learning process of neural network. Multi-objective algorithm is proposed in this research which optimizes the ANN architecture that leads to enhancement in load forecast accuracy and reduction in the computational cost. The results of several experiment conducted during this work, exhibits that forecast accuracy is considerably enhanced by using an optimized and reduced ANN structure. © 2014 IEEE.
format Conference or Workshop Item
author Islam, B.U.
Baharudin, Z.
Raza, M.Q.
Nallagownden, P.
spellingShingle Islam, B.U.
Baharudin, Z.
Raza, M.Q.
Nallagownden, P.
Optimization of neural network architecture using genetic algorithm for load forecasting
author_sort Islam, B.U.
title Optimization of neural network architecture using genetic algorithm for load forecasting
title_short Optimization of neural network architecture using genetic algorithm for load forecasting
title_full Optimization of neural network architecture using genetic algorithm for load forecasting
title_fullStr Optimization of neural network architecture using genetic algorithm for load forecasting
title_full_unstemmed Optimization of neural network architecture using genetic algorithm for load forecasting
title_sort optimization of neural network architecture using genetic algorithm for load forecasting
publisher IEEE Computer Society
publishDate 2014
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906351337&doi=10.1109%2fICIAS.2014.6869528&partnerID=40&md5=6a8e6d25720013a075c72cc181885672
http://eprints.utp.edu.my/32092/
_version_ 1741197685414690816
score 11.62408

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