Cluster analysis of simulated energy use for LEED certified U.S. office buildings

Abstract This study uses cluster analysis to examine simulated energy consumption of 134 U.S. LEED NC office buildings to classify buildings into high, medium, and low energy use intensity clusters. The analysis uses energy simulation results from the LEED database, as a comparably large data set of energy end uses from sub-meter data does not yet exist. The difference between the low energy use cluster and other clusters is explained mostly by lower process loads and lower heating energy intensity, and partly by lower intensities of other HVAC related end-uses. Lighting energy use shows the least variation between clusters. The lower heating energy intensity in the low intensity cluster is largely explained by lower roof U-values, lower window-to-wall ratio, and smaller building size. Unregulated process loads are the most significant contributor to total building energy use, accounting for 36%, 33%, and 31% of the energy use in the high, medium, and low intensity clusters, respectively. This analysis provides a quantitative evaluation of the large difference in energy intensities in high-performance office buildings, showing that these buildings are dominated by internal loads, especially unregulated process loads.

[1]  Cathy Turner,et al.  Green Building Performance Evaluation: Measured Results from LEED-New Construction Buildings , 2008 .

[2]  John H. Scofield,et al.  Efficacy of LEED-certification in reducing energy consumption and greenhouse gas emission for large New York City office buildings , 2013 .

[3]  G. Mihalakakou,et al.  Using principal component and cluster analysis in the heating evaluation of the school building sector , 2010 .

[4]  Paul Torcellini,et al.  Reducing Plug and Process Loads for a Large Scale, Low Energy Office Building: NREL's Research Support Facility; Preprint , 2011 .

[5]  Steven J. Emmerich,et al.  Multizone airflow models for calculating infiltration rates in commercial reference buildings , 2013 .

[6]  Andrew K. Persily,et al.  Indoor air quality analyses of commercial reference buildings , 2012 .

[7]  Balaji Rajagopalan,et al.  Model-predictive control of mixed-mode buildings with rule extraction , 2011 .

[8]  Mohammad Heidarinejad,et al.  Relative significance of heat transfer processes to quantify tradeoffs between complexity and accuracy of energy simulations with a building energy use patterns classification , 2014 .

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

[10]  B. Tabachnick,et al.  Using Multivariate Statistics , 1983 .

[11]  J. New,et al.  Evaluation of weather datasets for building energy simulation , 2012 .

[12]  Neil Brown,et al.  Novel instrumentation for monitoring after-hours electricity consumption of electrical equipment, and some potential savings from a switch-off campaign. , 2012 .

[13]  Yi Jiang,et al.  The reality and statistical distribution of energy consumption in office buildings in China , 2012 .

[14]  Enrico Fabrizio,et al.  Reference buildings for cost optimal analysis: Method of definition and application , 2013 .

[15]  S. Wilcox,et al.  Users Manual for TMY3 Data Sets (Revised) , 2008 .

[16]  S. Poll,et al.  Pilot Study of a Plug Load Management System: Preparing for Sustainability Base , 2012, 2012 IEEE Green Technologies Conference.

[17]  Alan H. Fielding,et al.  Cluster and Classification Techniques for the Biosciences , 2006 .

[18]  Bing Liu,et al.  U.S. Department of Energy Commercial Reference Building Models of the National Building Stock , 2011 .

[19]  Mohammad S. Al-Homoud.  Optimum thermal design of office buildings , 1997 .

[20]  Fu Xiao,et al.  Quantitative energy performance assessment methods for existing buildings , 2012 .

[21]  Ke Xu,et al.  HOURLY PLUG LOAD MEASUREMENTS AND PROFILES FOR A MEDIUM SIZED OFFICE BUILDING - A CASE STUDY , 2013 .

[22]  David Hsu,et al.  Characterizing Energy Use in New York City Commercial and Multifamily Buildings , 2012 .

[23]  Kristofer S. J. Pister,et al.  COTS-based stick-on electricity meters for building submetering , 2013, 2013 IEEE SENSORS.

[24]  Nelson Fumo,et al.  Methodology to estimate building energy consumption using EnergyPlus Benchmark Models , 2010 .

[25]  F. Bryant,et al.  Principal-components analysis and exploratory and confirmatory factor analysis. , 1995 .

[26]  Andreea Maura Monica Hamilton.  LEED and historic preservation : a study of USGBC’s LEED rating system for new construction and major renovations as it pertains to historic building renovations , 2012 .

[27]  H. Kaiser.  An index of factorial simplicity , 1974 .

[28]  Luis Pérez-Lombard,et al.  A review on buildings energy consumption information , 2008 .

[29]  Young Tae Chae,et al.  Building energy performance evaluation of building integrated photovoltaic (BIPV) window with semi-transparent solar cells , 2014 .

[30]  John H. Scofield,et al.  Do LEED-certified buildings save energy? Not really… , 2009 .

[31]  Matthew Dahlhausen.  Staging Building Energy Retrofits , 2014 .

[32]  Ke Xu.  ASSESSING THE MINIMUM INSTRUMENTATION TO WELL TUNE EXISTING MEDIUM SIZED OFFICE BUILDING ENERGY MODELS , 2012 .