The POWER Project contains over 380 satellite-derived meteorology and solar energy Analysis Ready Data (ARD) at four temporal levels: hourly, daily, monthly (by year 12 months + annual averages), and climatology. The POWER Data Archive provides data at the native resolution of the source data products. The data is updated nightly to maintain Near Real Time (NRT) availability (2-3 days for meteorological parameters and 5-7 days for solar). The POWER Project targets three specific user communities: Renewable Energy (RE), Sustainable Buildings (SB), and Agroclimatology (AG). The POWER Projects provides community specific parameters, output formats, naming conventions, and units that are commonly employed by each user community. The POWER Services Catalog consists of a series of RESTful Application Programming Interfaces (API), geospatial enabled image services, and a web mapping Data Access Viewer (DAV). These three different service offerings support data discovery, access, and distribution to our user base as ARD and as direct application inputs to decision support tools.

NASA's goal in Earth science is to observe, understand, and model the Earth system to discover how it is changing, to better predict change, and to understand the consequences for life on Earth. The Applied Sciences Program, within the Earth Science Division of the NASA Science Mission Directorate, serves individuals and organizations around the globe by expanding and accelerating societal and economic benefits derived from Earth science, information, and technology research and development.

The Prediction Of Worldwide Energy Resources (POWER) Project, funded through the Applied Sciences Program at NASA Langley Research Center, gathers NASA Earth observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in energy development, building energy efficiency, and supporting agriculture projects.

The POWER project contains over 380 satellite-derived meteorology and solar energy Analysis Ready Data (ARD) at four temporal levels: hourly, daily, monthly, and climatology. The POWER data archive provides data at the native resolution of the source products. The data is updated nightly to maintain near real time availability (2-3 days for meteorological parameters and 5-7 days for solar). The POWER services catalog consists of a series of RESTful Application Programming Interfaces, geospatial enabled image services, and web mapping Data Access Viewer. These three service offerings support data discovery, access, and distribution to the project’s user base as ARD and as direct application inputs to decision support tools.

The latest data version update includes hourly-based source ARD, in addition to enhanced daily, monthly, annual, and climatology data. The daily time series for meteorology is available from 1981, while solar-based parameters start in 1984. The hourly source data are from Clouds and the Earth's Radiant Energy System (CERES) and Global Modeling and Assimilation Office (GMAO), spanning from 1984 for meteorology and from 2001 for solar-based parameters. The hourly data equips users with the ARD needed to model building system energy performance, providing information directly amenable to decision support tools introducing the industry standard EnergyPlus Weather file format.

2021FEBEREC-STD-117

The data set consists of the hourly load demand, hourly solar irradiance and hourly wind speed of a community located in Northern part of Nigeria named Bara, Kirfi Local Government area of Bauchi state, Nigeria. The hourly solar resource data and wind resource data of the community for a period of one year is obtained from an existing database of the Power Data Access Viewer of National Aeronautic and Space Administration (NASA).

The Prediction of Worldwide Energy Resource (POWER) Project is funded through the NASA Applied Sciences Program within the Earth Science Mission Directorate. The POWER Project supports three user communities with solar and/or meteorological data: 1) Renewable Energy (RE), 2) Sustainable Buildings (SB), and 3) Agroclimatology (AG)POWER Data Sources:The POWER project provides access to community-based Analysis Ready Data (ARD) for meteorology and solar-related parameters, specifically formulated for assessing and designing renewable energy systems.The data is available on at the source models’ native latitude and longitude global grid.Temporal levels include Hourly, Daily, Monthly, Annual, and Climatology. Download options include single point, regional, and global data.Formats include NetCDF, CSV, ASCII, geoJSON, ICASA, & EPW.Meteorological parameters are derived from:NASA's GMAO MERRA-2 archive (Jan. 1, 1981 – 3 Months Behind Near Real Time)NASA's GEOS 5.12.4 FP-IT archive (End of MERRA2 – Near Real Time)Solar parameters are derived from:NASA's GEWEX/SRB release 4.0 archive (Jan. 1, 1984 – Dec. 31, 2000) NASA's CERES SYN1deg (Jan. 1, 2001 – 3 Months Behind Near Real Time)NASA's FLASHFlux (3 Months Behind Near Real Time – Near Real Time)If you have any comments or questions, please do not hesitate to contact us at larc-power-project@mail.nasa.gov

