Residential retrofits at scale: opportunity identification, saving estimation, and personalized messaging based on communicating thermostat data

  • Michael ZeifmanEmail author
  • Amine Lazrak
  • Kurt Roth


Basic insulation and heating system retrofits of existing homes could achieve annual energy savings of up to $4–5 billion in the USA. However, current US utility energy efficiency (EE) programs are costly and challenging to scale. Customer acquisition occurs primarily through energy bill mailers, mass media, and on-line advertising that lack specificity about particular home retrofit opportunities, expected energy savings, and cost-effectiveness. Specific retrofit opportunities are identified via on-site home energy assessments that can be inconvenient to homeowners, expensive, and of variable accuracy. Our paper discusses using communicating thermostat data to significantly increase the customer uptake of energy conservation measures (ECMs) by identifying homes with the most significant retrofit opportunities, estimating post-retrofit energy savings, and formulating home-specific outreach. We extended our previously developed gray-box building model to identify physical building parameters corresponding to the target retrofit opportunities, i.e., whole-home R value, air leakage characteristics, and heating system efficiency. The estimated R values and air leakage characteristics compare favorably with the ground truth. The validated algorithms can calculate home-specific energy savings estimate for each ECM, and the algorithm outputs can be used by utility EE programs to formulate home-specific retrofit offers.


Communicating thermostat Gray-box model Estimation Building thermal property R value ACH50 Home energy assessment Energy audit 



The work presented in this paper is funded in part by the U.S. Department of Energy Building America Program under Contract #DE-EE 0007571. We would like to extend our gratitude to Eric Werling, Director, and to Lena Burkett, Technical Research Manager of Building America Program for their support and fruitful discussions.

We would like to thank our utility partners for their relentless work on data retrieval, cleaning and anonymizing, and for their patient and persistent work with the CT and HEA vendors. In particular, we thank Brian Greenfield, Peter Klint, and Peter Kuhn of Eversource and Brenda Pike, Cassandra Vickers, and Rick Wester of National Grid.

Finally, we thank Dr. Iain Walker, of Lawrence Berkeley National Laboratory, for helpful discussions about modeling infiltration, and Bryan Urban and Duncan Howes for meaningful discussions and data processing.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest


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Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Fraunhofer USA Center for Manufacturing Innovation CMIBrooklineUSA

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