https://github.com/intelligent-environments-lab/CityLearn

Official reinforcement learning environment for demand response and load shaping
https://github.com/intelligent-environments-lab/CityLearn

Last synced: 2 months ago
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Official reinforcement learning environment for demand response and load shaping

• Host: GitHub
• URL: https://github.com/intelligent-environments-lab/CityLearn
• Owner: intelligent-environments-lab
• License: mit
• Created: 2019-06-30T02:41:48.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2024-11-09T21:58:44.000Z (2 months ago)
• Last Synced: 2024-11-13T00:16:55.690Z (2 months ago)
• Language: Python
• Homepage:
• Size: 425 MB
• Stars: 475
• Watchers: 20
• Forks: 171
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

• open-sustainable-technology - CityLearn - Official reinforcement learning environment for demand response and load shaping. (Energy Systems / Load and Demand Forecasting)
• awesome-production-machine-learning - CityLearn - environments-lab/CityLearn.svg?style=social) - CityLearn is an open source OpenAI Gym environment for the implementation of Multi-Agent Reinforcement Learning (RL) for building energy coordination and demand response in cities. (Industry Strength RL)

README

        
# CityLearn

CityLearn is an open source Farama Foundation Gymnasium environment for the implementation of Multi-Agent Reinforcement Learning (RL) for building energy coordination and demand response in cities. A major challenge for RL in demand response is the ability to compare algorithm performance. Thus, CityLearn facilitates and standardizes the evaluation of RL agents such that different algorithms can be easily compared with each other.

![Demand-response](https://github.com/intelligent-environments-lab/CityLearn/blob/master/assets/images/dr.jpg)

## Environment Overview

CityLearn includes energy models of buildings and distributed energy resources (DER) including air-to-water heat pumps, electric heaters and batteries. A collection of building energy models makes up a virtual district (a.k.a neighborhood or community). In each building, space cooling, space heating and domestic hot water end-use loads may be independently satisfied through air-to-water heat pumps. Alternatively, space heating and domestic hot water loads can be satisfied through electric heaters.

![Citylearn](https://github.com/intelligent-environments-lab/CityLearn/blob/master/assets/images/citylearn_systems.png)

## Installation

Install latest release in PyPi with `pip`:

```console

pip install CityLearn

```

## Documentation

Refer to the [docs](https://intelligent-environments-lab.github.io/CityLearn/) for documentation of the CityLearn API.