民航客机运营燃油消耗的动态预测方法研究
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摘要
为提高民航客机运营中燃油消耗的预测精度,提出了基于实际运营数据的带有机龄特征的预测方法。通过对飞机航线不同飞行阶段的燃油消耗过程解析,提出了基于最小二乘支持向量机(LSSVM)的动态预测方法,构造改进粒子群方法对LSSVM参数进行优化;提出基于横向与纵向的二维驱动的动态预测模型,使飞机运营燃油消耗动态预测更加与实际情况相符,以增加预测精度。最后通过某航空公司客机运营实际数据验证了所提预测方法的有效性,与传统LSSVM方法相比精度更高。
A fuel consumption prediction method with the characteristics of aircraft age based on actual civil aircraft is presented. Through the analysis of the fuel consumption of aircraft operations at different stages, a dynamic prediction method is proposed based on the least squares support vector machine(LSSVM), and an improved particle swarm optimization method is proposed to optimize the LSSVM parameters(IPSO-LSSVM);In order to increase the prediction accuracy, a two-dimensional prediction model based on horizontal and vertical is put forward to make the fuel consumption prediction of aircraft operation more consistent with the actual situation. Finally, the effectiveness of the proposed method is validated by the practical data of aircraft operation, furthermore, the proposed method has better estimating performance than the traditional LSSVM method.
A fuel consumption prediction method with the characteristics of aircraft age based on actual civil aircraft is presented. Through the analysis of the fuel consumption of aircraft operations at different stages, a dynamic prediction method is proposed based on the least squares support vector machine(LSSVM), and an improved particle swarm optimization method is proposed to optimize the LSSVM parameters(IPSO-LSSVM);In order to increase the prediction accuracy, a two-dimensional prediction model based on horizontal and vertical is put forward to make the fuel consumption prediction of aircraft operation more consistent with the actual situation. Finally, the effectiveness of the proposed method is validated by the practical data of aircraft operation, furthermore, the proposed method has better estimating performance than the traditional LSSVM method.
引文
[1]张延海,王晓东,孙宏,张培文.民用飞机航线平均燃油消耗评估模型研究[J].交通运输工程与信息学报,2014,12(4):85-90.Zhang Y H, Wang X D, Sun H, Zhang P W. Statistical modelto evaluate civil aircraft fuel consumption[J]. Journal of Transportation Engineering and Information, 2014,12(4):85-90
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[7] Yu L. An evolutionary programming based asymmetric weighted least squares support vector machine ensemble learning methodology for software repository mining[J]. Information Sciences, 2012, 191(15):31-46.
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[14]仇军,王景成.基于PSO-LSSVM的城市时用水量预测[J].控制工程,2014,21(2):232-236.Qiu J, Wang J X. Hourly urban water consumption prediction based on PSO-LSSVM[J].Control Engineering of China,2014,21(2):232-236.
[15]张军.钢铁企业冶炼过程操作解析与优化研究[D].东北大学,2014.Zhang J. Research on operation analytics and optimization of iron-steel enterprise smelting process[D], Northeastern University,2014.
[16]曹净,丁文云,赵党书,宋志刚,刘海明.基于LSSVM-ARMA模型的基坑变形时间序列预测[J],控制工程,2015,22(3):475-480.Cao J, Ding W Y, et al. Time series forecast of foundation pit deformation based on PSO-LSSVM[J].Control Engineering of China,2015,22(3):475-480.
[17]曹惠玲,贾超.基于QAR的民航发动机燃油流量控制规律研究[J].科学技术与工程,2013,13(13):3814-3818.Cao H L, Jia C. Research on fuel control law of civil aviation engine based on QAR[J]. Science Technology and engineering,2013,13(13):3814-3818.
[18]四座级轻型飞机设计报告[R].沈阳飞机工业(集团)有限公司,2015,10.Four levels of light aircraft design report[R]. Shenyang Aircraft Corporation,2005,10.
[19]马涛.规模数据支持的飞机油耗监控方法研究[D].中国民航大学,2015.Ma T. Research on the aircraft fuel consumption control method based on scale data support[D]. Civil Aviation University of China, 2015.
[20] Zhang, J. Optimal control problem of converter steelmaking production process based on operation optimization method[J].Discrete Dynamics in Nature and Society, 2015:483674-483687.
