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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See the blog post here for a general overview of the model development (probably out of date): [https://rdrn.me/flask-optimize-minigrid/](https://rdrn.me/flask-optimize-minigrid/)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.

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