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An Information-Theoretic Analysis of High-Frequency Load Disaggregation.

Gabriel Arquelau Pimenta Rodrigues1, André Luiz Marques Serrano1,2, Geraldo Pereira Rocha Filho1,3

  • 1Department of Electrical Engineering, University of Brasilia, Brasília 70910-900, Brazil.

Entropy (Basel, Switzerland)
|March 28, 2026
PubMed
Summary
This summary is machine-generated.

This study uses information theory to analyze non-intrusive load monitoring (NILM) data. High mutual information between an appliance and aggregate signals predicts better disaggregation performance, aiding in difficulty assessment.

Keywords:
NILMentropyinformation theorymutual informationrandom forest

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Area of Science:

  • Electrical Engineering
  • Information Theory
  • Machine Learning

Background:

  • Non-intrusive load monitoring (NILM) uses machine learning for appliance disaggregation from aggregate signals.
  • Current methods lack transparency into the aggregate signal's information structure.

Purpose of the Study:

  • Model NILM as a coding-decoding process using information-theoretic measures.
  • Quantify uncertainty, recoverability, temporal contribution, and masking effects in aggregate signals.
  • Characterize disaggregation difficulty and appliance observability prior to model training.

Main Methods:

  • Applied information-theoretic measures (transfer entropy, conditional mutual information) to NILM data.
  • Modeled NILM as a coding-decoding process.
  • Validated findings using Random Forest regression with Minimum Redundancy Maximum Relevance feature selection.

Main Results:

  • Transfer entropy indicated negligible temporal gains, supporting pointwise models.
  • Conditional mutual information revealed asymmetric masking, with laptop chargers as dominant interferers.
  • Mutual information between an appliance and the aggregate signal predicts disaggregation performance.
  • High mutual information appliances (hair dryer, water heater) showed lower errors; others (iron) were harder to recover.
  • Strong negative correlation (Spearman rs=-0.81, p=0.015) between normalized mutual information and disaggregation error.

Conclusions:

  • Information-theoretic analysis effectively characterizes NILM disaggregation difficulty.
  • Mutual information is a strong predictor of appliance disaggregation performance.
  • This approach aids in assessing appliance observability in high-frequency NILM data.