Aligning Pre-Trained LLMs for Enhanced UAV Power Consumption Forecasting
Bibliographic record
Abstract
Unmanned Aerial Vehicles (UAVs) are expanding beyond military use into sectors such as logistics, communication, and transportation. However, their dependence on high-power batteries limits their range, and while fuel cells provide longer flight times, they can reduce speed and acceleration due to safety concerns. Consequently, managing UAV power consumption has become a critical challenge, directly affecting flight duration and operational performance. Therefore, accurately predicting power consumption is important for enabling efficient UAV mission planning. Thus, in this paper, we propose a fine-tuning strategy called Large Language Model for Power Forecasting (LLM4PF) that employs a Generative Pretrained Transformer (GPT-2) model to reduce computational costs without sacrificing accuracy. LLM4PF predicts power consumption based on various UAV operational data including speed and altitude among others. Furthermore, we evaluate its performance in low-data scenarios through few-shot learning with 5% and 10% data subsets. Additionally, we compare LLM4PF to transformer-based models using a public dataset, demonstrating its effectiveness and efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".