A comparative study of benchmarking approaches for non-domestic buildings : Part 1 – Top-down approach

Benchmarking plays an important role in improving energy efficiency of non-domestic buildings. A review of energy benchmarks that underpin the UK’s Display Energy Certificate (DEC) scheme have prompted necessities to explore the benefits and limitations of using various methods to derive energy benchmarks. The existing methods were reviewed and grouped into top-down and bottom-up approaches based on the granularity of the data used. In the study, two top-down methods, descriptive statistics and artificial neural networks (ANN), were explored for the purpose of benchmarking energy performances of schools. The results were used to understand the benefits of using these benchmarks for assessing energy efficiency of buildings and the limitations that affect the robustness of the derived benchmarks. Compared to the bottom-up approach, top-down approaches were found to be beneficial in gaining insight into how peers perform. The relative rather than absolute feedback on energy efficiency meant that peer pressure was a motivator for improvement. On the other hand, there were limitations with regard to the extent to which the energy efficiency of a building could be accurately assessed using the top-down benchmarks. Moreover, difficulties in acquiring adequate data were identified as a key limitation to using the top-down approach for benchmarking non-domestic buildings. The study suggested that there are benefits in rolling out of DECs to private sector buildings and that there is a need to explore more complex methods to provide more accurate indication of energy efficiency in non-domestic buildings.

[1]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

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

[3]  William Chung,et al.  Review of building energy-use performance benchmarking methodologies , 2011 .

[4]  Melek Yalcintas,et al.  An energy benchmarking model based on artificial neural network method with a case example for tropical climates , 2006 .

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

[6]  Wen-Shing Lee,et al.  Using climate classification to evaluate building energy performance , 2011 .

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

[8]  M. N. Assimakopoulos,et al.  Using intelligent clustering techniques to classify the energy performance of school buildings , 2007 .

[9]  Dejan Mumovic,et al.  A comparative study of benchmarking approaches for non-domestic buildings : Part 2 – Bottom-up approach , 2014 .

[10]  Peng Zhou,et al.  A survey of data envelopment analysis in energy and environmental studies , 2008, Eur. J. Oper. Res..

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

[12]  Koen Steemers,et al.  Using Display Energy Certificates to quantify schools' energy consumption , 2011 .

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

[14]  R. Sharp,et al.  Benchmarking Energy Use in Schools , 1998 .

[15]  Dejan Mumovic,et al.  Improved benchmarking comparability for energy consumption in schools , 2014 .

[16]  E. Barbier Green Growth , 2020, Encyclopedia of the UN Sustainable Development Goals.

[17]  Dejan Mumovic,et al.  Determinants of energy use in UK higher education buildings using statistical and artificial neural network methods , 2012 .

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

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

[20]  Sandhya Samarasinghe,et al.  Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition , 2006 .

[21]  Jones,et al.  CIBSE review of energy benchmarks for Display Energy Cerificates , 2011 .