The Prediction Of Worldwide Energy Resource (POWER) Project gathers NASA Earth Observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access, and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in renewable energy development, building energy efficiency, and agriculture sustainability. POWER is funded through the NASA Earth Action Program within the Earth Science Mission Directorate at NASA Langley Research Center (LaRC).---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------This monthly radiation service provides time-enabled global Analysis Ready Data (ARD) parameters from 1984 to 2023 for the NASA POWER Project's communities.Time Interval: MonthlyTime Extent: 1984/01/01 to 2023/12/31Time Standard: Local Sidereal Time (LST)Grid Size: 1.0 X 1.0 DegreeProjection: GCS WGS84Extent: GlobalSource: NASA Prediction Of Worldwide Energy Resources (POWER)Radiation Data Sources:NASA's GEWEX/SRB release 4.0 archive (Jan. 1, 1984 – Dec. 31, 2000)NASA's CERES SYN1deg (Jan. 1, 2001 – Dec. 31, 2021)For questions or issues please email: larc-power-project@mail.nasa.govRadiation Data Parameters:ALLSKY_KT (All Sky Insolation Clearness Index): A fraction representing clearness of the atmosphere; the all sky insolation that is transmitted through the atmosphere to strike the surface of the earth divided by the average of top of the atmosphere total solar irradiance incident.ALLSKY_SFC_LW_DWN (All Sky Surface Longwave Downward Irradiance): The downward thermal infrared irradiance under all sky conditions reaching a horizontal plane the surface of the earth. Also known as Horizontal Infrared Radiation Intensity from Sky.ALLSKY_SFC_LW_UP (All Sky Surface Longwave Upward Irradiance): The upward thermal infrared irradiance under all sky conditions.ALLSKY_SFC_PAR_TOT (All Sky Surface PAR Total): The total Photosynthetically Active Radiation (PAR) incident on a horizontal plane at the surface of the earth under all sky conditions.ALLSKY_SFC_SW_DIFF (All Sky Surface Shortwave Diffuse Irradiance): The diffuse (light energy scattered out of the direction of the sun) solar irradiance incident on a horizontal plane at the surface of the earth under all sky conditions.ALLSKY_SFC_SW_DNI (All Sky Surface Shortwave Downward Direct Normal Irradiance): The direct solar irradiance incident to a horizontal plane normal (perpendicular) to the direction of the sun's position under all sky conditions.ALLSKY_SFC_SW_DWN (All Sky Surface Shortwave Downward Irradiance): The total solar irradiance incident (direct plus diffuse) on a horizontal plane at the surface of the earth under all sky conditions. An alternative term for the total solar irradiance is the "Global Horizontal Irradiance" or GHI.ALLSKY_SFC_SW_UP (All Sky Surface Shortwave Upward Irradiance): The upward shortwave irradiance under all sky conditions.ALLSKY_SFC_UV_INDEX (All Sky Surface UV Index): The ultraviolet radiation exposure index.ALLSKY_SFC_UVA (All Sky Surface UVA Irradiance): The ultraviolet A (UVA 315nm-400nm) irradiance under all sky conditions.ALLSKY_SFC_UVB (All Sky Surface UVB Irradiance): The ultraviolet B (UVB 280nm-315nm) irradiance under all sky conditions.ALLSKY_SRF_ALB (All Sky Surface Albedo): The all sky rate of reflectivity of the earth's surface; the ratio of the solar energy reflected by the surface of the earth compared to the total solar energy incident reaching the surface of the earth.AOD_55 (Aerosol Optical Depth 55): The optical thickness at 0.55 um measured vertically; the component of the atmosphere to quantify the removal of radiant energy from an incident beam.AOD_55_ADJ (Adjusted Aerosol Optical Depth 55): The adjusted optical thickness at 0.55 um measured vertically; the component of the atmosphere to quantify the removal of radiant energy from an incident beam.CLOUD_AMT (Cloud Amount): The average percent of cloud amount during the temporal period.CLOUD_AMT_DAY (Cloud Amount at Daytime): The average percent of cloud amount during daylight.CLOUD_AMT_NIGHT (Cloud Amount at Nighttime): The average percent of cloud amount during nighttime.CLOUD_OD (Cloud Optical Visible Depth): The vertical optical thickness between the top and bottom of a cloud.CLRSKY_DAYS (Clear Sky Day): The number of Clear Sky Days if the daytime cloud amount is less than 10 percent.CLRSKY_KT (Clear Sky Insolation Clearness Index): A fraction representing clearness of the atmosphere; the clear sky insolation that is transmitted through the atmosphere to strike the surface of the earth divided by the average of top of the atmosphere total solar irradiance incident.CLRSKY_SFC_LW_DWN (Clear Sky Surface Longwave Downward Irradiance): The downward thermal infrared irradiance under clear sky conditions reaching a horizontal plane the surface of the earth. Also known as Horizontal Infrared Radiation Intensity from Sky.CLRSKY_SFC_LW_UP (Clear Sky Surface Longwave Upward Irradiance): The upward thermal infrared irradiance under clear sky conditions.CLRSKY_SFC_PAR_TOT (Clear Sky Surface PAR Total): The total Photosynthetically Active Radiation (PAR) incident on a horizontal plane at the surface of the earth under clear sky conditions.CLRSKY_SFC_SW_DIFF (Clear Sky Surface Shortwave Downward Diffuse Horizontal Irradiance): The diffuse (light energy scattered out of the direction of the sun) solar irradiance incident on a horizontal plane at the surface of the earth under clear sky conditions.CLRSKY_SFC_SW_DNI (Clear Sky Surface Shortwave Downward Direct Normal Irradiance): The direct solar irradiance incident to a horizontal plane normal (perpendicular) to the direction of the sun's position under clear sky conditions.CLRSKY_SFC_SW_DWN (Clear Sky Surface Shortwave Downward Irradiance): The total solar irradiance incident (direct plus diffuse) on a horizontal plane at the surface of the earth under clear sky conditions. An alternative term for the total solar irradiance is the "Global Horizontal Irradiance" or GHI.CLRSKY_SFC_SW_UP (Clear Sky Surface Shortwave Upward Irradiance): The upward shortwave irradiance under clearsky conditions.CLRSKY_SRF_ALB (Clear Sky Surface Albedo): The clear sky rate of reflectivity of the earth's surface; the ratio of the solar energy reflected by the surface of the earth compared to the total solar energy incident reaching the surface of the earth.MIDDAY_INSOL (Midday Insolation Incident): The total amount of solar irradiance (i.e. direct plus diffuse) incident on a horizontal plane at the earth's surface during the solar noon hour midday period.PW (Precipitable Water): The total atmospheric water vapor contained in a vertical column of the atmosphere.TOA_SW_DNI (Top-Of-Atmosphere Shortwave Direct Normal Radiation): The total solar irradiance incident (direct plus diffuse) on a horizontal plane where oriented to the sun's position at the top of the atmosphere (extraterrestrial radiation).TOA_SW_DWN (Top-Of-Atmosphere Shortwave Downward Irradiance): The total solar irradiance incident (direct plus diffuse) on a horizontal plane at the top of the atmosphere (extraterrestrial radiation).TS_ADJ (Earth Skin Temperature Adjusted): The adjusted average temperature at the earth's surface.

