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.Comment: Accepted at Global IoT Summit 2022 Conferenc