TY  - CONF
TI  - A review on short term load forecasting using hybrid neural network techniques
SN  - 9781467350198
EP  - 851
SP  - 846
N2  - 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.
A1  - Raza, M.Q.
A1  - Baharudin, Z.
N1  - 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
Y1  - 2012///
AV  - none
ID  - scholars2493
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874478404&doi=10.1109%2fPECon.2012.6450336&partnerID=40&md5=55688aae94f62d5cde8bbf570bce5331
KW  - 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
KW  -  Backpropagation; Electric load forecasting; Global optimization; Learning algorithms; Neural networks; Particle swarm optimization (PSO); Simulated annealing
KW  -  Forecasting
CY  - Kota Kinabalu
ER  -