AeroPowerNet: Fixed-Wing UAV Power Consumption Estimation with an AI-Driven Hybrid Deep Learning Framework
Bibliographic record
Abstract
The instantaneous power consumption of electric-powered aerial vehicles in the aviation industry is crucial for optimizing flight activities. However, devising physics-based power consumption models requires deep insight into the dynamics of an unmanned aerial vehicle (UAV). This becomes challenging due to the variability and complexity of the parameters of airspeed, altitude, and motion of the flight controls. Therefore, a power consumption model is needed to map the influence of flight parameters on power utilization during varying flight phases. This model is crucial for mission planning, optimization, and extending the endurance of UAVs. This study introduces the AeroPowerNet framework, employing a data-driven approach based on deep learning to model UAV power consumption utilizing real-world flight data, which can serve as a foundation for future integration into fixed-wing UAV flight operations. We implemented a test of four different models: RNN-LSTM, GRU, Transformer, and a hybrid model, which were trained and compared. Experimental results show that the hybrid model outperforms all other models, achieving the best performance with MAE of 3.38W, R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 99.31%, and NRMSE of 0.21% for the first UAV flight, and an MAE of 7.52W, R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 96.68%, and NRMSE of 1.03% for the second flight.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".