Lead Performer: Lawrence Berkeley National Lab – Berkeley, CA
Partner: Sustainable IQ – Arlington, MA
DOE Total Funding: $250,000
FY21 DOE Funding: $250,000
Project Term: March 2020 – September 2021
Funding Type: directed
Project Objective
A previous collaboration between BTO and the American Institute of Architects (AIA) developed the Design Data Exchange (DDx) (https://2030ddx.aia.org), an online portal that supports AIA’s 2030 Commitment Reporting program. Now maintained by AIA, the DDx portal serves over 240 firms, large and small. Because it caters to a broad audience and supports a key AIA program, the DDx is not easy to either expand its data collection or to experiment with new features and analyses. This follow-on project, a complementary online resource, a Deep Design Data (D3) Portal that expands the data collection and analysis capabilities of the DDx and can be used to prototype new DDx functionality.
The D3 will target large firms—of the 18,000 AIA member firms, the largest 60 account for over 90% of the total square footage designed—that have greater in-house data collection capabilities and greater need for large-scale data analysis and modeling support. The D3 will not be part of the 2030 reporting program but will serve as a data analysis research and prototyping testbed. It will leverage the “batch import” functions and APIs developed for the DDx to collect additional data use that data to support new analytical and design decision support features. The intent is not to require firms to do additional reporting work, but rather to provide additional value to firms that already use automated reporting features.
Planned D3 data extensions include: i) design and BEM cost, ii) construction cost, iii) end-use EUIs, iv) envelope parameters, v) HVAC system type and parameters, vi) lighting parameters, and vii) Energy Conservation Measures (ECMs). Planned analytical extensions include: i) crosstabs on all input variables, ii) limited crosstabs on multiple variables, iii) longitudinal analysis across multiple years, iv) longitudinal analysis across multiple project phases. Future potential features include: i) plug-ins from common energy modeling tools, ii) ECM suggestions based on modeled end-use breakdowns, and iii) project and ECM specific modeling guidance. Features will be filtered, prioritized, and refined using discussions with participating firms.
Contacts
DOE Technology Manager: Amir Roth
Lead Performer: Cindy Regnier, LBNL