Load, wind and solar, prices in hourly resolution. This data package contains different kinds of timeseries data relevant for power system modelling, namely electricity prices, electricity consumption (load) as well as wind and solar power generation and capacities. The data is aggregated either by country, control area or bidding zone. Geographical coverage includes the EU and some neighbouring countries. All variables are provided in hourly resolution. Where original data is available in higher resolution (half-hourly or quarter-hourly), it is provided in separate files. This package version only contains data provided by TSOs and power exchanges via ENTSO-E Transparency, covering the period 2015-mid 2020. See previous versions for historical data from a broader range of sources. All data processing is conducted in Python/pandas and has been documented in the Jupyter notebooks linked below.

This Data Package contains a list of renewable energy power plants in lists of renewable energy-based power plants of Czechia, Denmark, France, Germany, Poland, Sweden, Switzerland and United Kingdom. Czechia: Renewable-energy power plants in Czech Republic. Denmark: Wind and phovoltaic power plants with a high level of detail. France: Renewable-energy power plants of various types (solar, hydro, wind, bioenergy marine, geothermal) in France. Germany: Individual power plants, all renewable energy plants supported by the German Renewable Energy Law (EEG). Poland: Summed capacity and number of installations per energy source per municipality (Powiat). Sweden: Wind power plants in Sweden. Switzerland: All renewable-energy power plants supported by the feed-in-tariff KEV (Kostendeckende Einspeisevergütung). United Kingdom: Renewable-energy power plants in the United Kingdom. Due to different data availability, the power plant lists are of different accurancy and partly provide different power plant parameter. Due to that, the lists are provided as seperate csv-files per country. Suspect data or entries with high probability of duplication are marked in the column 'comment'. Theses validation markers are explained in the file validation_marker.csv. Additionally, the Data Package includes daily time series of cumulated installed capacity per energy source type for Germany, Denmark, Switzerland, the United Kingdom and Sweden.

Source https://data.open-power-system-data.org/renewable_power_plants/2020-08-25

Detailed household load and solar generation in minutely to hourly resolution. This data package contains measured time series data for several small businesses and residential households relevant for household- or low-voltage-level power system modeling. The data includes solar power generation as well as electricity consumption (load) in a resolution up to single device consumption. The starting point for the time series, as well as data quality, varies between households, with gaps spanning from a few minutes to entire days. All measurement devices provided cumulative energy consumption/generation over time. Hence overall energy consumption/generation is retained, in case of data gaps due to communication problems. Measurements were conducted 1-minute intervals, with all data made available in an interpolated, uniform and regular time interval. All data gaps are either interpolated linearly, or filled with data of prior days. Additionally, data in 15 and 60-minute resolution is provided for compatibility with other time series data. Data processing is conducted in Jupyter Notebooks/Python/pandas.

This data package contains different kinds of timeseries data relevant for power system modelling, namely electricity prices, electricity consumption (load) as well as wind and solar power generation and capacities. The data is aggregated either by country, control area or bidding zone. Geographical coverage includes the EU and some neighbouring countries. All variables are provided in hourly resolution. Where original data is available in higher resolution (half-hourly or quarter-hourly), it is provided in separate files. This package version only contains data provided by TSOs and power exchanges via ENTSO-E Transparency, covering the period 2015-mid 2020.

Please read the readme for detailed column description

Original Data

Open Power System Data. 2020. Data Package National generation capacity. Version 2020-10-01. https://doi.org/10.25832/national_generation_capacity/2020-10-01.

Acknowlegement

Foto von Moritz Kindler auf Unsplash

Context

This dataset summarizes valuable data about recent total solar power generation and total electricity demand in a specific european country like Italy. Data are time series with hourly resolution, and values represent average of real-time power (generated and used) per market time unit (*). Data correspond to years 2015 and 2016. They are useful to analyze, for instance, the variation of solar generation with time in the four seasons of the year, the change of electricity demand depending on the day of the week, or in summer/winter holidays. Use of historical weather data could help to visualize the variation of solar power generation with climate conditions, extremely useful excercise for solar power generation forecasting. Studies of load forecasting could be also conducted by making use of the present dataset.

