relation: https://khub.utp.edu.my/scholars/5363/
title: A hybrid neuro-genetic approach for STLF: A comparative analysis of model parameter variations
creator: Ul Islam, B.
creator: Baharudin, Z.
creator: Nallagownden, P.
creator: Raza, M.Q.
description: This paper portrays the comparison of multiple techniques applied to predict the load demand. In particular, it highlights the latest trends under new circumstances based on modern non analytical soft computing models based on Artificial Neural Network (ANN) and heuristic search technique genetic algorithm (GA), deployed in the domain of load forecasting. The prediction of future load has always been recognized as a pivotal process in the planning and operational decision making by managers of electric utilities. Multiple techniques and approaches having different engineering considerations and economic analysis are deployed for this purpose. However, ANN based methods for load forecast are found better in terms of accuracy and robustness during the past few years. This supremacy is because of the inherent ability of mapping and memorizing the relationships between inputs and outputs of ANN models during their training phase. A hybrid approach that uses ANN and GA is proposed in this research with an emphasis to study the effect of varying the model parameters of both techniques. The focus is to study the impact of varying the input variables and architecture of neural network; and population size, of GA. Further, a clear comparison is also presented that explains the results of these variations in terms of load forecast accuracy and computational time. © 2014 IEEE.
publisher: IEEE Computer Society
date: 2014
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Ul Islam, B. and Baharudin, Z. and Nallagownden, P. and Raza, M.Q.  (2014) A hybrid neuro-genetic approach for STLF: A comparative analysis of model parameter variations.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901376518&doi=10.1109%2fPEOCO.2014.6814485&partnerID=40&md5=e220a50a30e3390d09d109986eecf9a4
relation: 10.1109/PEOCO.2014.6814485
identifier: 10.1109/PEOCO.2014.6814485