DocumentCode :
2603040
Title :
Neural Networks Initial Weights Optimisation
Author :
Al-Shareef, A.J. ; Abbod, M.F.
Author_Institution :
Electr. Eng. Dept., King Abdulaziz Univ., Jeddah, Saudi Arabia
fYear :
2010
fDate :
24-26 March 2010
Firstpage :
57
Lastpage :
61
Abstract :
Artificial Neural Networks (ANN) have lately received much attention, and a great number of papers have reported successful experiments and practical tests. This paper presents the development of a simple ANN topology for load forecasting model with much improved accuracy for the Regional Power Control Centre of Saudi Electricity Company. The proposed system is based on optimising the initial random weights of the ANN using Particle Swarm Optimisation (PSO). Results have shown higher modelling accuracy compared to standard ANN, weight optimised ANN, and ANN committee machine models. The networks were trained with weather-related, time based and special events indexes for electric load data from the calendar years 2005 to 2007.
Keywords :
electricity supply industry; load forecasting; neural nets; particle swarm optimisation; power engineering computing; ANN topology; Regional Power Control Centre; Saudi Electricity Company; artificial neural network; electric load data; initial random weights optimisation; load forecasting model; particle swarm optimisation; Artificial neural networks; Load forecasting; Load modeling; Network topology; Neural networks; Particle swarm optimization; Power control; Power system modeling; Predictive models; Testing; Artificial Neural Networks; Load Forecasting; Power Load Modelling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Modelling and Simulation (UKSim), 2010 12th International Conference on
Conference_Location :
Cambridge
Print_ISBN :
978-1-4244-6614-6
Type :
conf
DOI :
10.1109/UKSIM.2010.19
Filename :
5481067
Link To Document :
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