A comprehensive review of energy-related data for U.S. commercial buildings

Abstract U.S. commercial buildings consumed around 18% of total primary energy in 2017 and a 2.23 EJ increase is expected by 2050. Energy-related data for commercial buildings can be used for various applications, including benchmarking, building component analysis, market potential analysis, and policy making. Although there are plenty of data sources for energy usage in commercial buildings, they have not been thoroughly reviewed and summarized. As a result, users do not have comprehensive guidelines about selections of right data sources for specific application needs. To fill this gap, this paper conducts a comprehensive review to summarize data sources for energy usage in U.S. commercial buildings and discuss their usages for different applications. First, the paper summarizes the survey and simulation data sources for energy usage. The data sources are compared in terms of their data collection methods, released information, and relevant features. Second, this paper analyzes the applications for different survey and simulation data sources. This review categorizes the applications of data sources into five categories, including energy performance benchmarks, energy use forecasts and predictions, energy use contributions of building components, supports of building energy policies, and urban-scale energy use analysis. Moreover, the paper introduces several cases to demonstrate the usages of these data sources.

[1]  R. Judkoff,et al.  Methodology for Modeling Building Energy Performance across the Commercial Sector , 2008 .

[2]  Wei Tian,et al.  A review of sensitivity analysis methods in building energy analysis , 2013 .

[3]  Enrico Franconi,et al.  Commercial heating and cooling loads component analysis , 1999 .

[4]  Na Wang,et al.  Development of building energy asset rating using stock modelling in the USA , 2018 .

[5]  E. Mills Building commissioning: a golden opportunity for reducing energy costs and greenhouse gas emissions in the United States , 2010 .

[6]  Hema Sree Rallapalli A Comparison of Energy Plus and eQUEST Whole Building Energy Simulation Results for a Medium Sized Office Building , 2010 .

[7]  Jian Sun,et al.  Calibration of Building Energy Simulation Programs Using the Analytic Optimization Approach (RP-1051) , 2006 .

[8]  J. J. Hirsch,et al.  DOE-2 supplement: Version 2.1E , 1993 .

[9]  Steven J. Emmerich,et al.  Integration of Airflow and Energy Simulation Using CONTAM and TRNSYS | NIST , 2003 .

[10]  Marilyn A. Brown,et al.  Machine learning approaches for estimating commercial building energy consumption , 2017 .

[11]  Norman Bourassa,et al.  Action-oriented Benchmarking: Using the CEUS Database to Benchmark Commercial Buildings in California , 2008 .

[12]  T. Agami Reddy,et al.  Calibrating Detailed Building Energy Simulation Programs with Measured Data—Part I: General Methodology (RP-1051) , 2007 .

[13]  Liu Yang,et al.  Zero energy buildings and sustainable development implications – A review , 2013 .

[14]  Michael D. Sohn,et al.  Big-data for building energy performance: Lessons from assembling a very large national database of building energy use , 2015 .

[15]  Tianzhen Hong,et al.  The BayREN Integrated Commercial Retrofits (BRICR) Project: An Introduction and Preliminary Results , 2018 .

[16]  Michael D. Sohn,et al.  A regression-based approach to estimating retrofit savings using the Building Performance Database , 2016 .

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

[18]  Emily M. Ryan,et al.  Validation of building energy modeling tools under idealized and realistic conditions , 2012 .

[19]  Joshua D. Kneifel,et al.  Life-cycle carbon and cost analysis of energy efficiency measures in new commercial buildings , 2010 .

[20]  Ronald E. Jarnagin ASHRAE Building EQ , 2009 .

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

[22]  Mohammad S. Al-Homoud,et al.  Computer-aided building energy analysis techniques , 2001 .

[23]  Na Wang,et al.  Commercial Building Energy Asset Score System: Program Overview and Technical Protocol (Version 1.0) , 2013 .

[24]  Yeonsook Heo,et al.  Calibration of building energy models for retrofit analysis under uncertainty , 2012 .

[25]  Amir Roth,et al.  DEnCity: An Open Multi-Purpose Building Energy Simulation Database , 2012 .

[26]  Xiufeng Pang,et al.  REAL TIME MODEL-BASED ENERGY DIAGNOSTICS IN BUILDINGS , 2011 .

[27]  Edward Morofsky,et al.  Effectiveness of single and multiple energy retrofit measures on the energy consumption of office bu , 2011 .

[28]  Chris Marnay,et al.  Electric storage in California’s commercial buildings , 2013 .

