Review of building energy-use performance benchmarking methodologies

This paper is to review what kinds of mathematical methods used in developing benchmarking systems, to discuss the properties of the methods, and to classify two kinds of benchmarking systems based on their properties. We find that while benchmarking systems are developed by using the energy-use performance of a significant number of reference buildings, benchmarking results can be used to encourage poor reference performers (in energy-efficiency) to improve their performance. On the other hand, because benchmarking systems also function as a public yardstick of energy-use performance in buildings, some regulators release benchmarking information to the media. This proves advantageous because it brings public pressure on owners/developers of poorly performing non-reference buildings. However, not all benchmarking systems can be used by public users (i.e., other non-reference building owners). Depending on whether the resulting benchmarking system can be used in public, we note that there are two kinds of benchmarking system: public benchmarking and internal benchmarking. These two types of benchmarking system are developed by different methods.

[1]  Peijun Guo,et al.  Fuzzy DEA: a perceptual evaluation method , 2001, Fuzzy Sets Syst..

[2]  Priyadarsini Rajagopalan,et al.  Building energy efficiency labeling programme in Singapore , 2008 .

[3]  P. Schmidt.  Frontier production functions , 1985 .

[4]  Mary Ann Piette,et al.  Development of a California commercial building benchmarking database , 2002 .

[5]  Qiang Zhang,et al.  Model-based benchmarking with application to laboratory buildings , 2002 .

[6]  John Burnett,et al.  Benchmarking energy use assessment of HK-BEAM, BREEAM and LEED , 2008 .

[7]  Tony N.T. Lam,et al.  Artificial neural networks for energy analysis of office buildings with daylighting , 2010 .

[8]  C. Filippín.  Benchmarking the energy efficiency and greenhouse gases emissions of school buildings in central Argentina , 2000 .

[9]  Alan Meier,et al.  Rating the energy performance of buildings , 2004 .

[10]  J. M. MacDonald.  Benchmarking Residential Energy Use , 2001 .

[11]  Harold O. Fried,et al.  The Measurement of Productive Efficiency and Productivity Growth , 2008 .

[12]  Dieter Gstach,et al.  Another Approach to Data Envelopment Analysis in Noisy Environments: DEA+ , 1998 .

[13]  William Chung,et al.  A study of energy efficiency of private office buildings in Hong Kong , 2009 .

[14]  W. Meeusen,et al.  Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error , 1977 .

[15]  Lawrence M. Seiford,et al.  Data envelopment analysis (DEA) - Thirty years on , 2009, Eur. J. Oper. Res..

[16]  Thomas Olofsson,et al.  Energy Performance for Buildings Evaluated with Multivariate Analysis , 2005 .

[17]  L. Simar,et al.  Conditional nonparametric frontier models for convex and nonconvex technologies: a unifying approach , 2007 .

[18]  A. Charnes,et al.  Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis , 1984 .

[19]  Lee Siew Eang,et al.  Benchmarking energy use and greenhouse gas emissions in Singapore's hotel industry , 2010 .

[20]  M. Farrell.  The Measurement of Productive Efficiency , 1957 .

[21]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[22]  Melek Yalcintas,et al.  An energy benchmarking model based on artificial neural network method utilizing US Commercial Buildings Energy Consumption Survey (CBECS) database , 2007 .

[23]  P. Bohdanowicz,et al.  Determinants and benchmarking of resource consumption in hotels : Case study of Hilton international and Scandic in Europe , 2007 .

[24]  A. B. Birtles,et al.  Energy efficiency of buildings: Simple appraisal method , 1997 .

[25]  Armando C. Oliveira,et al.  Effect of louver shading devices on building energy requirements , 2010 .

[26]  Victor C. H. Yeung,et al.  Implementing building energy codes in Hong Kong: energy savings, environmental impacts and cost , 2005 .

[27]  William Chung,et al.  Benchmarking the energy efficiency of commercial buildings , 2006 .

[28]  D. Reifschneider,et al.  SYSTEMATIC DEPARTURES FROM THE FRONTIER: A FRAMEWORK FOR THE ANALYSIS OF FIRM INEFFICIENCY* , 1991 .

[29]  J. Kenneth Monts,et al.  Assessing energy efficiency and energy conservation potential among commercial buildings: A statistical approach , 1982 .

[30]  D. Aigner,et al.  P. Schmidt, 1977,?Formulation and estimation of stochastic frontier production function models,? , 1977 .

[31]  R. Banker.  Maximum likelihood, consistency and data envelopment analysis: a statistical foundation , 1993 .

[32]  William W. Cooper,et al.  Evaluating Program and Managerial Efficiency: An Application of Data Envelopment Analysis to Program Follow Through , 1981 .

[33]  Wen-Shing Lee,et al.  Benchmarking the performance of building energy management using data envelopment analysis , 2009 .

[34]  Timo Kuosmanen,et al.  Data Envelopment Analysis as Nonparametric Least-Squares Regression , 2010, Oper. Res..

[35]  Tianzhen Hong,et al.  2012 ACEEE Summer Study on Energy Efficiency in Buildings PANEL 3—Commercial Buildings: Technologies, Design, Operations, Performance, and Building Industry Trends , 2012 .

[36]  W. Greene.  Maximum likelihood estimation of econometric frontier functions , 1980 .

[37]  William W. Cooper,et al.  Chapter 13 Satisficing DEA models under chance constraints , 1996, Ann. Oper. Res..

[38]  Patxi Hernandez,et al.  Development of energy performance benchmarks and building energy ratings for non-domestic buildings: An example for Irish primary schools , 2008 .

[39]  Mohammad. Rasul,et al.  Energy conservation measures in an institutional building in sub-tropical climate in Australia , 2010 .

[40]  Y. Liu,et al.  Effects of shelterbelt trees on reducing heating-energy consumption of office buildings in Scotland , 2008 .

[41]  Denise Young,et al.  The potential for energy efficiency gains in the Canadian commercial building sector: A stochastic frontier study , 2007 .

[42]  Wen-Shing Lee,et al.  Benchmarking the energy efficiency of government buildings with data envelopment analysis , 2008 .

[43]  Roberto Lamberts,et al.  REGRESSION ANALYSIS OF ELECTRIC ENERGY CONSUMPTION OF COMMERCIAL BUILDINGS IN BRAZIL , 2007 .

[44]  Rajiv D. Banker,et al.  A model to evaluate variables impacting the productivity of software maintenance projects , 1991 .

[45]  L. T. Wong,et al.  An energy benchmarking model for ventilation systems of air-conditioned offices in subtropical climates , 2007 .

[46]  Rui Kang,et al.  A new ranking method to fuzzy data envelopment analysis , 2010, Comput. Math. Appl..

[47]  P. W. Wilson,et al.  Estimation and Inference in Nonparametric Frontier Models: Recent Developments and Perspectives , 2013 .