Prediction of Aircraft Cabin Energy Consumption Based on Improved Cooperative PSO Neural Network

doi:10.16182/j.issn1004731x.joss.201804040

Abstract

Abstract: To correctly evaluate the energy needs of the aircraft cabin and to predict the energy consumption of the aircraft cabin with higher accuracy, an energy consumption prediction method based on improved particle swarm optimization (PSO) neural network algorithm parameters is proposed. The method combines the cooperative particle swarm optimization algorithm with chaotic particle swarm optimization algorithm. On the basis of cooperative particle swarm optimization algorithm chaos theory is introduced. Continuous search ability by using chaos optimization method to overcome the collaborative optimization algorithm is easy to fall into the local extremum problem. The parameters of the neural network can accelerate the convergence rate of the cabin, and also can improve the accuracy of prediction by improving the particle swarm optimization algorithm. Simulation results verify the validity and feasibility of the proposed method.

Key words: improved cooperative PSO, neural network, aircraft cabin, energy consumption prediction

CLC Number:

TH137.8

Wang Xiuyan, Liu Yanmin, Zhang Gewen, Li Zongshuai, Lin Jiaquan. Prediction of Aircraft Cabin Energy Consumption Based on Improved Cooperative PSO Neural Network[J]. Journal of System Simulation, 2018, 30(4): 1535-1541.

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