Importance Sampling in Variational Autoencoders to Generate Industrial Carbon Emissions Scenarios
Date:
Concerns about the negative environmental impact of fossil fuels have intensified interest in decarbonization, with renewable energy playing a central role despite substantial uncertainties in supply and demand. Stochastic programming provides a principled framework for decision-making under such uncertainty, but its performance depends critically on the quality of generated scenarios and their associated probability masses. In this talk, we explore the use of variational autoencoders (VAEs), together with decentralized variants and importance-weighted autoencoders (IWAEs), for generating high-fidelity industrial carbon-emissions scenarios. Using industrial-sector CO$_2$ emissions data from Texas oil refineries, we demonstrate that these generative models can preserve key spatial and temporal structures while also supporting the estimation of scenario likelihoods required for stochastic optimization. The results highlight the promise of deep generative models as scalable tools for scenario generation in decarbonization-driven industrial decision-making.