Skip to main content

A Python module for reinforcement learning based energy management system tools

Project description

RL_MGM (reinforcement learning micro-grid managers) Module:

A set of modules that can be used to aid in simulating a micro-grid environment for training reinforcement learning based energy management policy generators. The two main modules are the _DATAGENERATORS and MG_Managers. They are described in the General information section below.

Table of Contents

General Info

This rl-migrogrid-mangers (reinforcement learning) module contains a set of submodules that define RL networks for use as energy management agents, tools to generate hourly building load and various MW sizes of PV output based on the month and hour based on fitted distributions. The modules can be defined as follows:

* _DATAGENERATORS: set of tools to create stochastic data generators that represent hourly load profiles and month/hour PV MW outputs.

* MG_Managers: set of RL networks that can be trained on simulated microgrid data to generate energy management policies.

* MG_Environments: tools that take a RL agent as a manager and simulate using them in the environment

Technologies

List the technologies used in this project. For example:

  • Python: 3.8
  • numpy: 1.26.3
  • pandas: 2.1.4
  • Joblib: 1.3.2
  • matplotlib: 3.8.2
  • torch: 2.1.2
  • distfit: 1.7.3

Installation

pip install rl-microgrid-managers

Usage

RL agent generation

Data Generation Tools

License

MIT License

Copyright (c) <2023>

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice (including the next paragraph) shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

rl_microgrid_managers-0.0.13.tar.gz (74.4 kB view details)

Uploaded Source

Built Distribution

rl_microgrid_managers-0.0.13-py3-none-any.whl (79.3 kB view details)

Uploaded Python 3

File details

Details for the file rl_microgrid_managers-0.0.13.tar.gz.

File metadata

File hashes

Hashes for rl_microgrid_managers-0.0.13.tar.gz
Algorithm Hash digest
SHA256 607fa74d9ff0bb38ec6f2192fdcc81920c01fb7d5039464abf3c72b1671d34f1
MD5 f1bfa21ce3d544e3e83885b3fd3e7e4c
BLAKE2b-256 171dc47bad80b6e1a203d8d077575aa00b5e2f6b362b63f3e5e0b608f14327f9

See more details on using hashes here.

File details

Details for the file rl_microgrid_managers-0.0.13-py3-none-any.whl.

File metadata

File hashes

Hashes for rl_microgrid_managers-0.0.13-py3-none-any.whl
Algorithm Hash digest
SHA256 86c95fcc83ead6438fa97687442d9d79eb0d8b5c07750a73b6366bc0f0ab16de
MD5 c1834f3c4e2ce5f6415aa617fbc1dc39
BLAKE2b-256 dc12e80b5df5d9d7468eef37a19bd4dc183da5d58dbf7c55676f1a17c4bace4a

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page