Navy Yard Building 101 Test Bed for Assessing Technologies and Tools

Publication Title: Navy Yard Building 101 Test Bed for Assessing Technologies and Tools

Consortium Member(s): Bayer Material Science,Ben Franklin Technology Partners of SE PA (BFTP),Carnegie Mellon University,Delaware Valley Industrial Resource Center (DVIRC),Drexel University,Morgan State University,New Jersey Institute of Technology (NJIT),Philadelphia Industrial Development Corporation (PIDC),Purdue University,Rutgers University,The Pennsylvania State University,United Technologies Research Center (UTRC),University of Pennsylvania,Virginia Tech

Project Contact: Alon Abramson

Date: April 11, 2012

"Building 101 in the Navy Yard is the temporary headquarters of the U.S. Department of Energy’s Energy Efficient Building Hub. The building, owned by the Philadelphia Industrial Development Corporation (PIDC), has become one of the nation’s most highly instrumented commercial buildings.

Acquired data is continuously stored and is made available to Hub researchers and other building energy efficiency researchers for development, validation and calibration modeling and simulation tools, and for assessment of the impact of building energy technologies and systems on energy use. The detailed building performance database is being utilized by the EEB Hub as it seeks to catalyze the building industry in the Philadelphia region to become a systems solutions provider of energy efficient buildings as a matter of standard practice. Influencing the regional building industry in this way will help the Hub achieve its goal of reducing energy use in the U.S. commercial buildings sector by 20 percent by 2020 while measurably improving indoor environments.

Data calibrated inverse models, often used to establish potential retrofit strategies for a building, as well as forward models, preferred for new construction design, are included in the data-based continuous improvement loop. The effort is coordinated with National Renewable Energy Laboratory researchers who are establishing an open platform environment and user-friendly GUI interfaces for a suite of predictive forward models which aim to predict load demand, energy utilization, indoor environment conditions, and lighting levels."