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primary topic: A review on short term load forecasting using hybrid neural network techniques

A review on short term load forecasting using hybrid neural network techniques
type: AcademicArticle
type: Article
abstract: Load forecasting is very essential for the efficient and reliable operation of a power system. Often uncertainties significantly decrease the prediction accuracy of load forecasting; this in turn affects the operation cost dramatically as well as the optimal day-to-day operation of the power system. In this article, an overview of recently published work on hybrid neural network techniques to forecast the electrical load demand. A hybrid neural network forecasting model is proposed, which is a combination of simulated annealing (SA) and particle swarm optimization (PSO) called SAPSO. In proposed techniqiue, particle swarm optimization (PSO) algorithm has the ability of global optimization and the simulated annealing (SA) algorithm has a strong searching capability. Therefore, the learning algorithm of a typical three layer feed forward neural network back propagation (BP) is replaced by SAPSO algorithm. Furthermore, preprocessing of input data, convergence, local minima and working of neural network with SAPSO algorithm also discussed. © 2012 IEEE.
date: 2012
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ConferenceItemEPrint
is part of: repository

PECon 2012 - 2012 IEEE International Conference on Power and Energy
type: Collection

M.Q. Raza
type: Person
given name: M.Q.
family name: Raza

Z. Baharudin
type: Person
given name: Z.
family name: Baharudin

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primary topic: A review on short term load forecasting using hybrid neural network techniques