The National Renewable Energy Laboratory's (NREL) Photovoltaic (PV) Rooftop Database (PVRDB) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for 128 metropolitan regions in the United States. The PVRDB data are organized by city and year of lidar collection. Five geospatial layers are available for each city and year: 1) the raster extent of the lidar collection, 2) buildings identified from the lidar data, 3) suitable developable planes for each building, 4) aspect values of the developable planes, and 5) the technical potential estimates of the developable planes.

The National Renewable Energy Laboratory's (NREL) PV Rooftop Database for Puerto Rico (PVRDB-PR) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for virtually all buildings in Puerto Rico. The dataset can be downloaded at the AWS S3 explorer page. The GitHub documentation page provides a description of the dataset with methods and assumptions. The Puerto Rico Solar-For-All dataset provides Census Tract level estimates of residential low-to-moderate income (LMI) PV rooftop technical potential as well as solar electric bill savings potential for LMI communities at the municipality level.

This dataset contains over two years of 1-minute resolution data collected from four floating solar sites, as well as data from a land-based PV system co-located with one of the floating sites. The dataset includes highly granular module temperature measurements - five modules per floating site, with three sensors per module, totaling 15 module temperature sensors per floating site. In addition to the module temperature data, meteorological data collected at the floating sites is also included, along with traditional PV system-level parameters. The data is intended for analysis of solar energy production, efficiency, and performance degradation over time.

For information about the data file usage see the "README" resource below. See "Metadata File" for information about individual files and other metadata information.

Data are taken from the Microgeneration Certification Scheme - MCS Installation Database.

For enquiries concerning this table email fitstatistics@energysecurity.gov.uk.

This data (csv file) provides simulated hourly time series of solar PV generation for the regions shown in the attached map. The best 50 % of locations (in terms of mean irradiance) within each region are simulated, with south-facing installations and tilt angles approximately representing existing installations. A generic PV module and inverter are assumed. The meteorological data are from ERA5-Land and pvlib is used for the transformation to power generation (see references below). The map shows the resulting capacity factors (annual mean). The time stamps are in GMT; the variable (column) names relate to the region names shown in the maps. The data include also country-level aggregations, e.g., UK00 is the aggregated solar PV generation of all the UK regions (weighted by regional installed capacities). The data are part of the variable renewable energy generation time series created for ENTSO-E in the 2021 update of the Pan-European Climate Database (PECD) dataset. ENTSO-E has used the data in ERAA 2021 and Winter Outlook 2021-2022 assessments, and they are used in TYNDP 2022. The simulations are carried out by DTU Wind Energy, with the future technology selection and data validation discussed and agreed with ENTSO-E and its members. This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581

The NREL PVDAQ is a large-scale time-series database containing system metadata and performance data from a variety of experimental PV sites and commercial public PV sites. The datasets are used to perform on-going performance and degradation analysis. Some of the sets can exhibit common elements that effect PV performance (e.g. soiling). The dataset consists of a series of files devoted to each of the systems and an associated set of metadata information that explains details about the system hardware and the site geo-location. Some system datasets also include environmental sensors that cover irradiance, temperatures, wind speeds, and precipitation at the site.

