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This content will become publicly available on December 15, 2025

Title: SplitVAEs: Decentralized scenario generation from siloed data for stochastic optimization problems
—Stochastic optimization problems in large-scale multi-stakeholder networked systems (e.g., power grids and supply chains) rely on data-driven scenarios to encapsulate complex spatiotemporal interdependencies. However, centralized aggregation of stakeholder data is challenging due to the existence of data silos resulting from computational and logistical bottlenecks. In this paper, we present SplitVAE, a decentralized scenario generation framework that leverages variational autoencoders to generate high-quality scenarios without moving stakeholder data. With the help of experiments on distributed memory systems, we demonstrate the broad applicability of SplitVAEs in a variety of domain areas that are dominated by a large number of stakeholders. Furthermore, these experiments indicate that SplitVAEs can learn spatial and temporal interdependencies in large-scale networks to generate scenarios that match the joint historical distribution of stakeholder data in a decentralized manner. Lastly, the experiments demonstrate that SplitVAEs deliver robust performance compared to centralized, state-ofthe-art benchmark methods while significantly reducing data transmission costs, leading to a scalable, privacy-enhancing alternative to scenario generation.  more » « less
Award ID(s):
2348411
PAR ID:
10637882
Author(s) / Creator(s):
; ;
Publisher / Repository:
IEEE
Date Published:
ISSN:
2573-2978
ISBN:
979-8-3503-6248-0
Page Range / eLocation ID:
938 to 948
Format(s):
Medium: X
Location:
Washington, DC, USA
Sponsoring Org:
National Science Foundation
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