A curated list of open technology projects to sustain a stable climate, energy supply, biodiversity and natural resources.

nmecr

Builds upon the energy efficiency community's past efforts to model complex and nuanced building energy use profiles.
https://github.com/kw-labs/nmecr

Category: Consumption
Sub Category: Buildings and Heating

Last synced: about 20 hours ago
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An implementation of peer-reviewed energy data analysis algorithms for site-specific M&V

README.md

nmecr

Overview

nmecr is a Measurement & Verification (M&V) practitioner's toolbox that builds upon the energy efficiency community's past efforts to model complex and nuanced building energy use profiles. While intended for use in the commercial and institutional sectors, its functions can be extended to model the energy use profile of industrial systems.

There are 11 energy modeling algorithms available in nmecr:

  1. For use with hourly, daily, and monthly time interval data:
  • Simple Linear Regression with Outside Air Temperature
  • Three Parameter Linear Model (Cooling/Heating)
  • Four Parameter Linear Model
  • Five Parameter Linear Model
  • Mean Model
  1. For use with hourly and daily time interval data:
  • Time-of-Week Model
  • Temperature & Time-of-Week Model
  1. For use with monthly and daily time interval data only:
  • Heating Degree Day Model
  • Cooling Degree Day Model
  • Heating & Cooling Degree Day Model

Please refer to the project site for more information on the available models.

Installation

You can download nmecr from GitHub.

install.packages("devtools")
devtools::install_github("kW-Labs/nmecr", upgrade = "never")

Please make sure R is updated on your system. Check here for the latest release.

Getting help

If you have a question or encounter a bug, please file a minimal reproducible example on github.


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Committers metadata

Last synced: 6 days ago

Total Commits: 474
Total Committers: 10
Avg Commits per committer: 47.4
Development Distribution Score (DDS): 0.211

Commits in past year: 48
Committers in past year: 4
Avg Commits per committer in past year: 12.0
Development Distribution Score (DDS) in past year: 0.479

Name Email Commits
MeeraSharma m****4@g****m 374
k-wolfe99 1****9 25
Devan Johnson j****n@k****m 24
Dexter Luu d****u@k****m 21
Maggie Jacoby m****y@k****m 19
DrDAJump d****p@k****m 3
kodanshaivya s****n@k****m 3
ApKhanuja a****a@k****m 2
Kyle Wolfe k****e@k****m 2
Mrinalini Sharma 3****a 1

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Issue and Pull Request metadata

Last synced: 2 days ago

Total issues: 18
Total pull requests: 29
Average time to close issues: 3 months
Average time to close pull requests: 1 day
Total issue authors: 11
Total pull request authors: 6
Average comments per issue: 2.72
Average comments per pull request: 0.21
Merged pull request: 18
Bot issues: 0
Bot pull requests: 0

Past year issues: 1
Past year pull requests: 0
Past year average time to close issues: about 1 hour
Past year average time to close pull requests: N/A
Past year issue authors: 1
Past year pull request authors: 0
Past year average comments per issue: 1.0
Past year average comments per pull request: 0
Past year merged pull request: 0
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/kw-labs/nmecr

Top Issue Authors

  • Cyy-fresh1994 (2)
  • jwoodward0 (2)
  • LucaNicoliYT88 (2)
  • DrDAJump (2)
  • christopherbalbach (2)
  • dexterluu (2)
  • mhsjacoby (2)
  • samberman1 (1)
  • shawk08033 (1)
  • greggridium (1)
  • NastaranAl (1)

Top Pull Request Authors

  • kodanshaivya (8)
  • dexterluu (6)
  • bhoffmankW (6)
  • mhsjacoby (5)
  • ApKhanuja (2)
  • devanjohnson (2)

Top Issue Labels

  • enhancement (2)

Top Pull Request Labels

  • documentation (2)

Dependencies

DESCRIPTION cran
  • R >= 3.5 depends
  • assertive * imports
  • dplyr * imports
  • lubridate * imports
  • magrittr * imports
  • methods * imports
  • purrr * imports
  • segmented * imports
  • stats * imports
  • timetk * imports
  • xts * imports
  • zoo * imports
  • knitr * suggests
  • rmarkdown * suggests
  • testthat >= 3.0.0 suggests

Score: 5.598421958998375