Title :
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
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;
Conference_Titel :
Computational and Information Sciences (ICCIS), 2012 Fourth International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4673-2406-9
DOI :
10.1109/ICCIS.2012.279