[29]  Joshua Ryan New,et al.  AUTOTUNE E+ BUILDING ENERGY MODELS , 2012 .

[30]  T. Agami Reddy,et al.  Literature review on calibration of building energy simulation programs : Uses, problems, procedures, uncertainty, and tools , 2006 .

[31]  M. Deru,et al.  Using DOE Commercial Reference Buildings for Simulation Studies: Preprint , 2010 .

[32]  Travis Walter,et al.  Big Data Analytics in the Building Industry , 2016 .

[33]  Lynne E. Parker,et al.  Calibrating building energy models using supercomputer trained machine learning agents , 2014, Concurr. Comput. Pract. Exp..

[34]  Peter Cappers,et al.  Demand Response for Ancillary Services , 2013, IEEE Transactions on Smart Grid.

[35]  Kwang Ho Lee,et al.  Advanced Benchmarking for Complex Building Types: Laboratories as an Exemplar , 2010 .

[36]  Zheng O'Neill,et al.  A methodology for meta-model based optimization in building energy models , 2012 .

[37]  Zheng O'Neill,et al.  Uncertainty and sensitivity decomposition of building energy models , 2012 .

[38]  Daniel Macumber,et al.  OpenStudio: An Open Source Integrated Analysis Platform , 2011 .

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

[40]  Steve Greenberg,et al.  Technology data characterizing space conditioning in commercial buildings: Application to end-use forecasting with COMMEND 4.0 , 1995 .

[41]  Daniel E. Fisher,et al.  EnergyPlus: creating a new-generation building energy simulation program , 2001 .

[42]  Christoph F. Reinhart,et al.  Urban building energy modeling – A review of a nascent field , 2015 .

[43]  Jon Hand,et al.  CONTRASTING THE CAPABILITIES OF BUILDING ENERGY PERFORMANCE SIMULATION PROGRAMS , 2008 .

[44]  Jian Zhang,et al.  Achieving the 30% Goal: Energy and Cost Savings Analysis of ASHRAE Standard 90.1-2010 , 2011 .

[45]  Terry R Sharp Derivation of Building Energy Use Intensity Targets for ASHRAE Standard 100 , 2014 .

[46]  Paul Torcellini,et al.  Assessment of the Energy Impacts of Outside Air in the Commercial Sector , 2009 .

[47]  P. Torcellini,et al.  Zero Energy Buildings: A Critical Look at the Definition; Preprint , 2006 .

[48]  Daniel Macumber,et al.  CITY SCALE MODELING WITH OPENSTUDIO , 2016 .

[49]  Mark A. Halverson,et al.  Review of Pre- and Post-1980 Buildings in CBECS - HVAC Equipment , 2006 .

[50]  Nance E. Matson,et al.  Review of California and National Methods for Energy PerformanceBenchmarking of Commercial Buildings , 2005 .

[51]  Joshua D. Kneifel,et al.  Beyond the code: Energy, carbon, and cost savings using conventional technologies , 2011 .

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

[53]  D. Kosterev,et al.  Load modeling in power system studies: WECC progress update , 2008, 2008 IEEE Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century.

[54]  Robert Ramirez,et al.  DrCEUS: Energy and Demand Usage from Commercial On-Site Survey Data , 2003 .

[55]  Travis Walter,et al.  Getting Real with Energy Data : Using the Buildings Performance Database to Support Data-Driven Analyses and Decision-Making , 2014 .

[56]  Wei Jiang,et al.  Analysis of Building Envelope Construction in 2003 CBECS , 2007 .

[57]  Joshua Ryan New,et al.  Building Simulation Modelers are we big-data ready? , 2014 .

[58]  R. Judkoff,et al.  Assessment of the Technical Potential for Achieving Net Zero-Energy Buildings in the Commercial Sector , 2007 .

[59]  H. Akbari,et al.  481 Prototypical commercial buildings for 20 urban market areas , 1991 .

[60]  Paul Raftery,et al.  A review of methods to match building energy simulation models to measured data , 2014 .

[61]  T. Agami Reddy,et al.  Calibrating Detailed Building Energy Simulation Programs with Measured Data—Part II: Application to Three Case Study Office Buildings (RP-1051) , 2007 .

[62]  Burcin Becerik-Gerber,et al.  Why is the reliability of building simulation limited as a tool for evaluating energy conservation measures , 2015 .

[63]  César Porras-Amores,et al.  Assessing the energy and IAQ potential of dynamic minimum ventilation rate strategies in offices , 2015, SpringSim.

[64]  J. J. Hirsch,et al.  DOE-2 Building Energy Analysis Program , 1984 .