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Data analytics for simplifying thermal efficiency planning in cities.

Mohammad Javad Abdolhosseini Qomi1, Arash Noshadravan2, Jake M Sobstyl3

  • 1Department of Civil and Environmental Engineering, University of California at Irvine, Irvine, CA 92617, USA.

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Summary

A new method uses gas bills and building footprints to identify homes with high heating energy-saving potential. Retrofitting just 16% of buildings could cut overall gas use by 40%.

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massive–passive data analyticsprobabilistic model reductionresponse surface methodologystrategic gas consumption planning

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Area of Science:

  • Building energy efficiency
  • Urban energy consumption analysis
  • Sustainable building retrofitting

Background:

  • Space heating and cooling constitute over 44% of US building energy use, contributing to 20% of national CO2 emissions.
  • Identifying high-potential buildings for energy savings is crucial for reducing environmental impact and costs.
  • Current detailed building analysis methods are often resource-intensive and slow.

Purpose of the Study:

  • To develop a novel, simplified inference method for estimating heating energy-saving potential in buildings.
  • To enable city-scale identification of buildings with the greatest energy-saving opportunities.
  • To inform policy and retrofitting strategies for improving urban building thermal efficiency.

Main Methods:

  • A novel inference method using a ranking algorithm to estimate heating energy savings.
  • Utilizing gas bill consumption records and building footprint data.
  • Statistical screening of weather, infrastructural, and resident variables to model building gas consumption and savings at a city scale.

Main Results:

  • The method simplifies energy loss calculations, reducing problem dimensionality.
  • Analysis of 6,200 buildings in Cambridge, MA, showed retrofitting 16% of buildings could yield a 40% reduction in total gas consumption.
  • Inferred heat loss rates follow a power-law distribution (Zipf's law), enabling optimized retrofitting paths.

Conclusions:

  • The developed method offers an efficient approach to identifying buildings with significant energy-saving potential.
  • Findings support targeted retrofitting strategies for maximizing gas savings in urban building stocks.
  • The research contributes to policy efforts aimed at reducing building energy consumption and greenhouse gas emissions.