Open PV Project is a collaborative effort between government, industry, and the public that is compiling a comprehensive database of photovoltaic (PV) installation data for the United States. Data for the project are voluntarily contributed from a variety of sources including utilities, installers, and the general public. The data collected is actively maintained by the contributors and are always changing to provide an evolving, up-to-date snapshot of the US solar power market. Data Collection. The Open PV Project is collecting data from any willing contributor of available information. NREL has "seeded" the Open PV database by requesting data from most state run incentive programs, large utilities, and other organizations. This initial data collection has provided a solid base of data for the project to launch from and it is our hope that the database will continue to grow through contributions from the PV community and anyone interested in understanding PV market dynamics in the US. Data Quality. Determining the quality of incoming data is dependent upon who is submitting the data to the project. This means that data coming from users associated with a particular organization may be "trusted" more than data from other unknown users. Each registered user is assigned a default "score" based on their organizational affiliation. This score is highest for Government users (State, Federal, etc.) because such users are often involved with incentive programs that have a defined data collection process in place. Second are utility and PV installers (and others in the PV industry), and so on. All users who contribute data to the project have the ability to gain a "project reputation" that can impact the score of the data they contribute. Validation. Data validation occurs on each record in the database on a regular basis. The database is continually analyzed for corrupt records, bad or invalid data, and outliers such as an abnormal cost to watt ratio. Records found to contain questionable data are flagged and are dealt with on a case by case basis by a member of the Open PV Team. Duplication. Understanding duplication is one of the ways that individual records are validated. In a publicly contributed database, it is imperative to anticipate the submission of duplicate records. When duplicate records are detected, they are added to an install specific list of duplicates and the data provided are aggregated into "summary records" of their respective installs. Identifying duplicate records helps validate PV installs in the database. The more a PV installation is duplicated in the database, the more trust the project places on the data for that installation. Data Fields. Required Fields. The Open PV Project is designed to be able to store nearly any type of information pertaining to PV installations. In order to provide the primary statistics from the database we have identified 4 data fields that are required of each PV install added to the project. These four fields are: - Date Installed (Completion date or interconnection date) - Size/Capacity of the PV Installation (in kW DC) - Location (Zipcode or Street Address) - Total Installed Cost (in USD, before incentives). Additional Fields. The four required fields listed above provide the Open PV Project with the base information needed to derive several key statistics on the US PV market, including historical trends and regional comparisons. However, the design of the Open PV database is capable of storing nearly any type of data associated with PV installation, so the Open PV Team would like to encourage you to contribute any additional information you are comfortable sharing. This extra information can be extremely valuable, for example, data that contains information about who installed the PV installation can help to answer very useful questions about where certain installers are working. Information on module or inverter types can be useful in mapping efficiency and detailed financing information can be a key factor in understanding trends in overall installation cost. The Open PV Team strongly encourages you to contribute any data you feel comfortable providing, especially data you would like to see visualized in our gallery someday.

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 United States Large-Scale Solar Photovoltaic Database (USPVDB) provides the locations and array boundaries of U.S. ground-mounted photovoltaic (PV) facilities with capacity of 1 megawatt or more. It includes corresponding PV facility information, including panel type, site type, and initial year of operation. The creation of this database was jointly funded by the U.S. Department of Energy (DOE) Solar Energy Technologies Office (SETO) via the Lawrence Berkeley National Laboratory (LBNL) Energy Markets and Policy Department, and the U.S. Geological Survey (USGS) Energy Resources Program. The PV facility records are collected from the U.S. Energy Information Administration (EIA), position-verified and digitized from aerial imagery, and checked for quality. EIA facility data are supplemented with additional attributes obtained from public sources. Archived from https://energy.usgs.gov/uspvdb/

This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by Catalyst Cooperative. It is organized into Frictionless Data Packages. For additional information about this data and PUDL, see the following resources:

The PUDL Repository on GitHub

PUDL Documentation

Other Catalyst Cooperative data archives

This dataset consists of 613 sets of corresponding current-voltage trace (IV) flash test data and electroluminescence (EL) image data for commercial PV modules from the Photovoltaic Systems Evaluation Laboratory at Sandia National Laboratories. PV modules are from fielded systems in Albuquerque, New Mexico, USA. Measurements of corresponding IV and EL data were taken over a 6 year period, with modules removed from the field and measured in the laboratory at 0 to 5 years of outdoor exposure. The 438 unique modules comprise 28 unique module models from 17 different brands, which are anonymized in the metadata. Additional metadata include current-voltage and electroluminescence acquisition parameters, and length of outdoor exposure.

For more metadata information see the AnonDB.csv file, which contains metadata for each module in the dataset, and provides information on each of the IV and EL measurements. Descriptions of each column in the AnonDB.csv file are listed under the "AnonDB Descriptions" resource linked below.

This project was funded under award "PV Proving Grounds" numbers 38268 and 52787.

This database is extracted from 180 studies on PV output forecasting and is used for the research paper "What drives the accuracy of PV output forecasts?". The data of 21 key variables including the publishing year of the papers, the error values, data processing techniques used by the models, the length of the test sets, the forecast resolution, the country and region of the studies, the methodology of the forecast models, the forecast horizon, and the error metrics are included. Besides, other information such as the weather condition of the forecasts, the number of power plants, the installed capacity... is also included.

NREL has updated a database of publicly available resources related to community solar in the U.S., including journal articles, reports, fact sheets, slides, videos, datasets, webinars, and other formats. This effort aims to compile resources that would benefit all partners interested in investigating the market status, data analysis, regulation, stakeholder engagement, and the best practices of community solar development.

