查询结果:   刘家学,沈贵宾.基于LSTM的航空公司能耗序列预测[J].计算机应用与软件,2019,36(10):60 - 65.
中文标题
基于LSTM的航空公司能耗序列预测
发表栏目
应用技术与研究
摘要点击数
425
英文标题
AIRLINE ENERGY CONSUMPTION SEQUENCE PREDICTION BASED ON LSTM
作 者
刘家学 沈贵宾 Liu Jiaxue Shen Guibin
作者单位
中国民航大学电子信息与自动化学院 天津 300300     
英文单位
College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China     
关键词
航空公司能耗 LSTM 网格搜索 时间窗 时间序列预测
Keywords
Airline energy consumption LSTM Grid-Search Time window Time series prediction
基金项目
民航局科技基金项目(MHRD201121);民航局节能减排专项计划项目(DPDSR0010)
作者资料
刘家学,教授,主研领域:飞行数据分析,飞机维修仿真。沈贵宾,硕士生。 。
文章摘要
为提高航空公司能耗的预测精度,针对能耗数据的复杂非线性时序特性,提出一种基于长短时记忆网络(LSTM)的时间窗滑动航空公司能耗预估模型。该方法对能耗时序数据进行预处理,消除能耗时序数据的季节性趋势;依据滑动时间窗将数据转换成监督型数据,构建基于LSTM的模型来实现航空公司能耗预测,并利用网格搜索算法进行参数优选。实验结果表明,该模型预测精度优于传统ARMA模型、SVR模型,验证了其可行性。
Abstract
In order to improve the prediction accuracy of airline energy consumption, aiming at the complex nonlinear timing characteristics of energy consumption data, we proposed a time window sliding airline energy consumption estimation model based on LSTM. Energy consumption time series data were preprocessed to eliminate the seasonal trend of energy consumption time series data. Then, data were converted into supervised data according to sliding time window. We constructed an LSTM-based model to realize airline energy consumption prediction, and parameters were optimized by grid search algorithm. The experimental results show that the prediction accuracy of this model is better than that of traditional ARMA model and SVR model, which verifies the feasibility of the model.
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