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Building Energy Modeling

About the portfolio

Building energy modeling (BEM)—physics-based calculation of building energy consumption—is a multi-use tool for building energy efficiency. Established use cases include design of new buildings and deep retrofits, development of whole-building energy efficiency codes and standards (e.g., ASHRAE 90.1) and performance-path compliance with those codes (e.g., ASHRAE 90.1 “Appendix G” Performance Rating Method), beyond-code physical asset rating and labeling (e.g., USGBC’s LEED energy credit), and development of prescriptive design guides (e.g., ASHRAE 50% beyond 90.1 Advanced Design and Retrofit Guides). Emerging uses include design of low-energy building control algorithms, continuous commissioning of building mechanical systems, and dynamic building control for energy optimization or demand response. DOE has been actively involved in BEM research and development since the early 1970s, before its ascension to a cabinet-level department.
Learn more about BTO's building energy modeling portfolio ►

 

Explore our software and projects

End-Use Breakdown: The Building Energy Modeling Blog

New OpenStudio-Standards Gem Delivers One Two Punch
The new OpenStudio-Standards Measure “Create Performance Rating Method Baseline Building” takes a model along with three arguments—code version, building type, and climate zone—and produces the corresponding ASHRAE 90.1 “Appendix G” baseline model. In this case, visible changes include removal of exterior shading and small changes in window area. Credit: Andrew Parker, NREL.

A new Ruby gem, openstudio-standards, delivers two significant and closely related capabilities—creation of prototype building models and automated derivation of a code baseline building model.

QCoefficient Uses EnergyPlus to Reduce Willis Tower Energy Bills
QCoefficient uses model predictive control (MPC) to pre-cool 13 of the 108 floors on Chicago’s Willis Tower. This figure shows cumulative energy use in 30 minute intervals between June 11 and September 28, 2012. QCoefficient’s optimized strategy replaced 1500 MWh of peak-time demand with 600 MWh of off-peak demand, saving approximately $250,000 and 900 tons of CO2 emissions in the process. Credit: QCoefficient.

Chicago firm QCoefficient uses model predictive control (MPC) to pre-cool large commercial buildings, savings hundreds of thousands of dollars and tons of CO2.