The Near Real-time Data Access (NeRDA) Portal is making near real-time data available to our stakeholders and interested parties. We're helping the transition to a smart, flexible system that connects large-scale energy generation right down to the solar panels and electric vehicles installed in homes, businesses and communities right across the country. In line with our Open Networks approach, our Near Real-time Data Access (NeRDA) portal is live and making available power flow information from our EHV, HV, and LV networks, taking in data from a number of sources, including SCADA PowerOn, our installed low voltage monitoring equipment, load model forecasting tool, connectivity model, and our Long-Term Development Statement (LTDS). Making near real-time data accessible from DNOs is facilitating an economic and efficient development and operation in the transition to a low carbon economy. NeRDA is a key enabler for the delivery of Net Zero - by opening network data, it is creating opportunities for the flexible markets, helping to identify the best locations to invest flexible resources, and connect faster. You can access this information via our informative near real-time Dashboard and download portions of data or connect to our API and receive an ongoing stream of near real-time data.

which applies to both positive and negative half cycles. One additional feature is that it does not require external power to turn on the bidirectional switches (Vth < 0.3 V).

The Global Power Plant Database is a comprehensive, open source database of power plants around the world. It centralizes power plant data to make it easier to navigate, compare and draw insights. Each power plant is geolocated and entries contain information on plant capacity, generation, ownership, and fuel type. As …

This map shows electricity access in Africa. The data source is from the International Energy Agency’s World Energy Outlook. The International Energy Agency’s World Energy Outlook first constructed a database on electrification rates for WEO-2002. The database once again was updated for WEO-2015, showing detailed data on national, urban and rural electrification.

