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.

(Link to Metadata) The Renewable Energy Atlas of Vermont and this dataset were created to assist town energy committees, the Clean Energy Development Fund and other funders, educators, planners, policy-makers, and businesses in making informed decisions about the planning and implementation of renewable energy in their communities - decisions that ultimately lead to successful projects, greater energy security, a cleaner and healthier environment, and a better quality of life across the state. Energy flows through nature into social systems as life support. Human societies depended on renewable, solar powered energy for fuel, shelter, tools, and other items for most of our history. Today, when we flip on a light switch, turn an ignition or a water faucet, or eat a hamburger, we engage complex energy extraction systems that largely rely on non-renewable energy to power our lives. About 90% of Vermont's total energy consumption is currently generated from non-renewable energy sources. This dependency puts Vermont at considerable risk, as the peaking of world oil production, global financial instability, climate change, and other factors impact the state.

Berkeley Lab's Tracking the Sun report series is dedicated to summarizing installed prices and other trends among grid-connected, distributed solar photovoltaic (PV) systems in the United States. The present report, the 11th edition in the series, focuses on systems installed through year-end 2017, with preliminary trends for the first half of 2018. As in years past, the primary emphasis is on describing changes in installed prices over time and variation in pricing across projects based on location, project ownership, system design, and other attributes. New to this year, however, is an expanded discussion of other project characteristics in the large underlying data sample. Future editions will include more of such material, beyond the reports traditional focus on installed pricing. The trends described in this report derive primarily from project-level data reported to state agencies and utilities that administer PV incentive programs, solar renewable energy credit (SREC) registration systems, or interconnection processes. In total, data were collected and cleaned for more than 1.3 million individual PV systems, representing 81% of U.S. residential and non-residential PV systems installed through 2017. The analysis of installed pricing trends is based on a subset of roughly 770,000 systems with available installed price data.

IntroductionThis dataset provides the locations of areas that restrict the suitability of land for solar PV developments; such as roads, residential areas, world heritage sites, agricultural land, and flood zones. In addition, it calculates the potential energy density of over 3.5 million individual areas for both solar and wind installations using windspeed and solar irradiance data.This results in an overall assessment of the suitability of a specific area for installing solar and wind generation. The assessment grades land as either YES – suitable, No – not suitable, or MAYBE – could be suitable.

This dataset was developed for UK Power Networks licence area by UEA Consulting Limited. It is based on public domain sources and previous analysis that was carried out as part of the IRENES EU Interreg project which examined renewable energy sources in eastern England.Methodological ApproachTo view the methodology behind this data please click here.This dataset displays the solar PV data, click here for the wind data.

Quality Control Statement This dataset is provided "as is".

Assurance Statement The Open Data Team and Local Net Zero Team worked together to ensure data accuracy and consistency.

Other

Download dataset information: Metadata (JSON)

Definitions of key terms related to this dataset can be found in the Open Data Portal Glossary: https://ukpowernetworks.opendatasoft.com/pages/glossary/

UK PV dataset

Domestic solar photovoltaic (PV) power generation data from Great Britain. This dataset contains data from over 30,000 solar PV systems. The dataset spans 2010 to 2025. The nominal generation capacity per PV system ranges from 0.47 kilowatts to 250 kilowatts. The dataset is updated with new data every few months. All PV systems in this dataset report cumulative energy generation every 30 minutes. This data represents a true accumulation of the total energy generated… See the full description on the dataset page: https://huggingface.co/datasets/openclimatefix/uk_pv.

This web mapping application gives estimates of the electricity that can be generated by grid-connected photovoltaic systems without batteries (in kWh/kWp) and of the mean daily global insolation (in MJ/m2 and in kWh/m2) for any location in Canada on a 60 arc seconds ~2 km grid. They are presented for each month and for the entire year, for six different PV array orientations: a sun-tracking orientation and five fixed South-facing orientations with latitude, vertical (90°), horizontal (0°) and latitude ± 15° tilts. Data can also be obtained directly for individual municipalities from a list of over 3500 municipalities or downloaded for all municipalities at once. These maps and datasets were developed by the Canadian Forest Service (Great Lakes Forestry Centre) in collaboration with the CanmetENERGY Photovoltaic systems group and the Federal Geospatial Platform. Insolation data were provided by Environment and Climate Change Canada. Web map application developed by Federal Geospatial Platform, 2020. References: Pelland S., McKenney D. W., Poissant Y., Morris R., Lawrence K., Campbell K. and Papadopol P., 2006. The Development of Photovoltaic Resource Maps for Canada, In Proceedings of the Annual Conference of the Solar Energy Society of Canada (SESCI) 2006. McKenney D. W., Pelland S., Poissant Y., Morris R., Hutchinson M, Papadopol P., Lawrence K. and Campbell K., 2008. Spatial insolation models for photovoltaic energy in Canada, Solar Energy 82, pp. 1049–1061.

