Improving airline fuel efficiency via fuel burn prediction and uncertainty estimation
Improving airline fuel efficiency via fuel burn prediction and uncertainty estimation
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DOI：
10.1016/j.trc.2018.10.002
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发表时间：
2018-12
期刊：
影响因子：
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通讯作者：
Lei Kang;M. Hansen
中科院分区：
文献类型：
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作者：
Lei Kang;M. Hansen
减少燃油消耗是整个航空业的统一目标。航空公司节省燃油的机会之一是可以减少调度员自行决定的燃油装载量。在这项研究中，我们提出了一种新颖的酌情燃油估计方法，可以帮助调度员做出更好的酌情燃油装载决策。根据对我们研究航空公司的分析，我们的方法被发现与当前的燃油装载实践相比，可以大大减少不必要的酌情燃油装载，同时保持相同的安全水平。这个想法是，通过向调度员提供更准确的信息和从飞行记录中得出的更好的建议，可以减少不必要的燃油装载和相应的携带成本。我们应用集成学习技术来改进燃油燃烧预测并构建预测区间（PI）来捕获模型预测的不确定性。然后，PI 的上限可用于酌情燃料装载。对于我们研究的航空公司来说，这种方法的潜在效益预计为每年节省 6150 万美元的燃油并减少 4.28 亿公斤的二氧化碳排放。这项研究还在酌情燃油估计和航空系统可预测性之间建立了联系，其中所提出的模型还可用于预测通过改进空中交通管理（ATM）以提高系统可预测性为目标而减少燃油装载所带来的好处。
Reducing fuel consumption is a unifying goal across the aviation industry. One fuel-saving opportunity for airlines is the possibility of reducing discretionary fuel loading by dispatchers. In this study, we propose a novel discretionary fuel estimation approach that can assist dispatchers with better discretionary fuel loading decisions. Based on the analysis on our study airline, our approach is found to substantially reduce unnecessary discretionary fuel loading while maintaining the same safety level compared to the current fuel loading practice. The idea is that by providing dispatchers with more accurate information and better recommendations derived from flight records, unnecessary fuel loading and corresponding cost-to-carry could both be reduced. We apply ensemble learning techniques to improve fuel burn prediction and construct prediction intervals (PIs) to capture the uncertainty of model predictions. The upper bound of a PI can then be used for discretionary fuel loading. The potential benefit of this approach is estimated to be $61.5 million in fuel savings and 428 million kg of CO2reduction per year for our study airline. This study also builds a link between discretionary fuel estimation and aviation system predictability in which the proposed models can also be used to predict benefits from reduced fuel loading enabled by improved Air Traffic Management (ATM) targeting on improved system predictability.