(*) Detailed data descriptions

Content

The two files include time series data of solar generation and total electricity consumption in Italy during the years 2015 and 2016, with hourly resolution. CSV files are structured in three columns: 1. Date and Time 2. Load 3. Solar Generation The time is expressed in Coordinated Universal Time (UTC), and the format of Date and Time is "%Y-%m-%dT%H%M%SZ". Solar generation and load are floating point numbers, which represent power expressed in MW (Mega Watts) units. Solar generation is the total solar power generated in Italy in 2015 and 2016, calculated by adding the generation in the different italian bidding zones (6 geographical regions: Nord, Centro Nord, Centro Sud, Sud, Sardegna and Sicilia, and 4 poles: Brindisi, Foggia, Priolo and Rossano). Load represents the total demand of power in the same periods. Note: the 2015 file presents a few missing data.

Acknowledgements

Data has been extracted from "Open Power System Data. 2017. Data Package Time series. Version 2017-07-09 (https://data.open-power-system-data.org/time_series/2017-07-09)."

The primary data source is ENTSO-E Transparency, the central data platform of the European transmission system operators (https://transparency.entsoe.eu).

Inspiration

Do you think we could improve the Day-ahead load forecasting? Navigate for instance the ENTSO-E Transparency website, it shows up-to-date comparisons between Day ahead total load forecast and Actual total load, by bidding zones or countries. As you will see, sometimes the differences may be significant.

Important: The ENTSO-E Platform is a great repository of energy data. Measured data and forecasts are provided to the Platform by the Primary Data Owners (see Terms and Conditions at https://transparency.entsoe.eu).

List of conventional power plants in Germany and European countries. This datapackage contains data on conventional power plants for Germany as well as other selected European countries. The data includes individual power plants with their technical characteristics. These include installed capacity, main energy source, type of technology, CHP capability, and geographical information. The geographical scope is primarily on Germany and its neighboring countries. The datapackage currently covers Germany, Austria, Belgium, Switzerland, Czech Republic, Denmark, Spain, Finland, France, Italy, the Netherlands, Norway, Poland, Sweden, Slovakia, Slovenia, and United Kingdom. Due to varying data quality of publicly available data, not all information can be provided for each country. Sources for European countries comprise detailed power plants lists from national institutions, ministries, or market participants as well as manually compiled lists of power plants for countries without a system-wide power plant list. All data processing is conducted in Python and pandas, and has been documented in the Jupyter Notebooks linked below.

Dataset provided by the Open Power System Data

This data package contains different kinds of timeseries data relevant for power system modelling, namely electricity prices, electricity consumption (load) as well as wind and solar power generation and capacities. The data is aggregated either by country, control area or bidding zone. Geographical coverage includes the EU and some neighbouring countries. All variables are provided in hourly resolution. Where original data is available in higher resolution (half-hourly or quarter-hourly), it is provided in separate files.

https://open-power-system-data.org/background/

Context

Germany country-wide totals of electricity consumption, wind power production, and solar power production for 2006-2017.

Content

This data was provided by Open Power System Data (OPSD). You can find details about the origin source of this data and details about its accuracy here.

Acknowledgements

It was referenced in this tutorial created by Jennifer Walker (Environmental scientist / data geek / Python evangelist).

Inspiration

This data is useful for time series analysis, as mentioned in the tutorial above.

Interannual Electricity Demand Calculator

Large parts of this code were originally developed by Lieke van der Most (University of Groningen) in the EU renewable energy modelling framework and release under MIT license. The original version of the code can be found here and is referenced below as [1]. This model has been validated against historical electricity demand data reported on the ENTSO-E transparancy platform.

We have made the following adjustments to the original version:

• generate hourly instead of daily electricity consumption profiles
• use snakemake for workflow management
• trim repository to demand-related code and data
• adjust code to accept cutouts from atlite for weather data

Purpose

Variations in weather conditions affect electricity demand patterns. This workflow generates country-level electricity consumption time series based on weather data using analysis by Lieke van der Most correlating historical electricity demand to temperature. This workflow first calculates a daily electricity demand based on the regression model developed in [1]. Subsequently, cumulative daily electricity demands are disaggregated using a hourly profile sampled from a random historical day (that is the same weekday) from the Open Power System Database. The resulting output/demand_hourly.csv file is compatible with the open-source electricity system model PyPSA-Eur.

Holidays are treated like weekend days. Data on national holidays across Europe are obtained using another repository by Aleksander Grochowicz and others that similarly computes artificial electricity demand time series: github.com/aleks-g/multidecade-data. The holidays are stored at input_files/noworkday.csv.

Installation and Usage

Clone the Repository

Download the demand_calculator repository using git.

/some/other/path % cd /some/path/without/spaces
/some/path/without/spaces % git clone https://github.com/martacki/demand_calculator.git

Install Dependencies with conda/mamba

Use conda or mamba to install the required packages listed in environment.yaml.

