eprintid: 4899
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/48/99
datestamp: 2023-11-09 16:16:36
lastmod: 2023-11-09 16:16:36
status_changed: 2023-11-09 15:59:50
type: conference_item
metadata_visibility: show
creators_name: Islam, B.U.
creators_name: Baharudin, Z.
creators_name: Raza, M.Q.
creators_name: Nallagownden, P.
title: Optimization of neural network architecture using genetic algorithm for load forecasting
ispublished: pub
keywords: Algorithms; Electric load forecasting; Forecasting; Genetic algorithms; Neural networks, Computational costs; Computational intelligent techniques; Forecast accuracy; Load forecasting; Multi objective algorithm; Multi-layer perceptron neural networks; Network structures; Neural network topology, Network architecture
note: cited By 33; Conference of 2014 5th International Conference on Intelligent and Advanced Systems, ICIAS 2014 ; Conference Date: 3 June 2014 Through 5 June 2014; Conference Code:107042
abstract: 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.
date: 2014
publisher: IEEE Computer Society
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906351337&doi=10.1109%2fICIAS.2014.6869528&partnerID=40&md5=6a8e6d25720013a075c72cc181885672
id_number: 10.1109/ICIAS.2014.6869528
full_text_status: none
publication: 2014 5th International Conference on Intelligent and Advanced Systems: Technological Convergence for Sustainable Future, ICIAS 2014 - Proceedings
place_of_pub: Kuala Lumpur
refereed: TRUE
isbn: 9781479946549
citation:   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.