The bRema (Building Energy Modeling and Analysis) app was developed by the Building Performance Lab of the CUNY Institute for Urban Systems. Using monthly energy consumption data (electricity, natural gas or steam) and average daily outdoor air temperature data, bRema visualizing linear change-point regression models for a portfolio of buildings, usually of the same use type (e.g., office buildings, schools, recreation centers, etc.).
A Lean energy analysis is then displayed for the portfolio, to show how each building ranks against its peers with regard to the amount of energy consumed for baseload, heating and cooling uses, and for change-point – the temperature above (for cooling) or below (for heating) which seasonal energy consumption is seen. bRema also displays each building’s Energy Use Intensity, greenhouse gas (GHG) emissions in metric tons of CO2 equivalents, and provides a breakdown of site and source energy used.
The production version of bRema allows users to call multiple functions and build desired linear change-point regression models from scratch and use their own statistical thresholds for selection the best-fit model. Users can also take advantage of built-in plotting functions to visualize models and perform additional statistical analysis using built-in stats functions. Moreover, bRema can model both retrofitted and unretrofitted building energy data. This demo version uses Plotly for visualization and lubridate for date-time manipulation.
RStudio Cloud: https://rstudio.cloud/project/259350
Deployed Shiny App: https://cunybpl.shinyapps.io/brema_shiny/
For more information on the inverse modeling process and Lean Analysis, click the following hyperlink: Inverse Modeling of Portfolio Energy Data for Effective Use with Energy Managers
Tin Naing, Junior Analyst
John DeBlase, Lead Developer
Krystyna Horn, Program Manager
Honey Berk, Managing Director
Building Performance Lab
CUNY Institute for Urban Systems