---
annotations_creators:
- machine-generated
language:
- en
language_creators:
- machine-generated
license:
- mit
multilinguality:
- monolingual
pretty_name: United Kingdom PV Solar generation
size_categories:
- 1B<n<10B
source_datasets:
- original
tags:
- pv
- photovoltaic
- environment
- climate
- energy
- electricity
task_categories:
- time-series-forecasting
task_ids:
- multivariate-time-series-forecasting
---

# UK PV dataset

Domestic solar photovoltaic (PV) power generation data from the United Kingdom.
This dataset contains data from over 30,000 solar PV systems. The dataset spans 2010 to 2025.
The dataset is updated with new data every few months.

All PV systems in this dataset report _cumulative_ energy generation every 30 minutes. This data represents a true accumulation of the total energy generated in the last half hour. This cumulative value is calculated on the meter itself. As such, the 30-minute data is generally of high quality and low noise. 

Of the 30,000 systems, about 1,309 of the solar PV systems _also_ report instantaneous readings every 5 minutes. This data is recorded in the `5_minutely.parquet` files. Please note that this data is noisy because these readings are instantaneous, and solar PV systems respond within milliseconds to changes in solar irradiance. And even small, wispy clouds can cause changes in solar irradiance!

The 30-minutely data is not an aggregation of the 5-minutely data. The 5-minutely data is instantaneous. The 30-minutely data is a true sum of the total energy produced over the time period. As such, the 30-minutely data should be the preferred source of data.

The data is recorded by professional meters, wired into the AC side of the inverter.

To protect the privacy of the solar PV system owners, we have reduced the precision of the geographical location of each PV system to about 1 kilometer.
If you are the owner of a PV system in the dataset, and do not want your solar data to be shared then please email us at info@openclimatefix.org.

This dataset is made possible by [Sheffield Solar](https://www.solar.sheffield.ac.uk/).

## Files

- `metadata.csv`: The geographical location, orientation, tilt, and nominal generation capacity for each solar PV system.
- `{5,30}_minutely/year=YYYY/month=MM/data.parquet`: Energy production time series data for each solar PV system, for one month.

### metadata.csv

Metadata of the different PV systems.

The csv columns are:
- `ss_id`: The Sheffield Solar identifier of the solar PV system.
- `start_datetime_GMT`: The datetime of the first observation in the 30-minutely dataset, in UTC.
- `end_datetime_GMT`: The datetime of the last observation in the 30-minutely dataset, in UTC.
- `latitude_rounded`: Latitude of the PV system, rounded to approximately the nearest km.
- `longitude_rounded`: Longitude of the PV system, rounded to approximately the nearest km.
- `orientation`: The orientation of the PV panels, in degrees.
- `tilt`: The tilt of the PV panels with respect to the ground, in degrees. 0 degrees would be horizontal (parallel to the ground). 90 degrees would be standing perpendicular to the ground.
- `kWp`: The nominal power generation capacity of the PV system (kilowatts peak). Note that some PV systems may occasionally produce _more_ than the nominal capacity.

### {5,30}_minutely/

Time series of solar generation, stored as partitioned Parquet files.

The files contain 3 columns:
- `ss_id`: The Sheffield Solar ID of the system.
- `datetime_GMT`: The datetime of the recording in Greenwich Mean Time (UTC+0). Each row represents data for the time period _ending_ at the `datetime_GMT`. For example, a row with a timestamp of 12:00 in a `30min.parquet` file would represent the total energy generated from 11:30 to 12:00.
- `generation_Wh`: The energy generated in the period (in Watt hours) at the given timestamp for the given system. One "Watt hour" is the amount of energy generated in one hour if the power output is one watt. So, to calculate the average _power_ (in Watts), multiply the `generation_Wh` by 12 in the 5-minutely datasets; or multiple `generation_Wh` by 2 in the 30-minutely datasets.

These Parquet files are partitioned using the [Hive convention of naming the partitions](https://docs.pola.rs/user-guide/io/hive/) `key=value`. Polars, Pandas, and DuckDb should all automatically infer the partitioning schema.

The Parquet files are sorted by SS_ID and then by datetime_GMT, and so should be fast to query. Full Parquet statistics are stored in the Parquet files.

## Citing this dataset

For referencing - you can use the DOI: 10.57967/hf/0878, or this is the full reference BibteX:

```bibtex
@misc {open_climate_fix_2023,
author = { {Open Climate Fix} },
title = { uk_pv (Revision <ABBREVIATED GIT COMMIT HASH>) },
year = 2025,
url = { https://huggingface.co/datasets/openclimatefix/uk_pv },
doi = { 10.57967/hf/0878 },
publisher = { Hugging Face }
}
```

## useful links

https://huggingface.co/docs/datasets/share - this repo was made by following this tutorial