Scholarlink

We present a model-based method to disaggregate residential smart meter data into behind-the-meter (BTM) production and consumption profiles without requiring PV measurements. The approach fits a PV system model to feed-in power under clear-sky conditions, minimizing ramp-period loss while enforcing peak-time validity constraints, and leverages high-resolution solar radiation data to reconstruct accurate production and consumption profiles. Evaluation on 33 households from the Pecan Street dataset yields normalized mean absolute errors of 0.038 for production and 0.031 for consumption relative to ground truth. The proposed method is non-intrusive and explainable, enabling smart meter data applications for grid management, balancing and demand-side flexibility.

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Disaggregating Household Electricity Production and Consumption from Smart Meter Data

  • 会议论文

  • 发表日期：

    2026年

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  • Benoit De Vrieze,
  • Hossein Tabari,
  • Peter Hellinckx

摘要

We present a model-based method to disaggregate residential smart meter data into behind-the-meter (BTM) production and consumption profiles without requiring PV measurements. The approach fits a PV system model to feed-in power under clear-sky conditions, minimizing ramp-period loss while enforcing peak-time validity constraints, and leverages high-resolution solar radiation data to reconstruct accurate production and consumption profiles. Evaluation on 33 households from the Pecan Street dataset yields normalized mean absolute errors of 0.038 for production and 0.031 for consumption relative to ground truth. The proposed method is non-intrusive and explainable, enabling smart meter data applications for grid management, balancing and demand-side flexibility.