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Objective Energy Performance Assessment Using Data Envelopment Analysis (DEA)
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 Title & Authors
Objective Energy Performance Assessment Using Data Envelopment Analysis (DEA)
Yoon, Seong-Hwan; Park, Cheol-Soo;
Objective energy performance assessment of buildings is crucial for building stakeholders' rational decision making. One of the most popular building energy performance measures is Energy Use Intensity (EUI, kwh/m2.yr). This has been widely used since it is straightforward, simple and easy to understand. However, it has a severe drawback that it only shows the number of consumed energy per unit floor area and can't represent objective energy performance of a building. In other words, EUI does not deliver how well a building serves occupants and provides satisfactory services (e.g. thermal comfort). It is often misinterpreted in a way that the less EUI, the better building energy performance is. In this paper, a Data Envelopment Analysis (DEA) was applied to assess objective building energy performance. The DEA quantifies performance of a given system when multiple inputs and outputs are given. The DEA is a data-oriented and non-parametric method. Thus, it does not require any energy model and can consider multivariate inputs/outputs simultaneously. For the study, a number of virtual buildings were generated out of Monte-Carlo sampling and then simulated using EnergyPlus to derive a data set. Energy consumption was used as an input and building service levels (e.g. occupancy density [people/m2], operation time [hrs/yr], thermal comfort [PPD]) were used as outputs. It is shown that the DEA is a more objective and rational performance assessment method than the EUI, and can be a good alternative for building energy performance evaluation.
Building Energy;Energy benchmarking;Energy Use Intensity (EUI);Data Envelopment Analysis (DEA);
 Cited by
Development of a Profiling System for Energy Performance Assessment of Existing Buildings, Journal of the Architectural Institute of Korea Structure & Construction, 2016, 32, 12, 77  crossref(new windwow)
Andes, S., Metzger, L. M., Kralewski, J., & Gans, D. (2002). Measuring efficiency of physician practices using data envelopment analysis, Managed Care, 8(11), 48-54. crossref(new window)

ASHRAE (2007). Standard 90.1 Energy standard for buildings except low-rise residential buildings, Atlanta, USA

ASHRAE (2013). ASHRAE Handbook fundamentals, Atlanta

Burhenne, S., Jacob, D., & Henze, G. P. (2010). Uncertainty analysis in building simulation with Monte Carlo techniques, SimBuild 2010, 4th National Conference of IBPSA-USA, New York City

Charnes, A., Cooper, W. W., Lewin, A. Y., & Seiford, L. M. (1994). Data Envelopment Analysis: Theory, Methodology and Application, Boston: Kluwer

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the Efficiency of Decision Making Units, European Journal of Operational Research, Amsterdam, 2(6), 429-444. crossref(new window)

CIBSE, Guide A (2006). Guide A: Environmental Design, Chartered Institution of Building Services Engineers, London

Cooper, W. W., Seiford, L. M., & Zhu, J. (2011). Handbook on Data Envelopment Analysis. 2nd ed., Kluwer Academic Publishers: Massachusetts.

Deru, M., Field, K., Studer, D., Benne, K., Griffith, B., Torcellini, P., Liu B., Halverson, M., Winiarski, D., Rosenberg, M., Yazdanian, M., Huang, J., & Crawley, D. (2011). U.S. Department of Energy Commercial Reference Building Models of the National Building Stock, National Renewable Energy Laboratory

DOE (1995). Energy Information Administration. Measuring energy efficiency in the United States' economy: a beginning. Washington, DC: United States Department of Energy, Energy Information Administration, from

DOE (2000). Energy Information Administration. Energy efficiency measurement discussion. Washington, DC: United States Department of Energy, Energy Information Administration, from

DOE (2012). EnergyPlus Input Output Reference: The Encyclopedic Reference to EnergyPlus Input and Output, US Department of Energy

EN 15217 (2008). Energy performance of buildings-Methods for expressing energy performance and the energy certification of buildings

EPA (2015). ENERGY STAR Benchmarking, U.S. Environmental Protection Agency, from

Farrell, M. (1957). The measurement of productive efficiency, Journal of the Royal Statistical Society, 120(3), 253-290. crossref(new window)

Heiselberg, P., Brohus, H., Hesselholt, A., Rasmussen, H., Seinre, E., & Thomas, S. (2009). Application of sensitivity analysis in design of sustainable buildings, Renewable Energy, 34, 2030-2036. crossref(new window)

