Prediction of Airport Energy Consumption Using a Hybrid Grey Neural Network Model

Abstract:

Prediction and control of airport energy consumption plays an important role in promoting energy saving and emission reduction in the civil aviation industry. In view of the complexity and nonlinearity of energy consumption system, as well as a small number of airport energy consumption data, this study develops a hybrid grey neural network model, which organically combines GM (1, 1) model and BP neural network in parallel and series connections, on the basis of analysis of main prediction methods. With energy consumption data from one Chinese airport for the whole year 2010, this study analyzes and compares different prediction results using different models through matlab. It shows that the hybrid model has a better accurate prediction, and its prediction accuracy can be controlled within 7%.

• Periodical: Advanced Materials Research (Volumes 608-609)
• Pages: 1252-1256
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.608-609.1252
• Online since: December 2012
• Authors: Jing Jie Chen, Chen Xiao, Wen Gao Qian
• Keywords: BP Neural Network (BPNN), Combined Prediction, Energy Consumption Prediction, Grey Model, Hybrid Grey Neural Network

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