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A data envelopment analysis (DEA) model for building energy benchmarking

Baabak Ashuri (Economics of the Sustainable Built Environment (ESBE) Lab, Building Construction/Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA)
Jun Wang (Economics of the Sustainable Built Environment (ESBE) Lab,Georgia Institute of Technology, Atlanta, Georgia, USA)
Mohsen Shahandashti (Department of Civil Engineering, University of Texas at Arlington, Arlington, Texas, USA)
Minsoo Baek (Economics of the Sustainable Built Environment (ESBE) Lab, Georgia Institute of Technology, Atlanta, Georgia, USA)

Journal of Engineering, Design and Technology

ISSN: 1726-0531

Article publication date: 30 April 2019

Issue publication date: 5 August 2019




Building energy benchmarking is required for adopting an energy certification scheme, promoting energy efficiency and reducing energy consumption. It demonstrates the current level of energy consumption, the value of potential energy improvement and the prospects for additional savings. This paper aims to create a new data envelopment analysis (DEA) model that overcomes the limitations of existing models for building energy benchmarking.


Data preparation: the findings of the literature search and subject matter experts’ inputs are used to construct the DEA model. Particularly, it is ensured that the included variables would not violate the fundamental assumption of DEA modeling, DEA convexity axiom. New DEA formulation: controllable and non-controllable variables, e.g. weather conditions, are differentiated in the new formulation. A new approach is used to identify outliers to avoid skewing the efficiency scores for the rest of the buildings under consideration. Efficiency analysis: three distinct efficiencies are computed and analyzed in benchmarking building energy: overall, pure technical, and scale efficiency.


The proposed DEA approach is successfully applied to a data set provided by a utility management and energy services company that is active in the multifamily housing industry. Building characteristics and energy consumption of 124 multifamily properties in 15 different states in the USA are found in the data set. Buildings in this data set are benchmarked using the new DEA energy benchmarking formulation. Building energy benchmarking is also conducted in a time series manner showing how a particular building performs across the period of 12 months compared with its peers.


The proposed research contributes to the body of knowledge in building energy benchmarking through developing a new outlier detection method to mitigate the impact of super-efficient and super-inefficient buildings on skewing the efficiency scores of the other buildings; avoiding ratio variables in the DEA formulation to adhere to the convexity assumption that existing DEA methods do not follow; and distinguishing between controllable and non-controllable variables in the DEA formulation. This research contributes to the state of practice through providing a new energy benchmarking tool for facility managers and building owners that strive to relatively rank the energy-efficiency of their properties and identify low-performing properties as investment targets to enhance energy efficiency.



This material is based upon work supported by the National Science Foundation under Grants nos. 1300918 and 1441208. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.


Ashuri, B., Wang, J., Shahandashti, M. and Baek, M. (2019), "A data envelopment analysis (DEA) model for building energy benchmarking", Journal of Engineering, Design and Technology, Vol. 17 No. 4, pp. 747-768.



Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

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