Short-Term Power Load Forecasting Based on Self-Adapting PSO-BP Neural Network Model

Author

He, Yaoyao ; Xu, Qifa

Author_Institution

Sch. of Manage., Hefei Univ. of Technol., Hefei, China

fYear

2012

fDate

17-19 Aug. 2012

Firstpage

1096

Lastpage

1099

Abstract

To resolve the problem of short-term power load forecasting, we propose a self-adapting particle swarm optimization (PSO) algorithm to optimize the error back propagation (BP) neural network model. The proposed model is called PSO-BP model which employs PSO to adjust control parameters of BP neural network. In order to verify the performance of PSO-BP, the practical datum of a city in China are selected in the experiments. Simulation results show that our approach outperforms simple BP neural network.

Keywords

backpropagation; load forecasting; neural nets; particle swarm optimisation; power engineering computing; power system planning; China; control parameters; error back propagation neural network model optimization; power system operation; power system planning; self-adapting PSO-BP neural network model; self-adapting particle swarm optimization algorithm; short-term power load forecasting; Forecasting; Load forecasting; Load modeling; Neural networks; Neurons; Particle swarm optimization; Predictive models; BP neural network; Particle Swarm optimization (PSO); forecasting error; load forecasting;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational and Information Sciences (ICCIS), 2012 Fourth International Conference on

Conference_Location

Chongqing

Print_ISBN

978-1-4673-2406-9

Type

conf

DOI

10.1109/ICCIS.2012.279

Filename

6300809