eprintid: 2493
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/00/24/93
datestamp: 2023-11-09 15:50:43
lastmod: 2023-11-09 15:50:43
status_changed: 2023-11-09 15:43:39
type: conference_item
metadata_visibility: show
creators_name: Raza, M.Q.
creators_name: Baharudin, Z.
title: A review on short term load forecasting using hybrid neural network techniques
ispublished: pub
keywords: Day-to-day operations; Electrical load; Forecasting models; Hybrid neural networks; Input datas; Load forecasting; Local minimums; Operation cost; Particle swarm optimization algorithm; Prediction accuracy; Reliable operation; Searching capability; Short term load forecasting; Simulated annealing algorithms; Three-layer, Backpropagation; Electric load forecasting; Global optimization; Learning algorithms; Neural networks; Particle swarm optimization (PSO); Simulated annealing, Forecasting
note: cited By 25; Conference of 2012 IEEE International Conference on Power and Energy, PECon 2012 ; Conference Date: 2 December 2012 Through 5 December 2012; Conference Code:95813
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
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874478404&doi=10.1109%2fPECon.2012.6450336&partnerID=40&md5=55688aae94f62d5cde8bbf570bce5331
id_number: 10.1109/PECon.2012.6450336
full_text_status: none
publication: PECon 2012 - 2012 IEEE International Conference on Power and Energy
place_of_pub: Kota Kinabalu
pagerange: 846-851
refereed: TRUE
isbn: 9781467350198
citation:   Raza, M.Q. and Baharudin, Z.  (2012) A review on short term load forecasting using hybrid neural network techniques.  In: UNSPECIFIED.