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

被引：0
作者：
Lin J. [1 ]
Sun F. [1 ]
Li Y. [1 ]
Zhuang Z. [2 ]
机构：
[1] Institute of Electronic Information and Automation, Civil Aviation University of China, Tianjin
[2] Flight Technical College, Civil Aviation University of China, Tianjin
来源：
Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica | 2020年 / 41卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Aircraft cabin; Elman neural network; Energy demands prediction; Ground air condition; Particle swarm optimization;
D O I：
10.7527/S1000-6893.2020.23614
中图分类号：
学科分类号：
摘要：
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. © 2020, Press of Chinese Journal of Aeronautics. All right reserved.
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共 24 条
  • [1] LIN J Q, LI W W., Best wind speed of ground air conditioning system based on PMV-PPD, Acta Aeronautica et Astronautica Sinica, 38, 8, (2017)
  • [2] CUI W, OUYANG Q, ZHU Y., Field study of thermal environment spatial distribution and passenger local thermal comfort in aircraft cabin, Building & Environment, 80, 10, pp. 213-220, (2014)
  • [3] FAN Z F, MA X P, SHAO X G, Et al., RBF neural network multi-step predictive control for nonlinear systems, Control and Decision, 29, 7, pp. 1274-1278, (2014)
  • [4] PI J, MA S, HE J C, Et al., Rolling bearing fault diagnosis based on IGA-ELM network, Acta Aeronautica et Astronautica Sinica, 39, 9, (2018)
  • [5] HAMID M F A, RICHARD H G A, RAMLI N A., An analysis on energy consumption of two different commercial buildings in Malaysia, IEEE International Conference on Power & Energy, pp. 344-349, (2017)
  • [6] WANG X Y, LIU Y M, ZHANG G W, Et al., Prediction of aircraft cabin energy consumption based on PSO and CRO algorithms, Journal of System Simulation, 30, 8, pp. 263-270, (2018)
  • [7] ZHOU C, DING L Y, HE R., PSO-based Elman neural network model for predictive control of air chamber pressure in slurry shield tunneling under Yangtze River, Automation in Construction, 36, 15, pp. 208-217, (2013)
  • [8] RATNAWEERA A, HALGAMUGE S K, WATSON H C., Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients, IEEE Transactions on Evolutionary Computation, 8, 3, pp. 225-240, (2004)
  • [9] WANG C F, LIU K., An improved particle swarm optimization algorithm based on comparative judgment, Natural Computing, 32, 6, pp. 123-132, (2017)
  • [10] ARUMUGAM M S, RAO M V C., On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems, Applied Soft Computing Journal, 8, 1, pp. 324-336, (2008)
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