Research on Neural Network Prediction of Power Transmission and Transformation Project Cost Based on GA-RBF and PSO-RBF

Dong Xiao Niu; Fu Yu Hua; Bing Jie Li; Ming Fu; Xiao Yan Yu; Liang Liang

doi:10.4028/www.scientific.net/AMM.644-650.2526

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 644-650Research on Neural Network Prediction of Power...

Research on Neural Network Prediction of Power Transmission and Transformation Project Cost Based on GA-RBF and PSO-RBF

Abstract:

This paper’s primary mission is to predict the cost of power transmission and transformation projects of a certain China’s province based on GA-RBF and PSO-RBF neural network. The projects’ data is divided into two main categories-power transformation projects and power line construction projects, with the cost per capacity (RMB/kVA) and cost per unit length (RMB/km) as the indicators of each category. After filtering out main influencing factors and initialization processing for the data, the obtained normalized data can be put into GA-RBF and PSO-RBF predicting model. The empirical analysis is carried on by Matlab. The prediction accuracy can be compared intuitively based on the output of neural network, and from the results we can conclude that GA-RBF is more precise than PSO-RBF when applied to project cost prediction.

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Periodical:

Applied Mechanics and Materials (Volumes 644-650)

Pages:

2526-2531

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.644-650.2526

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Online since:

September 2014

Authors:

Dong Xiao Niu*, Fu Yu Hua, Bing Jie Li, Ming Fu, Xiao Yan Yu, Liang Liang

Keywords:

Cost of Power Transmission, Genetic Algorithm (GA), Particle Swarm Optimization Algorithm, RBF Neural Network, Transformation Projects

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