Data

Tools

Indexes

Applications

Experiments

Open Source Science

marl-idr-multi-agent-reinforcement-learning-for-incentive-based-residential-demand-response

Code for the paper "MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response"

https://github.com/tu-delft-ai-energy-lab/marl-idr-multi-agent-reinforcement-learning-for-incentive-based-residential-demand-response

Science Score: 36.0%

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

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ✓
    Academic publication links
    Links to: arxiv.org
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (5.5%) to scientific vocabulary

Keywords

demand-response demand-side-management energy-consumption energy-system human-computer-interaction incentive-program multi-agent-reinforcement-learning
Last synced: 6 months ago · JSON representation

Repository

Code for the paper "MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response"

Basic Info
  • Host: GitHub
  • Owner: TU-Delft-AI-Energy-Lab
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 6.34 MB
Statistics
  • Stars: 32
  • Watchers: 0
  • Forks: 7
  • Open Issues: 3
  • Releases: 0
Topics
demand-response demand-side-management energy-consumption energy-system human-computer-interaction incentive-program multi-agent-reinforcement-learning
Created almost 3 years ago · Last pushed over 2 years ago
Metadata Files
Readme License Citation

README.md

Case study for MARL-iDR-Multi-Agent-Reinforcement-Learning-for-Incentive-based-Residential-Demand-Response

This repository contains code for the paper:

Jasper van Tilburg, Luciano C. Siebert, Jochen L. Cremer, "MARL-iDR: Multi-Agent Reinforcement Learning for Incentive-based Residential Demand Response" IEEE PowerTech 2023, Belgrade, Serbia, https://arxiv.org/abs/2304.04086

Data

This repository includes only placeholder Excel files in /data which includes the first and last data samples. The full data that was used in the case studies in our paper can be downloaded from Pecan Street Inc. [Online]. Available: https://www.pecanstreet.org/

License

This work is licensed under a License: MIT

Owner

  • Name: TU-Delft-AI-Energy-Lab
  • Login: TU-Delft-AI-Energy-Lab
  • Kind: organization

GitHub Events

Total
  • Watch event: 11
  • Fork event: 1
Last Year
  • Watch event: 11
  • Fork event: 1

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 29
  • Total Committers: 1
  • Avg Commits per committer: 29.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 1
  • Committers: 1
  • Avg Commits per committer: 1.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
JochenC 3****C 29

Issues and Pull Requests

Last synced: about 2 years ago

All Time
  • Total issues: 3
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 2
  • Total pull request authors: 0
  • Average comments per issue: 0.67
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 3
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 2
  • Pull request authors: 0
  • Average comments per issue: 0.67
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • saraershadi (2)
  • tfy02 (1)
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels