A Data-Driven Power Consumption Model for Electric UAVs
Bibliographic record
Abstract
Unmanned aerial vehicles (UAV) are becoming a widely applied technology in many kinds of industries, such as agriculture and delivery transportation. However, the range of the drone is limited by the amount of energy it has left to consume. Because of this, in order to optimize the flight control, it is important to estimate the instantaneous power of the drone so that the flight controller can determine the best method to increase the operational time as well as effective energy preservation. By being able to predict this power, a drone can use such information to optimize the flight. This paper proposes the use of a neural network-based model for predicting the power consumption of a drone, which offers a prediction that is high in fidelity and adaptability. The proposed method does not require the knowledge of all the drone's characteristics, such as dynamics, which allows for easier implementation. Experiments are carried out to demonstrate the benefits of the neural network model's prediction capabilities.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".