The general paucity of data on electricity access means that it must be gathered through a combination of sources, including: IEA energy statistics; a network of contacts spanning governments, multilateral development banks and country-level representatives of various international organisations; and, other publicly available statistics, such as US Agency for International Development (USAID) supported DHS survey data, the World Bank’s Living Standards Measurement Surveys (LSMS), the UN Economic Commission for Latin America and the Caribbean’s (ECLAC) statistical publications, and data from national statistics agencies. In the small number of cases where no data could be provided through these channels other sources were used. If electricity access data for 2013 was not available, data for the latest available year was used.

For many countries, data on the urban and rural breakdown was collected, but if not available an estimate was made on the basis of pre-existing data or a comparison to the average correlation between urban and national electrification rates. Often only the percentage of households with a connection is known and assumptions about an average household size are used to determine access rates as a percentage of the population. To estimate the number of people without access, population data comes from OECD statistics in conjunction with the United Nations Population Division reports World Urbanization Prospects: the 2014 Revision Population Database, and World Population Prospects: the 2012 Revision. Electricity access data is adjusted to be consistent with demographic patterns of urban and rural population. Due to differences in definitions and methodology from different sources, data quality may vary from country to country. Where country data appeared contradictory, outdated or unreliable, the IEA Secretariat made estimates based on cross-country comparisons and earlier surveys.

Abstract: Monthly and annual average solar resource potential for the lower 48 states of the United States of America.

Purpose: Provide information on the solar resource potential for the for the lower 48 states of the United States of America.

Supplemental Information: This data provides monthly average and annual average daily total solar resource averaged over surface cells of approximatley 40 km by 40 km in size. This data was developed from the Climatological Solar Radiation (CSR) Model. The CSR model was developed by the National Renewable Energy Laboratory for the U.S. Department of Energy. Specific information about this model can be found in Maxwell, George and Wilcox (1998) and George and Maxwell (1999). This model uses information on cloud cover, atmostpheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. The cloud cover data used as input to the CSR model are an 7-year histogram (1985-1991) of monthly average cloud fraction provided for grid cells of approximately 40km x 40km in size. Thus, the spatial resolution of the CSR model output is defined by this database. The data are obtained from the National Climatic Data Center in Ashville, North Carolina, and were developed from the U.S. Air Force Real Time Nephanalysis (RTNEPH) program. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. The procedures for converting the collector at latitude tilt are described in Marion and Wilcox (1994). Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalible at a 40km resolution. As a result, it is believed that the modeled values are accurate to approximately 10% of a true measured value within the grid cell. Due to terrain effects and other micoclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain.

Other Citation Details: George, R, and E. Maxwell, 1999: "High-Resolution Maps of Solar Collector Performance Using A Climatological Solar Radiation Model", Proceedings of the 1999 Annual Conference, American Solar Energy Society, Portland, ME.

License Info

This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

Access to electricity (% of population) in United States was reported at 100 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. United States - Access to electricity (% of population) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

This dataset is as presented in the paper titled "Data article: Distributed PV power data for three cities in Australia." in the Journal of Renewable and Sustainable Energy, volume 11 by Jamie M, Bright, Sven Killinger and Nicholas A. Engerer.

Abstract:
We present a publicly available dataset containing photovoltaic (PV) system power measurements and metadata from 1,287 residential installations across three states/territories in Australia--- though mainly for the cities of Canberra, Perth and Adelaide.
The data is recorded between September 2016 and March 2017 at 10-min temporal resolution and consists of real inverter reported power measurements from PV systems that are well distributed throughout each city. The dataset represents a considerably valuable resource as public access to spatio-temporal PV power data is almost non-existent; this dataset has been used in numerous articles already by the authors. The PV power data is free to download and is available in its raw, quality controlled (QC) and `tuned' formats. Each PV system is accompanied by individual metadata including geolocation, user reported metadata and simulated parameterisation. Data provenance,download, usage rights and example usage are detailed within.
Researchers are encouraged to leverage this rich spatio-temporal dataset of distributed PV power data in their research.

Further information is available at ANU Data Commons and Solcast.
This dataset has an embargo period for 3 years after the ARENA funded ANU project closure, though data is always available through Solcast.

