Uncertainty quantification for critical energy systems during compound extremes via BMW-GAM

2026-03-09

AI Summary

This study addresses the challenge of quantifying uncertainty risks posed by compound extreme weather events to critical energy systems, with the aim of enhancing their resilience. To this end, we propose an efficient modeling framework that integrates a Bayesian generalized additive model (BMW-GAM) with a Gaussian copula. The approach employs a sliding-window strategy to fit marginal distributions and leverages the copula to capture multivariate spatiotemporal dependencies, enabling both interpretable and computationally efficient probabilistic simulation and uncertainty quantification. We demonstrate the method’s effectiveness using high-fidelity climate data from Argonne National Laboratory’s ADDA dataset, performing joint uncertainty analysis on key meteorological variables—including temperature, wind speed, and global horizontal irradiance—to provide robust support for resilience assessment of energy systems.

Technology Category

• Reasoning under Uncertainty: Uncertainty Representations
• Machine Learning: Calibration & Uncertainty Quantification
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Web Mining and Content Analysis: Web data generation and simulation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

Extreme weather poses a large risk to critical energy systems (Ekisheva, Rieder, Norris, Lauby, & Dobson 2021; Levin, Botterud, Mann, Kwon, & Zhou 2022). Uncertainty quantification of negative impacts is important for developing resilience, especially during compound extreme weather events involving multiple climate variables. We leverage BMW-GAM (Economou & Garry 2022), a copula workflow that relies on fitting marginal distributions with Bayesian generalized additive models in moving windows -- an embarrassingly parallel task. The Gaussian copula has separable multivariate space-time correlation, allowing for efficient emulation and likelihood fitting with big datasets. Overall, the formulation is interpretable and provides uncertainty quantification through probabilistic simulations of weather variables during extreme events. Our method is illustrated in an analysis of temperature, wind speed, and global horizontal irradiance from Argonne National Laboratory's high-fidelity climate model output ADDA.

Problem

• uncertainty quantification
• critical energy systems
• compound extremes
• extreme weather
• climate variables

Innovation

• uncertainty quantification
• compound extremes
• Bayesian generalized additive models
• Gaussian copula
• energy system resilience