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Towards City-Scale Building Energy Performance Benchmarking: Poster Abstract

Published: 16 November 2016 Publication History

Abstract

Cities across the country (20 to date) are rapidly passing laws to mandate the collection and disclosure of energy usage data with the hopes that such data could be utilized to benchmark building energy performance and provide a basis for designing and deploying efficiency measures. We present an integrated data-driven method based on recursive partitioning and stochastic frontier analysis to benchmark energy performance of the building stock across an entire city. We implement the proposed method to over 10,000 buildings in New York City. The preliminary results quantify the potential for building energy efficiency and support policymakers for further examining effects of interventions.

References

[1]
Department of Energy. Better Buildings. Updated: 2014. Available at: https://betterbuildingssolutioncenter.energy.gov
[2]
Institute for Market Transformation. Map: U.S. building benchmarking and transparency policies. Updated: 2015. Available at: http://www.imt.org/resources/detail/map-u.s.-building-benchmarking-policies.
[3]
Breiman L., Friedman J., Stone C.J., Olshen R.A. 1984. Classification and regression trees. CRC press.
[4]
Coelli T. 1995. Estimators and hypothesis tests for a stochastic frontier function: a Monte Carlo analysis. Journal of productivity analysis. 6, 3, 247--268.

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cover image ACM Conferences
BuildSys '16: Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments
November 2016
273 pages
ISBN:9781450342643
DOI:10.1145/2993422
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

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Published: 16 November 2016

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