Usage rights:

There is a non-standard data usage rights agreement for this data. In the uploads is a 'license and metadata.txt' file that details the usage rights and metadata of the data. The exact agreement is reproduced here:

The data is released with bespoke terms. We state the crucial elements of these terms here. The dataset is freely provided to researchers as is with no guarantee of support. The dataset is not for commercial usage, but for research only. You are empowered to use this dataset however you wish in your research, through direct usage, adaptation, or improvements to the data itself. The data must not be redistributed, the access point for the data is exclusively through the website as described in Sec.III of the manuscript. Should you make significant changes to the data and wish to redistribute the new data, explicit permission must be obtained from the authors. Finally, appropriate accreditation to the creators must be made in all publications and outputs that arise from using this dataset in any way. To appropriately accredit the creators, we require that this exact data article (Bright et al., 2019) is referenced alongside its DOI: https://dx.doi.org/10.25911/5ca6a0640869a. Additionally, if using the QC version of the data, we also require a citation for the original papers detailing QCPV (Killinger et al., 2016a, 2016a). Furthermore, if using the tuned PV version of this data, we also require a citation for both the QCPV papers above and the PV tuning papers (Killinger et al., 2016b, 2017b) for full visibility of the data provenance. Lastly, the original hosts of this data PVoutput.org should be recognised for their efforts.

References:

Bright, Jamie M.; Killinger, Sven; and Engerer, Nicholas A. 2019. Data article: Distributed PV power data for three cities in Australia. Journal of Renewable and Sustainable Energy. Vol 11. See online for full details.

Killinger, Sven; Braam, Felix; Muller, Bjorn; Wille-Haussmann, Bernhard and McKenna, Russell, 2016a. Projection of power generation between differently-oriented PV systems. Solar Energy. 136, 153-165.

Killinger, Sven; Muller, Bjorn; Saint-Drenan, Yves Marie and McKenna, Russell. 2016b. Towards an improved nowcasting method by evaluating power profiles of PV systems to detect apparently atypical behavior. Conference Record of the IEEE Photovoltaic specialists Conference, pages 980-985.10.1109/PVSC.2016.7749757

Killinger, Sven; Engerer, Nicholas and Müller, Björn. 2017a. QCPV: A quality control algorithm for distributed photovoltaic array power output. Solar Energy. 143, 120-131.

Killinger, Sven; Bright, Jamie M.; Lingfors, David and Engerer, Nicholas A. 2017b. A tuning routine to correct systematic influences in reference PV systems’ power outputs. Solar Energy. 157, 6.

The National Solar Radiation Database (NSRDB) was produced by the National Renewable Energy Laboratory under the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy. The 1991-2010 NSRDB is an update of the 1991-2005 NSRDB released in 2006 and archived at NCDC. The serially complete hourly data provided in the NSRDB update are provided in two output formats: 1) ground-based solar and meteorological dataset, and 2) 10 km gridded output produced by the SUNY model. The 10 km gridded output is from the State University of New York/Albany (SUNY) satellite radiation model developed by Richard Perez and Clean Power Research. Data in the NSRDB are a slightly modified version of the SolarAnywhere dataset distributed by Clear Power Research. The modifications are detailed in the NSRDB User's Manual. The model uses hourly radiance images estimated from Geostationary Operational Environmental Satellite (GOES) imagery, daily snow cover data, and monthly averages of atmospheric water vapor, trace gases, and the amount of aerosols in the atmosphere to calculate the hourly total irradiance (sun and sky) falling on a horizontal surface. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. In simple terms, this satellite model uses the inverse relationship between reflected irradiance (that reflected by clouds and atmosphere back to space and the satellite sensor) and ground irradiance (that transmitted through the atmosphere to the Earth's surface). The high-resolution 10-km gridded data set from the SUNY model provides a consistency in modeled output data for its period of record for the years 1998 to 2009, the period for which necessary GOES imagery was available for the project. The SUNY model produces estimates of global and direct irradiance at hourly intervals on the 10-km grid for 49 states, excluding Alaska, where the geostationary satellites cannot resolve cloud cover with necessary detail. Although GOES images provide up to 1-km resolution, in the SUNY model, these data are down-sampled to 10-km resolution (0.1 degree x 0.1 degree). This resolution is adequate for most solar radiation resource applications and represents a practical trade-off between resolution and processing and data storage considerations. The model uses both GOES-East and GOES-West satellites for complete spatial coverage of the United States.

