Solar Power Mapping Data

This software combines datasets about solar panels (photovoltaic "PV" panels), and combines them into a harmonised data source which can be used for machine vision, short-term solar forecasting and other applications.

The goal is to produce a large and rich dataset of PV, which is fully open data.

This software was developed under the Alan Turing Institute's Climate Action project grant R-SPES-115: "Enabling worldwide solar PV nowcasting via machine vision and open data".

Overview of the directory structure

.
|-- admin            -- project process and planning docs
|-- data
|   |-- as_received  -- downloaded data files
|   |-- raw          -- manually edited files (replace dummy data)
|   |-- processed
|-- db               -- database creation
|-- doc              -- documentation
|-- explorations     -- exploratory work
`-- notebooks

Data

Data is held in three directories: as_received contains the data precisely as downloaded from its original source and in its original format; raw contains data that has been manually restructured or reformatted to be suitable for use by software in the project (see "Using this repo" header below). processed contains data that may have been processed in some way, such as by Python code, but is still thought of as “source” data.

The following sources of data are used:

• OpenStreetMap - Great Britain download (Geofabrik).
  • OSM data types
  • Solar PV tagging
• FiT - Report of installed PV (and other tech including wind). 100,000s entries.
• REPD - Official UK data from the "renewable energy planning database". It contains large solar farms only.
• Machine Vision dataset - supplied by Descartes labs (Oxford), not publicly available yet.

Project outcome

This repo includes a set of scripts that will take input datasets (REPD, OSM, FiT and machine vision – each in diff format), perform data cleaning/conversion, populate a PostgreSQL database, perform grouping of data where necessary (there are duplicate entries in REPD, multiple solar farm components in OSM) and then match entries between the data tables, based on the matching criteria we have come up with.

The database creation and matching scripts should work with newer versions of the source data files, or at least do so with minimal changes to the data processing (see "Using this repo" below).

The result of matching is a table in the database called matches that links the unique identifiers of the data tables. This also contains a column called match_rule, which refers to the method by which the match was determined, as documented in doc/matching.

Using this repo

First, download (or clone) this repository.

Install requirements

1. Install PostgreSQL with PostGIS
2. Install Python 3 (version 3.7 or later) and pip
3. Run pip install -r requirements.txt
4. Install Osmium

Download and prepare data files

1. Download the following data files from the internet and store locally. We recommend saving these original data files within the directory structure under data/as_received:
   • OSM PBF file (GB extract): Download
   • FiT reports: Navigate to ofgem and click the link for the latest Installation Report (during the Turing project, 30 September 2019 was used), then download the main document AND subsidiary documents
   • REPD CSV file: Download - this is always the most up to date version
   • Machine Vision dataset: supplied by Descartes labs (Oxford), not publicly available yet.
2. Navigate to data/raw and type make - this will convert some of the downloads into other file formats ready for further processing.
   • Note that the OpenStreetMap data will have been processed into a file osm.csv. If you do not need to do any merging/clustering, you could use this file directly, as a simplified extract of OSM solar PV data.
3. Carry out manual edits to the data files, as described in doc/preprocessing, editing the file copies in data/raw under the names suggested by the doc.
4. Navigate to data/processed and type make - this will create versions of the data files ready for import to PostgreSQL

Run the database creation and data matching

5. Make sure you have PostgreSQL on your machine, then run the command: createdb hut23-425 "Solar PV database matching" - this creates the empty database.
6. Navigate to db and run the command psql -f make-database.sql hut23-425 - this populates the database (see doc/database), carries out some de-duplication of the datasets and performs the matching procedure (see doc/matching). Note: this may take several minutes.

Note that the above commands require you to have admin rights on your PostgreSQL server. On standard Debian-based machines you could prepend the commands with sudo -u postgres, or you could assign privileges to your own user account.

Export data from the database

The data tables in PostgreSQL can be used for further analysis. To make a data "snapshot" we export back out again:

7. Navigate to db and run the command psql -f export.sql hut23-425

8. Navigate to data/exported and run make. Note: this may take several minutes.

   You can also run the statistical analysis and plotting -- however, this relies on some external data files such as GSP regions and LSOA regions. The file analyse_exported.py makes use of some local file paths (in data/other, not in the public source code). To do the additional plotting+stats, in data/exported run make all.

As a result of this, you should have a CSV and a GeoJSON file representing the harmonised data exported from the local database.