计算机与现代化 ›› 2022, Vol. 0 ›› Issue (08): 65-69.

• 数据库与数据挖掘 • 上一篇    下一篇

基于聚类分析的航班油耗组合估计

  

  1. (1.商飞软件有限公司,四川成都610000;2.中国民航大学工程技术训练中心,天津300300)
  • 出版日期:2022-08-22 发布日期:2022-08-22
  • 作者简介:李舒(1982—),男,江西南昌人,高级工程师,硕士,研究方向:民航系统工程,E-mail: 740721087@qq.com; 张伟业(1995—),男,湖北武汉人,助理工程师,硕士,研究方向:民航系统工程,E-mail: zhangweiye@comac.cc; 汪坤(1984—),男,四川阆中人,高级工程师,硕士,研究方向:民航系统工程,E-mail: cauc_zwy@163.com; 段照斌(1989—),男,河南南阳人,讲师,硕士,研究方向:民航系统工程,E-mail: 26428496@qq.com。
  • 基金资助:
    国家自然科学基金资助项目(61703406)

Combined Estimation Method of Fuel Conesumption Based on Cluster Analysis

  1. (1. COMAC Software Co., Ltd., Chengdu 610000, China; 
    2. Engineering Technology Training Center, Civil Aviation University of China, Tianjin 300300, China)
  • Online:2022-08-22 Published:2022-08-22

摘要: 针对碳排放报告中燃油消耗数据存在单个不连续缺失和连续缺失2类数据,使用单一方法估计误差大的问题,提出一种基于聚类分析的组合估计方法。该方法首先采用K-medoids聚类算法将数据归类为单个不连续缺失数据以及连续缺失数据,然后使用NB方法对单个不连续数据进行估计填充,使用DTW方法对连续缺失数据估计填充,最后分别在1%、2%以及3%均方根误差时进行估计结果评价。实验结果表明:基于聚类分析的NB-DTW组合方法能有效降低估计误差,在1%、2%以及3%均方根误差时比NB方法分别降低了9.3%、12.1%、12.96%,比DTW方法分别降低了35.46%、43.62%、55.04%。

关键词: 油耗估计, 聚类分析, 朴素贝叶斯, 动态时间规整, 数据缺失

Abstract: Aiming at the problem of single discontinuous missing data and continuous missing data in the carbon emission report, the estimation error of using a single method is large, a combined estimation method based on cluster analysis is proposed. The method firstly uses the K-medoids clustering algorithm to classify the data into single discontinuous missing data and continuous missing data, and then uses the Naive Bayes (NB) method to estimate the single discontinuous data, uses Dynamic Time Warping (DTW) method to estimate the continuous missing data, and finally evaluates the estimation results at 1%, 2%, and 3% root mean square error. The simulation results show that the NB-DTW combination method based on cluster analysis can effectively reduce the estimation error, which is 9.3%, 12.1% and 12.96% lower than the NB method at 1%, 2% and 3% root mean square error, respectively, and reduced by 35.46%, 43.62% and 55.04% respectively than DTW method.

Key words: fuel consumption, cluster analysis, Naive Bayes, dynamic time warping;missing data