DocumentCode :
3470047
Title :
A Novel Particle Swarm Grey Neural Network Model for Power Load Risk Forecasting
Author :
Li, Chunjie ; Chen, Tao ; Dong, Jun ; Chen, Wen
Author_Institution :
Inst. of Bus. Manage., North China Electr. Power Univ., Beijing
fYear :
2008
fDate :
12-14 Oct. 2008
Firstpage :
1
Lastpage :
4
Abstract :
In order to establish a high accuracy forecasting model for short-term electric power load, this paper made a change to grey differential equation utilizing the fundamental theorem of discrete time function. Through mapping the parameters of the equation into the BP neural network, giving the corresponding parameters when the sequence sample of load was converged in the network. In this case, optimizing the deviation of gray neural network step by step utilizing the quickly hunt ability of overall situation of the particle swarm optimized model and establishing the gray neural network forecasting model-PGNN with less deviation based on particle swarm optimization. Finally, the model´s effectiveness and accuracy were examined through a case study. The result by computer simulation suggested that the new model had a high accuracy for forecasting.
Keywords :
backpropagation; differential equations; grey systems; load forecasting; neural nets; particle swarm optimisation; power engineering computing; risk management; BP neural network; discrete time function; grey differential equation; particle swarm grey neural network model; particle swarm optimization; power load risk forecasting; short-term electric power load; Differential equations; Economic forecasting; Energy management; Load forecasting; Load modeling; Neural networks; Particle swarm optimization; Power systems; Predictive models; Risk management;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Wireless Communications, Networking and Mobile Computing, 2008. WiCOM '08. 4th International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4244-2107-7
Electronic_ISBN :
978-1-4244-2108-4
Type :
conf
DOI :
10.1109/WiCom.2008.2478
Filename :
4680667
Link To Document :
بازگشت