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 MIT, 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

gebco/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 indicates that the user accepts the conditions of use and disclaimer information given below.

Users are free to:

• Copy, publish, distribute and transmit The GEBCO Grid

• Adapt The GEBCO Grid

• Commercially exploit The GEBCO Grid, by, for example, combining it with other information, or by including it in their own product or application

Users must:

• Acknowledge the source of The GEBCO Grid. A suitable form of attribution is given in the documentation that accompanies The GEBCO Grid.

• Not use The GEBCO Grid in a way that suggests any official status or that GEBCO, or the IHO or IOC, endorses any particular application of The GEBCO Grid.

• Not mislead others or misrepresent The GEBCO Grid or its source.

• https://www.gebco.net/data_and_products/gridded_bathymetry_data/documents/gebco_2014_historic.pdf

je-e-21.03.02.xls

• Population and GDP data for Swiss Cantons
• Source: https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html
• Extract from Terms of Use:

Information on the websites of the Federal Authorities is accessible to the public. Downloading, copying or integrating content (texts, tables, graphics, maps, photos or any other data) does not entail any transfer of rights to the content.

Copyright and any other rights relating to content available on the websites of the Federal Authorities are the exclusive property of the Federal Authorities or of any other expressly mentioned owners.

Any reproduction requires the prior written consent of the copyright holder. The source of the content (statistical results) should always be given.

Abstract: Monthly and annual average solar resource potential for Hawaii.

Purpose: Provide information on the solar resource potential for Hawaii. The insolation values represent the average solar energy available to a flat plate collector, such as a photovoltaic panel, oriented due south at an angle from horizontal equal to the latitude of the collector location.

Supplemental Information: This data provides monthly average and annual average daily total solar resource averaged over surface cells of approximately 40 km by 40 km in size. This data was developed from the Climatological Solar Radiation (CSR) Model. The CSR model was developed by the National Renewable Energy Laboratory for the U.S. Department of Energy. Specific information about this model can be found in Maxwell, George and Wilcox (1998) and George and Maxwell (1999). This model uses information on cloud cover, atmostpheric water vapor and trace gases, and the amount of aerosols in the atmosphere to calculate the monthly average daily total insolation (sun and sky) falling on a horizontal surface. The cloud cover data used as input to the CSR model are an 7-year histogram (1985-1991) of monthly average cloud fraction provided for grid cells of approximately 40km x 40km in size. Thus, the spatial resolution of the CSR model output is defined by this database. The data are obtained from the National Climatic Data Center in Ashville, North Carolina, and were developed from the U.S. Air Force Real Time Nephanalysis (RTNEPH) program. Atmospheric water vapor, trace gases, and aerosols are derived from a variety of sources. The procedures for converting the collector at latitude tilt are described in Marion and Wilcox (1994). Where possible, existing ground measurement stations are used to validate the data. Nevertheless, there is uncertainty associated with the meterological input to the model, since some of the input parameters are not avalible at a 40km resolution. As a result, it is believed that the modeled values are accurate to approximately 10% of a true measured value within the grid cell. Due to terrain effects and other micoclimate influences, the local cloud cover can vary significantly even within a single grid cell. Furthermore, the uncertainty of the modeled estimates increase with distance from reliable measurement sources and with the complexity of the terrain. Units are in watt hours.

Other Citation Details:

George, R, and E. Maxwell, 1999: "High-Resolution Maps of Solar Collector Performance Using A Climatological Solar Radiation Model", Proceedings of the 1999 Annual Conference, American Solar Energy Society, Portland, ME.

Maxwell, E, R. George and S. Wilcox, "A Climatological Solar Radiation Model", Proceedings of the 1998 Annual Conference, American Solar Energy Society, Albuquerque NM.

