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This work presents the “Data-driven Accumulated Nonlinear Imputation” (DANLI) method that combines energy time series consumption profiles with pattern-matching to reconstruct missing values. It is designed for scenarios with only boundary measurements and is based on the hypothesis that cumulative power and daily usage similarities matter more than instantaneous power consumption in many applications. Results indicate that compared to statistical imputation, interpolation, and point-wise Euclidean pattern-matching, DANLI generates physically plausible reconstructions while achieving competitive accuracy across gaps.

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Data-Driven Power Time Series Imputation Using Cumulative Pattern Matching

  • 会议论文

  • 发表日期：

    2026年

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  • Jorge A. Silva-Castro,
  • Jorge Á. González-Ordiano,
  • Milagros Santos-Moreno,
  • Luis Cortés-Muñoz,
  • Diego Márquez-Esqueda

摘要

This work presents the “Data-driven Accumulated Nonlinear Imputation” (DANLI) method that combines energy time series consumption profiles with pattern-matching to reconstruct missing values. It is designed for scenarios with only boundary measurements and is based on the hypothesis that cumulative power and daily usage similarities matter more than instantaneous power consumption in many applications. Results indicate that compared to statistical imputation, interpolation, and point-wise Euclidean pattern-matching, DANLI generates physically plausible reconstructions while achieving competitive accuracy across gaps.