The environment can be installed and activated using

.../demand_calculator % conda env create -f environment.yaml
.../demand_calculator % conda activate demand

Retrieve Input Data

The only required additional input files are ERA5 cutouts which can be recycled from the PyPSA-Eur weather data deposit on Zenodo. Place the file europe-2013-era5.nc in the following location (and rename!):

./input_files/cutouts/europe-era5-2013.nc

Cutouts for other weather years than 2013 can be built using the build_cutout rule from the PyPSA-Eur repository.

Run the Workflow

This repository uses snakemake for workflow management. To run the complete workflow, execute:

.../demand_calculator % snakemake -jall all

After successfully running the workflow, the output files will be located in output/energy_demand named demand_hourly_{yr}.csv.

The years to compute can be modified directly in the Snakefile.

License

The file demand_hourly.csv is released under CC-BY-4.0 license.

The file src.zip is released under MIT license.

Content

There is 2 datasets from https://open-power-system-data.org/ which contains conventional power plants in EU countries.

Acknowledgements

I want to sank thank you to open-power-system-data.org for these amazing datasets.

List of renewable energy power stations. This Data Package contains a list of renewable energy power plants in lists of renewable energy-based power plants of Germany, Denmark, France, Switzerland, the United Kingdom and Poland. Germany: More than 1.7 million renewable power plant entries, eligible under the renewable support scheme (EEG). Denmark: Wind and phovoltaic power plants with a high level of detail. France: Aggregated capacity and number of installations per energy source per municipality (Commune). Poland: Summed capacity and number of installations per energy source per municipality (Powiat). Switzerland: Renewable power plants eligible under the Swiss feed in tariff KEV (Kostendeckende Einspeisevergütung). United Kingdom: Renewable power plants in the United Kingdom. Due to different data availability, the power plant lists are of different accurancy and partly provide different power plant parameter. Due to that, the lists are provided as seperate csv-files per country and as separate sheets in the excel file. Suspect data or entries with high probability of duplication are marked in the column 'comment'. Theses validation markers are explained in the file validation_marker.csv. Additionally, the Data Package includes daily time series of cumulated installed capacity per energy source type for Germany, Denmark, Switzerland and the United Kingdom. All data processing is conducted in Python and pandas and has been documented in the Jupyter Notebooks linked below.

Three primary global data outputs from the research:

• grid.gpkg: Vectorized predicted distribution and transmission line network, with existing OpenStreetMap lines tagged in the 'source' column
• targets.tif: Binary raster showing locations predicted to be connected to distribution grid.
• lv.tif: Raster of predicted low-voltage infrastructure in kilometres per cell.

This data was created with code in the following three repositories:

• https://github.com/carderne/gridfinder
• https://github.com/carderne/predictive-mapping-global-power
• https://github.com/carderne/access-estimator

Full steps to reproduce are contained in this file:

• https://github.com/carderne/predictive-mapping-global-power/blob/master/README.md

The data can be visualized at the following location:

• https://gridfinder.org

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur.

It contains alternating current lines at and above 220 kV voltage level and all high voltage direct current lines, substations, an open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power.

Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles to be downloaded and extracted as noted in the documentation.

This is the full data bundle to be used for rigorous research. It includes large bathymetry and natural protection area datasets.

While the code in PyPSA-Eur is released as free software under the GPLv3, different licenses and terms of use apply to the various input data, which are summarised below:

corine/*

• CORINE Land Cover (CLC) database
• Source: https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012/
• Extract from Terms of Use:

Access to data is based on a principle of full, open and free access as established by the Copernicus data and information policy Regulation (EU) No 1159/2013 of 12 July 2013. This regulation establishes registration and licensing conditions for GMES/Copernicus users and can be found here. Free, full and open access to this data set is made on the conditions that:

• When distributing or communicating Copernicus dedicated data and Copernicus service information to the public, users shall inform the public of the source of that data and information.

• Users shall make sure not to convey the impression to the public that the user's activities are officially endorsed by the Union.

• Where that data or information has been adapted or modified, the user shall clearly state this.

• The data remain the sole property of the European Union. Any information and data produced in the framework of the action shall be the sole property of the European Union. Any communication and publication by the beneficiary shall acknowledge that the data were produced “with funding by the European Union”.

• https://land.copernicus.eu/pan-european/corine-land-cover/clc-2012?tab=metadata

eez/*

• World exclusive economic zones (EEZ)
• Source: http://www.marineregions.org/sources.php#unioneezcountry
• Extract from Terms of Use:

Marine Regions’ products are licensed under CC-BY-NC-SA. Please contact us for other uses of the Licensed Material beyond license terms. We kindly request our users not to make our products available for download elsewhere and to always refer to marineregions.org for the most up-to-date products and services.

