Convolutional Non-homogeneous Poisson Process with Application to Wildfire Risk Quantification for Power Delivery Networks

TL;DR

This paper introduces a novel spatio-temporal point process model, cNHPP, to better quantify wildfire risks affecting power grids by incorporating environmental factors and network dependencies, with applications to California's transmission lines.

Contribution

The paper develops the convolutional non-homogeneous Poisson process model, capturing dynamic environmental effects and network dependencies, and links it to neural networks for wildfire risk assessment.

Findings

Effective wildfire risk estimation on California transmission lines.

Improved predictive accuracy over traditional methods.

Insights into environmental and network factors influencing wildfires.

Abstract

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.