基于新陈代谢灰色马尔科夫 ARMA模型的航空公司能耗预测

航空公司能耗的预测直接影响能源需求的规划与节能的决策.针对航空公司能耗数据既有趋势性又有波动性的特点,提出了新陈代谢灰色马尔科夫-ARMA的能耗组合滑动预测模型.该模型利用灰色马尔科夫方法描述了能耗的变动趋势,通过ARMA模型捕捉残差序列的相关性来描述波动性,用新陈代谢的方法剔除模型中失去时效性的旧数据,解决了常规预测模型不足以完全描述航空公司能耗运动趋势的问题,提高了模型预测精度.仿真结果表明:提出模型精度优于传统ARMA模型和灰色马尔科夫模型,能够实现月度能耗的有效预测,为航空公司能耗监测和节能工作的优化开展提供了有力支持.

Forecast of airline's energy consumption based on the Metabolic Gray Markov-ARMA model

The energy consumption forecast of airline directly affects energy demand planning and energy saving decisions. A single prediction model is not sufficient to fully describe the trend of airline's energy consumption, which leads to the prediction accuracy is not high. Considering the trend and volatility of the data,a combined sliding prediction model of the energy consumption based on the metabolic Gray Markov-ARMA is put forward to improve the prediction accuracy in this paper. Firstly,the Gray Markov is used to describe the trend of energy consumption. Secondly,ARMA model is used to describe the volatility by exploring the correlation between the residual sequences. Thirdly,Metabolic is used to overcome the timeliness loss of old data. The simulation results show that the prediction accuracy of the metabolic Gray Markov-ARMA model is higher than that of the ARMA model and the Gray Markov model,so it can effectively predict the monthly energy consumption. The study of this method can provide support for the monitoring of energy consumption of airlines and the optimization of energy saving work.