• http://www.marineregions.org/disclaimer.php

natura/*

• Natura 2000 natural protection areas
• Source: https://www.eea.europa.eu/data-and-maps/data/natura-10
• Extract from Terms of Use:

EEA standard re-use policy: unless otherwise indicated, re-use of content on the EEA website for commercial or non-commercial purposes is permitted free of charge, provided that the source is acknowledged (https://www.eea.europa.eu/legal/copyright). Copyright holder: Directorate-General for Environment (DG ENV).

• https://www.eea.europa.eu/data-and-maps/data/natura-10#tab-metadata

naturalearth/*

• World country shapes
• Source: https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-countries/
• Extract from Terms of Use:

All versions of Natural Earth raster + vector map data found on this website are in the public domain. You may use the maps in any manner, including modifying the content and design, electronic dissemination, and offset printing. The primary authors, Tom Patterson and Nathaniel Vaughn Kelso, and all other contributors renounce all financial claim to the maps and invites you to use them for personal, educational, and commercial purposes.

No permission is needed to use Natural Earth. Crediting the authors is unnecessary.

• http://www.naturalearthdata.com/about/terms-of-use/

NUTS_2013_60M_SH/*

• Europe NUTS3 regions
• Source: https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units
• Extract from Terms of Use:

In addition to the general copyright and licence policy applicable to the whole Eurostat website, the following specific provisions apply to the datasets you are downloading. The download and usage of these data is subject to the acceptance of the following clauses:

1. The Commission agrees to grant the non-exclusive and not transferable right to use and process the Eurostat/GISCO geographical data downloaded from this page (the "data").

2. The permission to use the data is granted on condition that: the data will not be used for commercial purposes; the source will be acknowledged. A copyright notice, as specified below, will have to be visible on any printed or electronic publication using the data downloaded from this page.

• https://ec.europa.eu/eurostat/web/gisco/geodata/reference-data/administrative-units-statistical-units
• https://ec.europa.eu/eurostat/about/policies/copyright

ch_cantons.csv

• Mapping between Swiss Cantons and NUTS3 regions
• Source: https://en.wikipedia.org/wiki/Data_codes_for_Switzerland
• Extract from Terms of Use:

Creative Commons Attribution-ShareAlike 3.0 Unported License

• https://en.wikipedia.org/wiki/Data_codes_for_Switzerland

EIA_hydro_generation_2000_2014.csv

• Hydroelectricity generation per country and year
• Source: https://www.eia.gov/beta/international/data/browser/#/?pa=000000000000000000000000000000g&c=1028i008006gg6168g80a4k000e0ag00gg0004g800ho00g8&ct=0&ug=8&tl_id=2-A&vs=INTL.33-12-ALB-BKWH.A&cy=2014&vo=0&v=H&start=2000&end=2016
• Extract from Terms of Use:

Public domain and use of EIA content: U.S. government publications are in the public domain and are not subject to copyright protection. You may use and/or distribute any of our data, files, databases, reports, graphs, charts, and other information products that are on our website or that you receive through our email distribution service. However, if you use or reproduce any of our information products, you should use an acknowledgment, which includes the publication date, such as: "Source: U.S. Energy Information Administration (Oct 2008)."

• https://www.eia.gov/about/copyrights_reuse.php

GEBCO_2014_2D.nc

• GEBCO bathymetric dataset
• Source: https://www.gebco.net/data_and_products/gridded_bathymetry_data/version_20141103/
• Extract from Terms of Use:

The GEBCO Grid is placed in the public domain and may be used free of charge. Use of the GEBCO Grid

Open Power System Data platform

Context

The goal of this dataset is to forecast wind power generation at a daily rate using different time series and traditional machine learning models.

Content

This dataset is pretty simple, It's a time series dataset containing measurements of daily temperature, wind production and capacity from 2017 to 2019.

