relation: https://khub.utp.edu.my/scholars/4899/
title: Optimization of neural network architecture using genetic algorithm for load forecasting
creator: Islam, B.U.
creator: Baharudin, Z.
creator: Raza, M.Q.
creator: Nallagownden, P.
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.
publisher: IEEE Computer Society
date: 2014
type: Conference or Workshop Item
type: PeerReviewed
identifier:   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.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906351337&doi=10.1109%2fICIAS.2014.6869528&partnerID=40&md5=6a8e6d25720013a075c72cc181885672
relation: 10.1109/ICIAS.2014.6869528
identifier: 10.1109/ICIAS.2014.6869528