Analysts from the U.S. Geological Survey and Lawrence Berkeley National Laboratory collaborated to develop and release the United States Large Scale Solar Photovoltaic Database (USPVDB). This effort built from the expertise gained while developing the regularly updated United States Wind Turbine Database (USWTDB). Starting from Energy Information Administration (EIA) data, locations of large-scale solar photovoltaic (LSPV) facilities were visually verified using high-resolution aerial imagery; a polygon was drawn around the extent of facility panel arrays, and facility attributes were appended. Quality assurance and control were achieved via team peer review and comparing the USPVDB to other datasets of U.S. solar photovoltaic. Some facility information did not exist within our source data or not yet built, not built at all, or located elsewhere. Thus, uncertainty may exist for certain facilities and are rated in a confidence level. None of the data are field verified.

This dataset includes information on completed and pipeline (not yet installed) solar electric projects supported by the New York State Energy Research and Development Authority (NYSERDA). Blank cells represent data that were not required or are not currently available. Contractor data is provided for completed projects only, except for Community Distributed Generation projects. Pipeline projects are subject to change. The interactive map at https://data.ny.gov/Energy-Environment/Solar-Electric-Programs-Reported-by-NYSERDA-Beginn/3x8r-34rs provides information on solar photovoltaic (PV) installations supported by NYSERDA throughout New York State since 2000 by county, region, or statewide. Updated monthly, the graphs show the number of projects, expected production, total capacity, and annual trends.

The New York State Energy Research and Development Authority (NYSERDA) offers objective information and analysis, innovative programs, technical expertise, and support to help New Yorkers increase energy efficiency, save money, use renewable energy, and reduce reliance on fossil fuels. To learn more about NYSERDA’s programs, visit https://nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

This dataset corresponds to data which have been extracted from the 60W MSX-60 solar panel model. The model of the PV was obtained using particle swarm optimisation. The accuacy of the model is exellent and attractive as it was benchmarked by experiemal curves provided by the manufacturer. Precisely, the datasheet contain 27x399 data points corresponding to some variables such as Temperature, Irradiance, Maximum power voltage, maximum power current, open circuit voltage, short circuit current and many others. This data was generated for the design and implementation of a solar Phtotovoltaic Emulator (PVE). The authors recommend that the prescribed data offers a broad spectrum for solar panel research, such as MPPT, PV performance studies and many more.

Over 4,400 large scale commercial solar facilities are in operation in the United States as of December, 2021, representing over 60 gigawatts of electric power capacity; of these, over 3,900 are ground-mounted with capacities of 1MW or more, specified as large scale solar photovoltaic (LSPV) facilities. LSPV ground-mounted installations continue to grow, with over 400 projects coming online in 2021 alone. Currently, a comprehensive, publicly available georectified data describing the locations and spatial footprints of these facilities does not exist. Analysts from the US Geological Survey and Lawrence Berkeley National Laboratory collaborated to develop and release the United States Large Scale Solar Photovoltaic Database (USPVDB). This effort built from the expertise gained while developing the regularly updated United States Wind Turbine Database (USWTDB). Starting from Energy Information Administration (EIA) data, locations of LSPV facilities were visually verified using high-resolution aerial imagery; a polygon was drawn around the extent of facility panel arrays, and facility attributes were appended. Quality assurance and control were achieved via team peer review, and comparing the USPVDB to other datasets of US PV. The data are available in several formats, including an interactive web application, comma-separated value spreadsheet (CSV), application programming interface (API), and a shapefile. The data are available for use by academic researchers, engineers and developers from PV companies, government agencies, planners, educators, and the general public.

Solar PV Installations for Systems 1 MW and Smaller: 2023. Energy data and map are from the California Energy Commission CEC-1304B. Map depicts small solar photovoltaic capacity (with nameplate capacity of 1,000 kW or less). Projection: NAD 1983 (2011) California (Teale) Albers (Meters). For more information, contact John Hingtgen at (916) 510-9747 or john.Hingtgen@energy.ca.gov

