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A novel maximum likelihood based probabilistic behavioral data fusion algorithm for modeling residential energy

Tanmoy Bhowmik1, Naveen Chandra Iraganaboina2, Naveen Eluru2

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This study introduces a novel probabilistic data fusion method to enhance residential energy consumption models. By integrating diverse datasets, the approach improves model accuracy and predictive capabilities for energy use.

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Area of Science:

  • Energy Science
  • Data Science
  • Statistical Modeling

Background:

  • Residential energy consumption modeling relies on diverse, often disparate, data sources.
  • Existing methods struggle to effectively integrate these varied datasets, limiting model accuracy.
  • The Residential Energy Consumption Survey (RECS) and National Household Travel Survey (NHTS) offer valuable but distinct information.

Purpose of the Study:

  • To propose a novel maximum likelihood-based probabilistic data fusion approach for modeling residential energy consumption.
  • To enhance energy use models by integrating data from RECS and NHTS.
  • To demonstrate improved model fit and predictive capability through data fusion.

Main Methods:

  • Developed a probabilistic data fusion algorithm using maximum likelihood estimation.
  • Employed a flexible differential weighting method based on attribute similarity for data integration.
  • Fused RECS and NHTS datasets to create an enriched dataset for energy modeling.
  • Estimated updated residential energy use models using the fused dataset.

Main Results:

  • The fused dataset significantly improved model fit measures compared to models using RECS data exclusively.
  • Weighted contribution estimation highlighted the value of integrated data.
  • Validation exercises confirmed enhanced explanatory power and predictive capability of the fusion algorithm.
  • The proposed approach demonstrated applicability beyond energy consumption, including mobility and ridehailing pattern analysis.

Conclusions:

  • The probabilistic data fusion approach effectively integrates disparate data sources for improved residential energy consumption modeling.
  • The method offers enhanced explanatory and predictive power, outperforming models based on single datasets.
  • This fusion technique has broad applicability in various sectors analyzing energy-influencing patterns.