# Data-Efficient Modeling for Precise Power Consumption Estimation of Quadrotor Operations Using Ensemble Learning

arXiv: https://arxiv.org/abs/2205.10997 · HTML: https://mlsanity.com/paper/2205.10997/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 39 references that are in the map.
- First posted on arXiv 23 May 2022.

## Builds on (most cited first, 6 of 6)

- [Neural Network Approximation: Three Hidden Layers Are Enough](https://mlsanity.com/paper/2010.14075/) (2020)
- [Vehicle Routing Problems for Drone Delivery](https://mlsanity.com/paper/1608.02305/) (2016)
- [Autonomous Recharging and Flight Mission Planning for Battery-operated Autonomous Drones](https://mlsanity.com/paper/1703.10049/) (2017)
- [CVaR-based Flight Energy Risk Assessment for Multirotor UAVs using a Deep Energy Model](https://mlsanity.com/paper/2105.15189/) (2021)
- [In-flight positional and energy use data set of a DJI Matrice 100 quadcopter for small package delivery](https://mlsanity.com/paper/2103.13313/) (2021)
- [Conflict-Free Four-Dimensional Path Planning for Urban Air Mobility Considering Airspace Occupations](https://mlsanity.com/paper/2107.12829/) (2021)

## Built on by (most cited first, 1 of 1)

- [Conflict Resolution under Degraded Surveillance in Air Corridors Using Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2607.20547/) (2026)

## Abstract (arXiv)

Electric Take-Off and Landing (eVTOL) aircraft is considered as the major aircraft type in the emerging urban air mobility. Accurate power consumption estimation is crucial to eVTOL, supporting advanced power management strategies and improving the efficiency and safety performance of flight operations. In this study, a framework for power consumption modeling of eVTOL aircraft was established. We employed an ensemble learning method, namely stacking, to develop a data-driven model using flight records of three different types of quadrotors. Random forest and extreme gradient boosting, showing advantages in prediction, were chosen as base-models, and a linear regression model was used as the meta-model. The established stacking model can accurately estimate the power of a quadrotor. Error analysis shows that about 80% prediction errors fall within one standard deviation interval and less than 0.5% error in the prediction for an entire flight can be expected with a confidence of more than 80%. Our model outperforms the existing models in two aspects: firstly, our model has a better prediction performance, and secondly, our model is more data-efficient, requiring a much smaller dataset. Our model provides a powerful tool for operators of eVTOL aircraft in mission management and contributes to promoting safe and energy-efficient urban air traffic.