Heo, Y. S. (2011). Bayesian calibration of building energy models for energy retrofit decision-making under uncertainty, Ph.D. thesis, Georgia Institute of Technology, Atlanta, GA. USA

Hinge, A., Rutherford, J., Abrey, D., daSilva, J., Titus, E., & Smyth, E. (2002). Back to School on Energy Benchmarking, Proceedings of the ACEEE 2002 Summer Study on Energy Efficiency in Buildings

Hopfe, C. J. (2009). Uncertainty and sensitivity analysis in building performance simulation for decision support and design optimization, Ph.D. thesis, Technische Universiteit Eindhoven

Jones, J. R., & Boonyatikarn, S. (1990). Factors influencing overall building efficiency, ASHRAE Transactions, 96, 1449-1458.

KEMCO (2011). The Guide to Energy Saving Design Standard, Korea Energy Management Corporation

Kim, S. H. (2012). Air Conditioning System, Geongiwon Book Publishing.

Kinney, S. and Piette, M. A. (2002). Development of a California Commercial Building Energy Benchmarking Database, Lawrence Berkeley National Laboratory Report LBNL-50676, Berkeley, California, Presented at the 2002 ACEEE Summer Study

Kinney, S. and Piette, M. A. (2003). High performance commercial building systems, California commercial building energy benchmarking final project report, Lawrence Berkeley National Laboratory

Korolija, I., Zhang, Y., Marjanovic-Halburd, L., & Hanby, V. (2013). Regression models for predicting UK office building energy consumption from heating and cooling demands, Energy and Buildings, 59, 214-227. crossref(new window)

Lam, J. C., Tsang, C. L., & Yang, L. (2006). Impacts of lighting density on heating and cooling loads in different climates in China, Energy Convers Manage, 47, 1942-1953. crossref(new window)

Lee, W. S. (2008). Benchmarking the energy efficiency of government buildings with data envelopment analysis, Energy and Buildings, 40, 891-895. crossref(new window)

MOL (2012). Business Labor force Survey, Ministry of Employment and Labor in South Korea, from

MOLIT (2014). Status of Buildings, Ministry of Land, Infrastructure and Transport, from

MOTIE (2013). Notification about the limitations of energy use, Ministry of Trade, Industry and Energy

Nataraja, N. R., & Johnson, A. L. (2011). Guidelines for using variable selection techniques in data envelopment analysis, European Journal of Operational Research, 215, 662-669 crossref(new window)

Onut, S., & Soner, S. (2006). Energy efficiency assessment for the Antalya Region hotels in Turkey, Energy and Buildings, 38, 964-971. crossref(new window)

Perera, M. D. A. E. S., Henderson, J., & Webb, B. C. (1997). Simple air leakage predictor for office buildings: assessing envelope airtightness during design or before refurbishment, Proceedings of CIBSE National Conference, 21-26

Prime Minister's Office (2012). Power Supply and Energy Saving Plan for Winter, South Korea

Ramanathan, R. (2003). An Introduction to Data Envelopment Analysis: A Tool for Performance Measurement, Sage Publication Ltd.

Rhodes, E. L. (1978). Data Envelopment Analysis and Approaches for Application to Program Follo-Through in U.S. Education, Carnegie Mellon University

Sarkis, J. (2007). Preparing your data for DEA, In ZHU, J., & Cook, W. D. (eds.) Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis, Worcester Polytechnic Institute, New York, Springer

Talluri, S. (2000). Data envelopment analysis: Models and Extension, Decision Line, 8-11.

Tavares, P. F. A. F., & Martins, A. M. O. G. (2007). Energy efficient building design using sensitivity analysis-a case study, Energy and Buildings, 39, 23-31. crossref(new window)

Yoon, S. H., & Park, C. S. (2014). x-Ray Approach to Develop Energy Model for Existing Buildings, Journal of the Architectural Institute of Korea, Planning and Design Section, 30(1), 235-244.

Yu, F. W., & Chan, K. T. (2013). Energy management of chiller systems by data envelopment analysis. Facilities, 31(3/4), 106-118. crossref(new window)

Yue, P. (1994). Data Envelopment Analysis and Commercial Bank Performance: A Primer with Applications to Missouri Banks, Federal Reserve Bank of St Louis Review, 74(1), 31-45.