Prediction of aircraft cabin energy consumption based on IPSO-Elman neural network

LIN Jiaquan; SUN Fengshan; LI Yachong; ZHUANG Zibo

doi:10.7527/S1000-6893.2020.23614

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2020 , Vol. 41 >Issue 7: 323614 - 323614

DOI: https://doi.org/10.7527/S1000-6893.2020.23614

Electronics and Electrical Engineering and Control

Prediction of aircraft cabin energy consumption based on IPSO-Elman neural network

LIN Jiaquan ,
SUN Fengshan ,
LI Yachong ,
ZHUANG Zibo

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1. Institute of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China;
2. Flight Technical College, Civil Aviation University of China, Tianjin 300300, China

Received date: 2019-10-30

Revised date: 2019-11-19

Online published: 2020-02-06

Supported by

Joint Fund of the National Natural Science Foundation of China and the Civil Aviation Administration of China (U1433202)

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Abstract

To improve the prediction accuracy of cabin energy consumption when the aircraft cabin uses ground air conditioning for refrigeration, an aircraft cabin energy consumption prediction model based on the Improved Particle Swarm Optimization (IPSO) Elman neural network is proposed. The main procedure of the IPSO is as follows:the convergence domain analysis of the inertia weight and the learning factors is used to obtain a reasonable range of these two parameters; the range of the two parameters and the distance from the particle to the global optimal position are combined to dynamically adjust the two parameters; the dynamic adjustment function of the inertia weight and the learning factors are then constructed; a variation factor is introduced and a strategy proposed to prevent the PSO from being trapped in the local optimum. The IPSO-Elman is applied to the Boeing738 aircraft cabin energy consumption prediction and compared with PSO-Elman and Elman. The simulation results show the effectiveness of the cabin energy consumption prediction model based on IPSO-Elman in improving both the prediction accuracy and the convergence rate. The research results establish a theoretical basis for the aircraft cabin energy prediction model and provide further support for the energy saving of ground air conditioning and the reasonable allocation of airport electrical energy.

Key words： aircraft cabin; ground air condition; energy demands prediction; particle swarm optimization; Elman neural network

Cite this article

LIN Jiaquan , SUN Fengshan , LI Yachong , ZHUANG Zibo . Prediction of aircraft cabin energy consumption based on IPSO-Elman neural network[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2020 , 41(7) : 323614 -323614 . DOI: 10.7527/S1000-6893.2020.23614

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