  Energy management is a critical aspect of risk assessment for Uncrewed Aerial
Vehicle (UAV) flights, as a depleted battery during a flight brings almost
guaranteed vehicle damage and a high risk of human injuries or property damage.
Predicting the amount of energy a flight will consume is challenging as
routing, weather, obstacles, and other factors affect the overall consumption.
We develop a deep energy model for a UAV that uses Temporal Convolutional
Networks to capture the time varying features while incorporating static
contextual information. Our energy model is trained on a real world dataset and
does not require segregating flights into regimes. We illustrate an improvement
in power predictions by $29\%$ on test flights when compared to a
state-of-the-art analytical method. Using the energy model, we can predict the
energy usage for a given trajectory and evaluate the risk of running out of
battery during flight. We propose using Conditional Value-at-Risk (CVaR) as a
metric for quantifying this risk. We show that CVaR captures the risk
associated with worst-case energy consumption on a nominal path by transforming
the output distribution of Monte Carlo forward simulations into a risk space.
Computing the CVaR on the risk-space distribution provides a metric that can
evaluate the overall risk of a flight before take-off. Our energy model and
risk evaluation method can improve flight safety and evaluate the coverage area
from a proposed takeoff location.
  The video and codebase are available at https://youtu.be/PHXGigqilOA and
https://git.io/cvar-risk .
