A tool for optimising electricity access pathways
Project description
# openelec
[![Build Status](https://travis-ci.org/carderne/openelec.svg?branch=master)](https://travis-ci.org/carderne/openelec)
This is a slowly merging combination of two tools:
1. Creating national-level plans for achieving universal electricity access
2. Optimising town/village-level mini-grid, densification and standalone systems.
## National-level
A tool for modelling the optimal pathways to improving electricity access.
Described in my blog post here: [Modelling the optimum way to achieve universal electrification](https://rdrn.me/modelling-universal-electrification/)
## Town-level
A tool for optimising rural [mini-grid systems](https://energypedia.info/wiki/Mini_Grids) using OpenStreetMap building data and a minimum spanning tree approach to network optimisation.
Provides model features through a simple API, as well as a basic Flask web app.
See the blog post here for a general overview of the model development (probably out of date): [https://rdrn.me/flask-optimize-minigrid/](https://rdrn.me/flask-optimize-minigrid/)
Installation
--------
**Requirements**
minigrid-optimiser requires Python >= 3.5 with the following packages installed:
- ``flask`` >= 1.0.2 (only for the web app)
- ``numpy`` >= 1.14.2
- ``pandas`` >= 0.22.0
- ``geopandas`` >= 0.4.0 (0.4.0 had API breaking changes so this version is needed)
- ``shapely`` >= 1.6.4
- ``scipy`` >= 1.0.0
- ``scikit-learn`` >= 0.17.1
**Install**
Downloads or clone the repository:
```
git clone https://github.com/carderne/openelec.git
```
Then ``cd`` into the directory, and install the required packages into a virtual environment:
```
pip install -r requirements.txt
```
Then run ``jupyter notebook`` and open ``minigrid-optimiser.ipynb`` or `electrify.ipynb` to go over the main model usage and API.
[![Build Status](https://travis-ci.org/carderne/openelec.svg?branch=master)](https://travis-ci.org/carderne/openelec)
This is a slowly merging combination of two tools:
1. Creating national-level plans for achieving universal electricity access
2. Optimising town/village-level mini-grid, densification and standalone systems.
## National-level
A tool for modelling the optimal pathways to improving electricity access.
Described in my blog post here: [Modelling the optimum way to achieve universal electrification](https://rdrn.me/modelling-universal-electrification/)
## Town-level
A tool for optimising rural [mini-grid systems](https://energypedia.info/wiki/Mini_Grids) using OpenStreetMap building data and a minimum spanning tree approach to network optimisation.
Provides model features through a simple API, as well as a basic Flask web app.
See the blog post here for a general overview of the model development (probably out of date): [https://rdrn.me/flask-optimize-minigrid/](https://rdrn.me/flask-optimize-minigrid/)
Installation
--------
**Requirements**
minigrid-optimiser requires Python >= 3.5 with the following packages installed:
- ``flask`` >= 1.0.2 (only for the web app)
- ``numpy`` >= 1.14.2
- ``pandas`` >= 0.22.0
- ``geopandas`` >= 0.4.0 (0.4.0 had API breaking changes so this version is needed)
- ``shapely`` >= 1.6.4
- ``scipy`` >= 1.0.0
- ``scikit-learn`` >= 0.17.1
**Install**
Downloads or clone the repository:
```
git clone https://github.com/carderne/openelec.git
```
Then ``cd`` into the directory, and install the required packages into a virtual environment:
```
pip install -r requirements.txt
```
Then run ``jupyter notebook`` and open ``minigrid-optimiser.ipynb`` or `electrify.ipynb` to go over the main model usage and API.
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