  The current projection shows that much of the continental U.S. will have
significantly hotter and drier days in the following decades, leading to more
wildfire hazards that threaten the safety of power grid. Unfortunately, the
U.S. power industry is not well prepared and still predominantly relies on
empirical fire indices which do not consider the full spectrum of dynamic
environmental factors. This paper proposes a new spatio-temporal point process
model, Convolutional Non-homogeneous Poisson Process (cNHPP), to quantify
wildfire risks for power delivery networks. The proposed model captures both
the current short-term and cumulative long-term effects of covariates on
wildfire risks, and the spatio-temporal dependency among different segments of
the power delivery network. The computation and interpretation of the intensity
function are thoroughly investigated, and the connection between cNHPP and
Recurrent Neural Network is also discussed. We apply the proposed approach to
estimate wildfire risks on major transmission lines in California, utilizing
historical fire data, meteorological and vegetation data obtained from the
National Oceanic and Atmospheric Administration and National Aeronautics and
Space Administration. Comparison studies are performed to show the
applicability and predictive capability of the proposed approach. Useful
insights are obtained that potentially enhance power grid resilience against
wildfires.
