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. However, numerous municipalities are struggling to translate such data into actionable insights on which buildings are the best candidates for energy efficiency interventions. Current studies are limited in their ability to compare energy efficiency of buildings at the city scale and maintain interpretability necessary to result in effective decision making that can facilitate municipal policy and program design. In this paper, we present an integrated data-driven method based on recursive partitioning and stochastic frontier analysis to benchmark energy performance of building stock across an entire city. We implement the proposed method on a dataset of over 10,000 buildings in New York City. The preliminary results begin to quantify the potential for building energy efficiency and give further support to city officials for examining effects of possible interventions. We aim to establish a robust methodology that provides a standard way to benchmark building energy performance at the city scale, which can be easily applied to other cities across the country.
['Zheng Yang', 'Jonathan E. Roth', 'Rishee K. Jain']
Data-driven benchmarking of building energy performance at the city scale