Abstract
ComStock™ is an analytic methodology for modeling the energy usage of the commercial building stock within the United States of America. The commercial building stock is represented through a sampling of complex probabilistic distributions of various features of interest for modeling energy usage within commercial buildings. Each sample from these distributions is converted into a building energy model based on the features of that specific sample. Each building energy model can be simulated as is, but additional changes can be made to the model through addition of energy conservation measures, component faults, or other desired alterations. The results of the simulations are then processed to provide insights for various stakeholders, including but not limited to policy makers, engineers, and marketers.
- Developers:
-
Horsey, Henry [1] ; Parker, Andrew [1] ; Farthing, Amanda [1] ; Dahlhausen, Matthew [1] ; Praprost, Marlena [1] ; Bianchi, Carlo [1] ; Robertson, Joseph [1] ; Horowitz, Scott [1] ; Zhang, Liang [1]
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Release Date:
- 2020-10-18
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
Ruby
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOE Laboratory Directed Research and Development (LDRD) ProgramPrimary Award/Contract Number:AC36-08GO28308USDOE Office of Energy Efficiency and Renewable Energy (EERE), Strategic Programs OfficePrimary Award/Contract Number:AC36-08GO28308
- Code ID:
- 62900
- Site Accession Number:
- SWR-19-33
- Research Org.:
- National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Country of Origin:
- United States
Citation Formats
Horsey, Henry, Parker, Andrew, Farthing, Amanda, Dahlhausen, Matthew, Praprost, Marlena, Bianchi, Carlo, Robertson, Joseph, Horowitz, Scott, and Zhang, Liang.
ComStock™ [SWR-19-33 and SWR-20-32].
Computer Software.
https://github.com/NREL/ComStock.
USDOE Laboratory Directed Research and Development (LDRD) Program, USDOE Office of Energy Efficiency and Renewable Energy (EERE), Strategic Programs Office.
18 Oct. 2020.
Web.
doi:10.11578/dc.20210830.5.
Horsey, Henry, Parker, Andrew, Farthing, Amanda, Dahlhausen, Matthew, Praprost, Marlena, Bianchi, Carlo, Robertson, Joseph, Horowitz, Scott, & Zhang, Liang.
(2020, October 18).
ComStock™ [SWR-19-33 and SWR-20-32].
[Computer software].
https://github.com/NREL/ComStock.
https://doi.org/10.11578/dc.20210830.5.
Horsey, Henry, Parker, Andrew, Farthing, Amanda, Dahlhausen, Matthew, Praprost, Marlena, Bianchi, Carlo, Robertson, Joseph, Horowitz, Scott, and Zhang, Liang.
"ComStock™ [SWR-19-33 and SWR-20-32]." Computer software.
October 18, 2020.
https://github.com/NREL/ComStock.
https://doi.org/10.11578/dc.20210830.5.
@misc{
doecode_62900,
title = {ComStock™ [SWR-19-33 and SWR-20-32]},
author = {Horsey, Henry and Parker, Andrew and Farthing, Amanda and Dahlhausen, Matthew and Praprost, Marlena and Bianchi, Carlo and Robertson, Joseph and Horowitz, Scott and Zhang, Liang},
abstractNote = {ComStock™ is an analytic methodology for modeling the energy usage of the commercial building stock within the United States of America. The commercial building stock is represented through a sampling of complex probabilistic distributions of various features of interest for modeling energy usage within commercial buildings. Each sample from these distributions is converted into a building energy model based on the features of that specific sample. Each building energy model can be simulated as is, but additional changes can be made to the model through addition of energy conservation measures, component faults, or other desired alterations. The results of the simulations are then processed to provide insights for various stakeholders, including but not limited to policy makers, engineers, and marketers.},
doi = {10.11578/dc.20210830.5},
url = {https://doi.org/10.11578/dc.20210830.5},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20210830.5}},
year = {2020},
month = {oct}
}