Online Edge Flow Imputation on Networks

Journal article (2022)

Authors

Rohan Money University of Agder

Joshin Krishnan Simula Metropolitan Center for Digital Engineering

Baltasar Beferull-Lozano University of Agder

E. Isufi

DOI: https://doi.org/10.1109/LSP.2022.3221846

Optimization Line Graph Time series analysis Reactive power Kalman filters Laplace equations Signal processing algorithms Missing Flow Imputation Signal reconstruction Simplicial Complex Topological Signal Processing

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Published Date

2022

Language

English

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Abstract

An online algorithm for missing data imputation for networks with signals defined on the edges is presented. Leveraging the prior knowledge intrinsic to real-world networks, we propose a bi-level optimization scheme that exploits the causal dependencies and the flow conservation, respectively via <italic>(i)</italic> a sparse line graph identification strategy based on a group-Lasso and <italic>(ii)</italic> a Kalman filtering-based signal reconstruction strategy developed using simplicial complex (SC) formulation. The advantages of this first SC-based attempt for time-varying signal imputation have been demonstrated through numerical experiments using EPANET models of both synthetic and real water distribution networks.

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