The columns in the dataset are : * utc_timestamp : Time in UTC * wind_generation : Daily wind production in MW * wind_capacity : Electrical capacity of wind in MW * temperature : Daily Temperature in degrees C

Acknowledgements

I would like to thank OPSD for making this dataset available publicly. https://open-power-system-data.org/

Simulated hourly country-aggregated heat demand and COP time series. This dataset comprises national time series for representing building heat pumps in power system models. The heat demand of buildings and the coefficient of performance (COP) of heat pumps is calculated for 28 European countries from 2008 to 2022 in an hourly resolution. Heat demand time series for space and water heating are computed by combining gas standard load profiles with spatial temperature and wind speed reanalysis data as well as population geodata. The profiles are year-wise scaled to 1 TWh each. For the years 2008 to 2015, the data is additionally scaled with annual statistics on the final energy consumption for heating. COP time series for different heat sources – air, ground, and groundwater – and different heat sinks – floor heating, radiators, and water heating – are calculated based on COP and heating curves using reanalysis temperature data, spatially aggregated with respect to the heat demand, and corrected based on field measurements. All data processing as well as the download of relevant input data is conducted in python and pandas and has been documented in the Jupyter notebooks linked below. Please also consider and cite our Data Descriptor of the original dataset as well as our Working Paper at on recent updates and extensions of the dataset.

This dataset is based on the When2Heat dataset, which provides an hourly-resolution time series representing building heat pumps in power system models for 28 European countries from 2008 to 2022. It is accompanied by supplementary data files (see data directory) to enhance usability and improve data preprocessing.

Kindly refer to https://data.open-power-system-data.org/when2heat/2023-07-27 for more detailed information about the dataset.

📄 Original Notes

"This dataset comprises national time series for representing building heat pumps in power system models. The heat demand of buildings and the coefficient of performance (COP) of heat pumps is calculated for 28 European countries from 2008 to 2022 in an hourly resolution. Heat demand time series for space and water heating are computed by combining gas standard load profiles with spatial temperature and wind speed reanalysis data as well as population geodata. The profiles are year-wise scaled to 1 TWh each. For the years 2008 to 2015, the data is additionally scaled with annual statistics on the final energy consumption for heating. COP time series for different heat sources – air, ground, and groundwater – and different heat sinks – floor heating, radiators, and water heating – are calculated based on COP and heating curves using reanalysis temperature data, spatially aggregated with respect to the heat demand, and corrected based on field measurements. All data processing as well as the download of relevant input data is conducted in python and pandas and has been documented in the Jupyter notebooks linked below. Please also consider and cite our Data Descriptor of the original dataset as well as our Working Paper at on recent updates and extensions of the dataset."

📝 Attribution

Data license: Creative Commons Attribution 4.0

Script license: MIT License

License Attribution: Attribution should be given as follows:

• Ruhnau, O., Hirth, L., and Praktiknjo, A. (2019). Time series of heat demand and heat pump efficiency for energy system modeling. Scientific Data, 6, 189. https://doi.org/10.1038/s41597-019-0199-y
• Ruhnau, O., Muessel, J. (2023). Update and extension of the When2Heat dataset. Econstor Working Paper. http://hdl.handle.net/10419/249997
• Ruhnau, O., Muessel, J. (2023). When2Heat Heating Profiles. Open Power System Data. https://doi.org/10.25832/when2heat/2023-07-27

💡 Potential Use Cases

📊 Machine Learning:

Time-Series Forecasting – Predict future heating demand Predicting heating demand based on temperature trends Developing energy efficiency models

🔬 Climate Research:

Analyzing how temperature fluctuations affect energy use

🏙 Urban & Energy Planning:

Improving district heating infrastructure

Hourly geographically aggregated weather data for Europe. This data package contains radiation and temperature data, at hourly resolution, for Europe, aggregated by Renewables.ninja from the NASA MERRA-2 reanalysis. It covers the European countries using a population-weighted mean across all MERRA-2 grid cells within the given country.

The dataset provided here is intended for publication - Harmonized and Open Energy Dataset for Modeling a Highly Renewable Brazilian Power System. The assembled dataset comprises the following subcategories as detailed in the Method section of the publication: i) map of the defined region, ii) aggregated grid network topology, iii) vRES potentials – profile and installable generation capacity, iv) geographically installable capacity of biomass thermal plants, v) hydropower plants inflow, vi) existing and planned power generators with their capacity, vii) electricity load profile, viii) scenarios of sectoral energy demand and iv) cross-border electricity exchanges. This dataset is resolved geographically by Brazilian federal states, and time series data are resolved by hours, spanning the period 2012-2020.

The dataset can be used as input to popular open energy system models such as PyPSA and any other modeling framework.

Aggregated generation capacity by technology and country. This data package comprises technology-specific aggregated generation capacities for European countries. The generation capacities are consistently categorized based on fuel and technology. For each European country, various references are used ranging from international (e.g. ENTSOE or EUROSTAT) to national sources from e.g. regulatory authorities. The input data is processed in the script linked below.