A harmonised, high-coverage, open dataset of solar photovoltaic installations in the UK

Abstract

Solar photovoltaic (PV) is an increasingly significant fraction of electricity generation. Efficient management, and innovations such as short-term forecasting and machine vision, demand high-resolution geographic datasets of PV installations. However, official and public sources have notable deficiencies: spatial imprecision, gaps in coverage and lack of crucial meta data, especially for small-scale solar panel installations. We present the results of a major crowd-sourcing campaign to create open geographic data for over 260,000 solar PV installations across the UK, covering an estimated 86% of the capacity in the country. We focus in particular on capturing small-scale domestic solar PV, which accounts for a significant fraction of generation but was until now very poorly documented. Our dataset suggests nameplate capacities in the UK (as of September 2020) amount to a total of 10.66 GW explicitly mapped, or 13.93 GW when missing capacities are inferred. Our method is applied to the UK but applicable worldwide, and compatible with continual updating to track the rapid growth in PV deployment.

A crowdsourced dataset of aerial images with annotated solar photovoltaic arrays and installation metadata

“…Recent research to construct global PV inventories 12 , 13 is limited to solar farms and does not include rooftop PV. A recent crowdsourcing effort allowed the mapping of 86% of the United Kingdom’s rooftop PV installations 14 . Other available datasets are aggregated at the communal scale (census level) 15 .…”

A General Guide for Harmonizing Data: Drawing Lessons from Harmonizing COVID-19 PHSM Data

“…In some cases, the data had to be somewhat reformatted in such a way that also impacted how the unique_id assigned by the original dataset was formatted though this was always done so in a way that makes it possible to trace back to the original dataset. 13 In the case of OxCGRT, no unique identiőers are provided in the original dataset and in this case we generate them using a combination of the policy indicator, date, country and where applicable, province.…”

“…It mainly rests on the availability of massive datasets, of that there can be no doubt.ž From őelds as varied as socio-economics [2,4,5] to ecology [6] and the 'Internet of Things' [7], data scientists report the lack of big data itself is a major bottleneck in using big data tools. Increasingly, data scientists must őrst sort through heterogeneous, incongruent, and fragmented datasets before any analyses can be conducted [8ś 13]. Such problems with data availability are often exacerbated in emergency situations where real time analyses are often stymied by unevenly documented or unclean data [14][pg.…”

Promoting citizen science in the energy sector: Generation Solar, an open database of small-scale solar photovoltaic installations

“…Efforts have been made to incorporate this kind of practices in the energy sector, covering aspects such as the evaluation of transition scenarios 4 , the engagement in energy efficiency and energy demand management 5,6 , the support in the performance assessment of photovoltaic systems 7 and the mapping of existing PV installations into OpenStreetMap 8 . It is clear that the urgent energy transition in which we are immersed, with the relevance of renewable energies, will only be possible through the concerted action of all stakeholders, putting into practice the benefits of a "quadruple helix of innovation" [9][10][11] , and going beyond social acceptance by reinforcing the participation in decision-making processes 12,13 .…”