Decentralized Importance Sampling in Variational Autoencoders to Generate Industrial Scenarios
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Decarbonization of industrial systems increasingly relies on stochastic optimization models that require high-quality scenarios to represent uncertainty in renewable energy and process operations. In practice, however, the data needed to construct such scenarios are often siloed across stakeholders and may be heterogeneous, making centralized scenario generation difficult. In this talk, we present a decentralized framework based on variational autoencoders (VAEs), SplitVAEs, and importance-weighted autoencoders (IWAEs) to generate industrial scenarios together with their probability masses for stochastic programming applications. Through experiments on solar and wind energy data in Texas, we show that the proposed decentralized approach retains essential spatiotemporal information, produces scenario quality comparable to centralized methods, and achieves favorable computational performance. These results suggest that decentralized deep generative models offer a practical and privacy-aware pathway for large-scale industrial scenario generation.