The global solar PV data logger market is experiencing robust growth, driven by the increasing adoption of solar photovoltaic (PV) systems worldwide. The market, currently valued at approximately $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant expansion is fueled by several key factors. Firstly, the ongoing global transition towards renewable energy sources necessitates efficient monitoring and management of solar PV installations. Data loggers play a crucial role in optimizing system performance, detecting faults, and maximizing energy generation, thus becoming an indispensable component of modern solar power plants. Secondly, advancements in data logger technology, including enhanced data analytics capabilities, cloud connectivity, and improved sensor integration, are further driving market growth. The increasing demand for smart grids and the integration of renewable energy sources into existing grids also contribute significantly. Finally, government incentives and supportive policies aimed at promoting solar energy adoption across various regions are fueling market expansion. The market is segmented by system size (over 100kWp and below 100kWp) and application (solar resource monitoring and solar resource assessment). The over 100kWp segment currently holds a larger market share due to the prevalence of large-scale solar power projects. However, the below 100kWp segment is expected to experience faster growth driven by the increasing adoption of rooftop solar systems in residential and commercial sectors. The market's growth is not uniform across geographical regions. North America and Europe are currently leading the market, driven by established solar energy infrastructure and supportive regulatory environments. However, rapid economic growth and government initiatives in Asia Pacific, particularly in China and India, are expected to fuel substantial market expansion in these regions during the forecast period. While the market faces challenges like high initial investment costs and the potential for data security concerns, these are being mitigated by technological advancements and the increasing availability of cost-effective solutions. Key players like SMA Solar, Huawei, and Fronius are actively investing in research and development, enhancing their product offerings, and expanding their global reach to maintain a competitive edge. This competitive landscape, coupled with continued technological innovations and robust government support, indicates a bright outlook for the solar PV data logger market in the coming years.

The tabular dataset contains measured soiling levels and associated operational and meteorological data for the parabolic trough collectors at the Andasol-3 power plant in southern Spain. The soiling values are measured with a gloss meter at predefined positions in the solar field. The dataset includes three years of data from 2015 to 2017.

https://zenodo.org/records/7061913

(1) Output of the Renewable Energy Model (REM) as described in Insights into weather-driven extremes in Europe’s resources for renewable energy (Ho and Fiedler, 2024), last modification on 30.10.2023 from Linh Ho, named year_PV_wind_generation_v2.nc, with 23 years from 1995 to 2017. REM includes one simulation of photovoltaic (PV) power production and one simulation of wind power production across European domain, with a horizontal resolution of 48 km, hourly output for the period 1995--2017.

The output has a European domain with the same size as in the reanalysis dataset COSMO-REA6. This is a rotated grid with the coordinates of the rotated North Pole −162.0, 39.25, and of the lower left corner −23.375, −28.375. See Bollmeyer et al. (2014, http://doi.org/10.1002/qj.2486). Data downloaded from https://opendata.dwd.de/climate_environment/REA/COSMO_REA6/

(2) Weather pattern classification daily for Europe from 1995 to April 2020, named EGWL_LegacyGWL.txt, from James (2007, http://doi.org/10.1007/s00704-006-0239-3)

(3) The installation data of PV and wind power in Europe for one scenario in 2050 from the CLIMIX model, processed to have the same horizontal resolution as in REM, named installed_capacity_PV_wind_power_from_CLIMIX_final.nc. Original data were provided at 0.11 degree resolution, acquired from personal communication with the author from Jerez et al. (2015, http://doi.org/10.1016/j.rser.2014.09.041)

(4) Python scripts of REM, including: - model_PV_wind_complete_v2.py: the main script to produce REM output - model_PV_wind_potential_v2.py: produce potential (capacity factor) of PV and wind power for model evaluations, e.g., against CDS and Renewables Ninja data, as descript in Ho and Fiedler (2024) - model_PV_wind_complete_v1_ONLYyear2000.py: a separate Python script to produce REM output only for the year 2000. Note that the data for 2000 from COSMO-REA6 were read in a different approach (using cfgrib) probably due to the time stamp changes at the beginning of the milenium, also explains the larger size of the final output - utils_LH_archive_Oct2022.py: contains necessary Python functions to run the other scripts

(5) Jupyter notebook files to reproduce the figures in Ho and Fiedler (2024), named Paper1_Fig*_**.ipynb

(6) Time series of European-aggregated PV and wind power production hourly during the period 1995--2017, processed data from the dataset (1) to facilitate the reproduction of the figures, including two installations scale-2019 and scenario-2050: - Timeseries_all_hourly_1995_2017_GW_scale2019.csv - Timeseries_all_hourly_1995_2017_GW_scen2050.csv