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energy-efficiency

Code, works in progress, and supplemental information related to my master's thesis

https://github.com/gstechschulte/energy-efficiency

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.1%) to scientific vocabulary
Last synced: 6 months ago · JSON representation ·

Repository

Code, works in progress, and supplemental information related to my master's thesis

Basic Info
  • Host: GitHub
  • Owner: GStechschulte
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 52.6 MB
Statistics
  • Stars: 0
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 4 years ago · Last pushed over 3 years ago
Metadata Files
Readme Citation

README.md

Energy-Efficiency-Thesis

Reproduce the results

entsorgung_gp entsorgung_spc entsorgung_spc

Method 1

Note: It is advised to create a new environment

1.) Clone the repo and cd into the root directory

2.) Build image

docker build thesis-model .

3.) Run the container and model(s)

docker run -i thesis-model

Method 2

1.) Pull the image from Docker Hub (contact author for access to private Docker Hub )

docker pull [OPTIONS] NAME[:TAG|@DIGEST]

Available Data

Upon building and running the container, you will be asked to enter a machine and the time aggregation you would like to analyze

Enter machine name:

Enter time aggregation (10 or 30):

Available time sampling is 10 and 30 minutes. The following machines have energy baseline models ready to perform inference (prediction):

  • Entsorgung
  • Hauptluftung
  • Gesamtmessung
  • UV EG
  • UV OG
  • EG

Returns

After entering a machine and time aggregation, the model will perform one day ahead predictions at the time sampling interval passed. Scoring metrics (MSE, RMSE, MAPE, ACE, and Pinball Loss) are returned for the predictions. In addition, a pd.DataFrame will be returned containing the time, actual kW, predicted kW, and upper / lower control limit.

Citation (citation.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
  - family-names: Stechschulte
    given-names: Gabriel
    orcid: https://orcid.org/0000-0003-4925-7248
title: "Gaussian Processes for Industrial Equipment Energy Efficiency Estimation and Performance Deviation Detection"
version: 1.0,
url: "https://github.com/GStechschulte/energy-efficiency"
date-released: 2022-05-01

GitHub Events

Total
Last Year

Committers

Last synced: 11 months ago

All Time
  • Total Commits: 250
  • Total Committers: 3
  • Avg Commits per committer: 83.333
  • Development Distribution Score (DDS): 0.036
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
GStechschulte s****g@g****m 241
braulio b****n@h****h 8
Entor Arifi e****i@j****h 1
Committer Domains (Top 20 + Academic)
jaywalker.ch: 1 hslu.ch: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 0
  • Total pull requests: 16
  • Average time to close issues: N/A
  • Average time to close pull requests: less than a minute
  • Total issue authors: 0
  • Total pull request authors: 2
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 16
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Top Authors
Issue Authors
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  • GStechschulte (15)
  • entorarifi (1)
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