License Info

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This blog post was posted by Susannah Fox on February 22, 2016

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The Prediction Of Worldwide Energy Resource (POWER) Project gathers NASA Earth Observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access, and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in renewable energy development, building energy efficiency, and agriculture sustainability. POWER is funded through the NASA Earth Action Program within the Earth Science Mission Directorate at NASA Langley Research Center (LaRC).---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------This monthly meteorology service provides time-enabled global Analysis Ready Data (ARD) parameters from 1981 to 2023 for POWER’s communities. Time Interval: MonthlyTime Extent: 1981/01/01 to 2023/12/31Time Standard: Local Sidereal Time (LST)Grid Size: 0.5 x 0.5 DegreeProjection: GCS WGS84Extent: GlobalSource: NASA Prediction Of Worldwide Energy Resources (POWER)For questions or issues please email: larc-power-project@mail.nasa.govMeteorology Data Sources:NASA's GMAO MERRA-2 archive (Jan. 1, 1981 – Dec. 31, 2021)Meteorology Data Parameters:CDD10 (Cooling Degree Days Above 10 C): The daily accumulation of degrees when the daily mean temperature is above 10 degrees Celsius.CDD18_3 (Cooling Degree Days Above 18.3 C): The daily accumulation of degrees when the daily mean temperature is above 18.3 degrees Celsius.DISPH (Zero Plane Displacement Height): The height at which the mean velocity is zero due to large obstacles such as buildings/canopy.EVLAND (Evaporation Land): The evaporation over land at the surface of the earth.EVPTRNS (Evapotranspiration Energy Flux): The evapotranspiration energy flux at the surface of the earth.FROST_DAYS (Frost Days): A frost day occurs when the 2m temperature cools to the dew point temperature and both are less than 0 C or 32 F.GWETTOP (Surface Soil Wetness): The percent of soil moisture a value of 0 indicates a completely water-free soil and a value of 1 indicates a completely saturated soil; where surface is the layer from the surface 0 cm to 5 cm below grade.HDD10 (Heating Degree Days Below 10 C): The daily accumulation of degrees when the daily mean temperature is below 10 degrees Celsius.HDD18_3 (Heating Degree Days Below 18.3 C): The daily accumulation of degrees when the daily mean temperature is below 15.3 degrees Celsius.PBLTOP (Planetary Boundary Layer Top Pressure): The pressure at the top of the planet boundary layer.PRECSNOLAND_SUM (Snow Precipitation Land Sum): The snow precipitation sum over land at the surface of the earth.PRECTOTCORR_SUM (Precipitation Corrected Sum): The bias corrected sum of total precipitation at the surface of the earth.PS (Surface Pressure): The average of surface pressure at the surface of the earth.QV10M (Specific Humidity at 10 Meters): The ratio of the mass of water vapor to the total mass of air at 10 meters (kg water/kg total air).QV2M (Specific Humidity at 2 Meters): The ratio of the mass of water vapor to the total mass of air at 2 meters (kg water/kg total air).RH2M (Relative Humidity at 2 Meters): The ratio of actual partial pressure of water vapor to the partial pressure at saturation, expressed in percent.T10M (Temperature at 10 Meters): The air (dry bulb) temperature at 10 meters above the surface of the earth.T2M (Temperature at 2 Meters): The average air (dry bulb) temperature at 2 meters above the surface of the earth.T2MDEW (Dew/Frost Point at 2 Meters): The dew/frost point temperature at 2 meters above the surface of the earth.T2MWET (Wet Bulb Temperature at 2 Meters): The adiabatic saturation temperature which can be measured by a thermometer covered in a water-soaked cloth over which air is passed at 2 meters above the surface of the earth.TO3 (Total Column Ozone): The total amount of ozone in a column extending vertically from the earth's surface to the top of the atmosphere.TQV (Total Column Precipitable Water): The total atmospheric water vapor contained in a vertical column of unit cross-sectional area extending from the surface to the top of the atmosphere.TS (Earth Skin Temperature): The average temperature at the earth's surface.WD10M (Wind Direction at 10 Meters): The average of the wind direction at 10 meters above the surface of the earth.WD2M (Wind Direction at 2 Meters): The average of the wind direction at 2 meters above the surface of the earth.WD50M (Wind Direction at 50 Meters): The average of the wind direction at 50 meters above the surface of the earth.WS10M (Wind Speed at 10 Meters): The average of wind speed at 10 meters above the surface of the earth.WS2M (Wind Speed at 2 Meters): The average of wind speed at 2 meters above the surface of the earth.WS50M (Wind Speed at 50 Meters): The average of wind speed at 50 meters above the surface of the earth.

Access to electricity (% of population) in Ecuador was reported at 98.7 % in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Ecuador - Access to electricity (% of population) - actual values, historical data, forecasts and projections were sourced from the World Bank on August of 2025.

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