Skip to content

aliag18/Generating-Intelligent-Module-Modules-GIMM-

View on GitHub ↗Related repositories →

This set of codes implements our paper "Analysis of Weather and Time Features in Machine Learning-aided ERCOT Load Forecasting". This work is under the open license: CC BY 4.0.

active 2025-02-11 → 2025-02-25 (UTC)

Complete coverage27,512 / 27,512 hourly files (100%) · 2 absent upstream2023-08-15 → 2026-10-04 (UTC)
Events
37
Pushes
13
Pull requests
11
Issues
0
Stars
0
Forks
0

Activity over time

Daily event counts in the loaded window

Line chart, 15 days from 2025-02-11 to 2025-02-25. Pushes: 13 total, peak 5 in a day. Pull requests: 11 total, peak 4 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 0 total, peak 0 in a day.

  • Pushes
  • Pull requests
  • Issues
  • Comments
  • Stars

Top contributors

Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity

ContributorContributionsPushesPRsComments
aliag1812570
Angel87643211740
mpa781100

Recent activity

Latest issues, pull requests and releases

  • Pull request#7aliag182025-02-25 20:00
  • Pull request#6Angel8764322025-02-25 19:54
  • Pull request#6Angel8764322025-02-25 19:54
  • Pull request#5aliag182025-02-21 01:03
  • Pull request#5aliag182025-02-21 01:03
  • Pull request#4aliag182025-02-20 23:59
  • Pull request#4aliag182025-02-20 23:57
  • Pull request#3Angel8764322025-02-11 22:02
  • Pull request#3Angel8764322025-02-11 22:02
  • Pull request#2aliag182025-02-11 21:36
  • Pull request#2aliag182025-02-11 21:35

Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 0 stars here means stars gained during the window, not the repo's star count.