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A comparative study on energy consumption models for drones

Muli, Carlos, Park, Sangyoung and Liu, Mingming orcid logoORCID: 0000-0002-8988-2104 (2023) A comparative study on energy consumption models for drones. In: 5th Global IoT Summit 2022, 20-22 June 2022, Dublin, Ireland. ISBN 978-3031209352

Abstract
Creating an appropriate energy consumption prediction model is becoming an important topic for drone-related research in the literature. However, a general consensus on the energy consumption model is yet to be reached at present. As a result, there are many variations that attempt to create models that range in complexity with a focus on different aspects. In this paper, we benchmark the five most popular energy consumption models for drones derived from their physical behaviours and point to the difficulties in matching with a realistic energy dataset collected from a delivery drone in flight under different testing conditions. Moreover, we propose a novel data-driven energy model using the Long Short-Term Memory (LSTM) based deep learning architecture and the accuracy is compared based on the dataset. Our experimental results have shown that the LSTM based approach can easily outperform other mathematical models for the dataset under study. Finally, sensitivity analysis has been carried out in order to interpret the model.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Unmanned Aerial Vehicle; Drone Energy Model; Energy Consumption; Deep Learning; Long Short-Term Memory
Subjects:Computer Science > Machine learning
Engineering > Electronic engineering
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: González-Vidal, Aurora, Abdelgawad, Ahmed Mohamed, Sabir, Essaid, Ziegler, Sébastien and Ladid, Latif, (eds.) Internet of Things: 5th Global IoT Summit, GIoTS 2022. Lecture Notes in Computer Science (LCNS) 13533. Springer. ISBN 978-3031209352
Publisher:Springer
Official URL:https://doi.org/10.1007/978-3-031-20936-9_16
Copyright Information:© 2023 The Authors.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Entwine Research Centre, Science Foundation Ireland under Grant Number SFI/12/RC/2289_P2, Ide3a Programme
ID Code:27277
Deposited On:17 Jun 2022 12:25 by Mingming Liu . Last Modified 16 Nov 2023 12:58
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