Short-Term Gas Load Forecasting Based on Wavelet BP Neural Network Optimized by Genetic Algorithm

Feng Yu; Zhi Qing Wang; Xiao Zhong Xu

doi:10.4028/www.scientific.net/AMM.631-632.79

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 631-632Short-Term Gas Load Forecasting Based on Wavelet...

Short-Term Gas Load Forecasting Based on Wavelet BP Neural Network Optimized by Genetic Algorithm

Abstract:

Aiming at the limitations of a single neural network for effective gas load forecasting, a combinational model based on wavelet BP neural network optimized by genetic algorithm is proposed. The problems that traditional BP algorithm converges slowly and easily falls into local minimum are overcame. The wavelet neural network strengthens the function approximation capacity of the network by combining the well time-frequency local feature of wavelet transform with the self-learning ability of neural network. And optimized by the real coded genetic algorithm, the network converges more quick than non-optimized one. This proposed model is applied to daily gas load forecasting for Shanghai and the simulation results indicate that this algorithm has excellent prediction effect.

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Periodical:

Applied Mechanics and Materials (Volumes 631-632)

Pages:

79-85

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.631-632.79

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Online since:

September 2014

Authors:

Feng Yu*, Zhi Qing Wang, Xiao Zhong Xu

Keywords:

BP Neural Network (BPNN), Genetic Algorithm (GA), Load Forecasting, Simulation, Wavelet Analysis

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* - Corresponding Author

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