[2]刘婧.基于飞行数据分析的飞机燃油估计模型[D].南京航空航天大学,2010.Liu Jing. The aircraft fuel estimation model based on flight data analysis[D]. Nanjing University of Aeronautics and Astronautics,2010.
[3]王长坤.基于多元线性回归的飞机巡航阶段燃油流量模型研究[J].科技致富向导,2010,2(2):15-18.Wang C K. The research of fuel flow method in flight cruising basing on multiple linear regression[J]. Guide of Sci-tech Magazine, 2010,2(2):15-18.
[4]耿宏,揭俊.基于QAR数据的飞机巡航段燃油流量回归模型[J].航空发动机,2008,34(4):46-50.Gen H, Jie J. Fuel flow regression model of aircraft cruise based on QAR data[J] Aeroengine, 2008,34(4):46-50.
[5] Schilling G D. Modeling aircraft fuel consumption with a neural network[D]. Virginia Polytechnic Institute and State University, 1997.
[6] Trani A A. Enhancements to SIMMOD:A Neural Network Post-processor to Estimate Aircraft Fuel Consumption[D]. Virginia Polytechnic Institute and State University, 1997.
[7] Yu L. An evolutionary programming based asymmetric weighted least squares support vector machine ensemble learning methodology for software repository mining[J]. Information Sciences, 2012, 191(15):31-46.
[8] Ismail S, Shabri A, Samsudin R. A hybrid model of self-organizing maps(SOM)and least square support vector machine[LSSVM] for time-series forecasting[J]. Expert Systems with Applications, 2011,38(8):10574-10578.
[9] Yuan S F, Chu F L. Fault diagnosis based on support vector machines with parameter optimization by artificial immunization algorithm[J].Mechanical Systems and Signal Processing, 2007,21(3):1318-1330.
[10] Kennedy J, Eberhart R C. Particle swarm optimization[C]. IEEE International Conference on Neural Networks, Perth, Western Australia, 1995,1942-1948.
[11] Zheng Y L, Ma L H, Zhang L Y. Empirical study of particle swarm optimizer with an increasing inertia weight[C].The 2003 Congress on Evolutionary Computation, 2003,221-226.
[12] Clerc M. Discrete Particle Swarm Optimization[M]. New Optimization Techniques in Engineering, Springer Verlag, 2004,219-239.
[13]孟洛明,朱杰辉,杨杨,孟玲莉,张平平,高志鹏.支持向量机回归预测在网络故障检测中的应用[J].北京邮电大学学报,2014,4(37):23-29.Meng L M, Zhu J H, et al. A fault detection algorithm for wireless sensor networks based on support vector regression[J].Journal of Beijing University of Posts and Telecommunications.2014,4(37):23-29.
[14]仇军,王景成.基于PSO-LSSVM的城市时用水量预测[J].控制工程,2014,21(2):232-236.Qiu J, Wang J X. Hourly urban water consumption prediction based on PSO-LSSVM[J].Control Engineering of China,2014,21(2):232-236.
[15]张军.钢铁企业冶炼过程操作解析与优化研究[D].东北大学,2014.Zhang J. Research on operation analytics and optimization of iron-steel enterprise smelting process[D], Northeastern University,2014.
[16]曹净,丁文云,赵党书,宋志刚,刘海明.基于LSSVM-ARMA模型的基坑变形时间序列预测[J],控制工程,2015,22(3):475-480.Cao J, Ding W Y, et al. Time series forecast of foundation pit deformation based on PSO-LSSVM[J].Control Engineering of China,2015,22(3):475-480.
[17]曹惠玲,贾超.基于QAR的民航发动机燃油流量控制规律研究[J].科学技术与工程,2013,13(13):3814-3818.Cao H L, Jia C. Research on fuel control law of civil aviation engine based on QAR[J]. Science Technology and engineering,2013,13(13):3814-3818.
[18]四座级轻型飞机设计报告[R].沈阳飞机工业(集团)有限公司,2015,10.Four levels of light aircraft design report[R]. Shenyang Aircraft Corporation,2005,10.
[19]马涛.规模数据支持的飞机油耗监控方法研究[D].中国民航大学,2015.Ma T. Research on the aircraft fuel consumption control method based on scale data support[D]. Civil Aviation University of China, 2015.
[20] Zhang, J. Optimal control problem of converter steelmaking production process based on operation optimization method[J].Discrete Dynamics in Nature and Society, 2015:483674-483687.