Adap-Informer: Adaptive Aircraft Fuel Prediction Framework Supporting Emergency Decision-Making and Aviation Decarbonization

This study proposes Adap-Informer, an adaptive fuel prediction framework addressing the limitations of fixed input and output structures and underutilized real-time data in existing methods. It employs a grid search with early stopping algorithm to determine optimal sequence configurations and pre-trains dedicated models for distinct flight phases. An online selection mechanism dynamically matches the most suitable model based on accumulating real-time data, enabling progressively refined predictions. Experimental results show a continuous reduction in prediction error as more data becomes available, with the Mean Absolute Error decreasing from 0.12 to 0.052—corresponding to a maximum fuel quantity error of 1400 kg. This is substantially lower than the 2000–5000 kg of redundant fuel currently carried. The framework’s accuracy complies with core aviation safety regulations like ETOPS and FAA Part 121, providing a technical basis for safe fuel load optimization. By reducing redundant fuel, it directly contributes to aviation decarbonization, supporting the industry’s alignment with ICAO’s net-zero emissions target by 2050 and offering robust support for sustainable aviation development.