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A tool for optimising electricity access pathways

Project description

# openelec
[![Build Status](](

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](

## Town-level

A tool for optimising rural [mini-grid systems]( 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See the blog post here for a general overview of the model development (probably out of date): [](



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


Downloads or clone the repository:

git clone

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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