|Title||The BayREN Integrated Commercial Retrofits (BRICR) Project: An Introduction and Preliminary Results|
|Publication Type||Conference Proceedings|
|Year of Publication||2018|
|Authors||Barry Hooper, Tianzhen Hong, Danie Macumber, Sang Hoon Lee, Yixing Chen, Nicholas Long, Edwin Lee, Imma Regina Del Cruz, Mary Ann Piette, Jennifer Berg|
|Conference Name||2018 Summer Study on Energy Efficiency in Buildings|
BayREN Integrated Commercial Retrofits (BRICR) is a DOE-funded project which aims to enhance the capacity of energy efficiency programs to recruit participants, develop retrofits, and measure outcomes in small and medium-sized commercial buildings, a sector notoriously hard to reach and expensive to serve, that accounts for ⅔ of US commercial floor space. To address these barriers, BRICR leverages existing incentives, financing, data, and open source software to facilitate two paths for comprehensive improvements: a deep energy retrofit, or serial upgrades integrated into capital improvement and maintenance cycles. BRICR is developing an integrated workflow for iterative energy modeling of commercial buildings for city energy program managers and auditors - starting with mass building-scale simulation based on public records and proceeding through audit, retrofit, and measurement and verification stages. BRICR builds on existing tools including LBNL’s CBES, NREL’s OpenStudio, PNNL’s Audit Template, and DOE’s BuildingSync and SEED PlatformTM. At each stage, BRICR uses available information to inform simulations (starting with public records but iteratively augmented with observations from energy program staff) to improve the quality of the models that inform decision making. This paper presents initial results from energy models of 1699 office and retail buildings in San Francisco. Building stock data from public records were translated to create a BuildingSync file for each building which was stored in SEED. Each BuildingSync file was then translated to multiple OpenStudio Workflow files for EnergyPlus simulation to estimate energy savings of energy conservation measures (ECM). Energy savings predictions for each ECM were written back to an updated BuildingSync file for each building and re-uploaded to SEED. The distribution of baseline energy simulations was calibrated against the publicly disclosed distribution of energy benchmarking data to increase confidence in results.