Key concepts

This page explains concepts and limitations to understand consider when building solar map layers.

flowchart
subgraph Docker Container
    direction LR
    A[Source Datasets] --> C(pipeline.py) --> E[Solar Datasets]
    B[parameters] --> C
    D(algorithms) <--> C
end

Diagram: pipeline workflow which generates solar datasets.

Pipeline end-to-end script

• Our main pipeline script provides end-to-end calculations to create solar potential datasets.
• The pipeline script is customizable.
• It accepts parameters to select from datasets, algorithms, and constrained problem spaces.
• With each release, we add attributes and improve results, by replacing estimations with better algorithms.

CPU intensive

• We process large national datasets, using CPU intensive algorithms, which can take days to months to complete.
• So we initially develop and test on small datasets, before scaling to the full problem space.

Docker container

• Our command line scripts run inside a Docker container.
• This enables our code to be run from multiple platforms, including Mac, Windows, Linux, and to scale to Cloud Computing.
• We manage the complicated python and GRASS application dependencies within Docker, building upon UbuntuGIS.