Solar Footprints in California

This GIS dataset consists of polygons that represent the footprints of solar powered electric generation facilities and related infrastructure in California called Solar Footprints. The location of solar footprints was identified using other existing solar footprint datasets from various sources along with imagery interpretation. CEC staff reviewed footprints identified with imagery and digitized polygons to match the visual extent of each facility. Previous datasets of existing solar footprints used to locate solar facilities include:

GIS Layers: (1) California Solar Footprints, (2) UC Berkeley Solar Points, (3) Kruitwagen et al. 2021, (4) BLM Renewable Project Facilities, (5) Quarterly Fuel and Energy Report (QFER)

Imagery Datasets: Esri World Imagery, USGS National Agriculture Imagery Program (NAIP), 2020 SENTINEL 2 Satellite Imagery, 2023

Solar facilities with large footprints such as parking lot solar, large rooftop solar, and ground solar were included in the solar footprint dataset. Small scale solar (approximately less than 0.5 acre) and residential footprints were not included. No other data was used in the production of these shapes. Definitions for the solar facilities identified via imagery are subjective and described as follows:

Rooftop Solar: Solar arrays located on rooftops of large buildings.

Parking lot Solar: Solar panels on parking lots roughly larger than 1 acre, or clusters of solar panels in adjacent parking lots.

Ground Solar: Solar panels located on ground roughly larger than 1 acre, or large clusters of smaller scale footprints.

Once all footprints identified by the above criteria were digitized for all California counties, the features were visually classified into ground, parking and rooftop categories. The features were also classified into rural and urban types using the 42 U.S. Code § 1490 definition for rural. In addition, the distance to the closest substation and the percentile category of this distance (e.g. 0-25th percentile, 25th-50th percentile) was also calculated. The coverage provided by this data set should not be assumed to be a complete accounting of solar footprints in California. Rather, this dataset represents an attempt to improve upon existing solar feature datasets and to update the inventory of "large" solar footprints via imagery, especially in recent years since previous datasets were published.

This procedure produced a total solar project footprint of 150,250 acres. Attempts to classify these footprints and isolate the large utility-scale projects from the smaller rooftop solar projects identified in the data set is difficult. The data was gathered based on imagery, and project information that could link multiple adjacent solar footprints under one larger project is not known. However, partitioning all solar footprints that are at least partly outside of the techno-economic exclusions and greater than 7 acres yields a total footprint size of 133,493 acres. These can be approximated as utility-scale footprints.

Metadata: (1) CBI Solar Footprints

Abstract: Conservation Biology Institute (CBI) created this dataset of solar footprints in California after it was found that no such dataset was publicly available at the time (Dec 2015-Jan 2016). This dataset is used to help identify where current ground based, mostly utility scale, solar facilities are being constructed and will be used in a larger landscape intactness model to help guide future development of renewable energy projects. The process of digitizing these footprints first began by utilizing an excel file from the California Energy Commission with lat/long coordinates of some of the older and bigger locations. After projecting those points and locating the facilities utilizing NAIP 2014 imagery, the developed area around each facility was digitized. While interpreting imagery, there were some instances where a fenced perimeter was clearly seen and was slightly larger than the actual footprint. For those cases the footprint followed the fenced perimeter since it limits wildlife movement through the area. In other instances, it was clear that the top soil had been scraped of any vegetation, even outside of the primary facility footprint. These footprints included the areas that were scraped within the fencing since, especially in desert systems, it has been near permanently altered. Other sources that guided the search for solar facilities included the Energy Justice Map, developed by the Energy Justice Network which can be found here:

https://www.energyjustice.net/map/searchobject.php?gsMapsize=large&giCurrentpageiFacilityid;=1&gsTable;=facility&gsSearchtype;=advanced

The Solar Energy Industries Association’s “Project Location Map” which can be found here:

https://www.seia.org/map/majorprojectsmap.php

also assisted in locating newer facilities along with the "Power Plants" shapefile, updated in December 16th, 2015, downloaded from the U.S. Energy Information Administration located here:

https://www.eia.gov/maps/layer_info-m.cfm

There were some facilities that were stumbled upon while searching for others, most of these are smaller scale sites located near farm infrastructure. Other sites were located by contacting counties that had solar developments within the county. Still, others

Energy systems need decarbonisation in order to limit global warming to within safe limits. While global land planners are promising more of the planet’s limited space to wind and solar photovoltaic, there is little information on where current infrastructure is located. The majority of recent studies use land suitability for wind and solar, coupled with technical and socioeconomic constraints, as a proxy for actual location data. Here, we address this shortcoming. Using readily accessible OpenStreetMap data we present, to our knowledge, the first global, open-access, harmonised spatial datasets of wind and solar installations. We also include user friendly code for enabling users to easily create newer versions of the dataset. Finally, we include first order estimates of power capacities of installations. We anticipate this data will be of widespread interest within global studies of the future potential and trade-offs associated with the global decarbonisation of energy systems.

This dataset identifies locations of known solar installations. It includes both photovoltaic (PV) and solar hot water installations. The location may not fall in the exact placement of the solar equipment, especially on larger parcels, or parcels with equipment in multiple locations. This dataset only includes active installations. This dataset will be updated quarterly with new installations and to remove records for installations that have been retired.

Open PV Project is a collaborative effort between government, industry, and public that is compiling a comprehensive database of photovoltaic (PV) installation data for the U.S. Data for the project are voluntarily contributed from a variety of sources including utilities, installers, and general public.

Open PV provides a web-based resource for users to add their own PV installation data, browse PV data entered by others, and view statistics from the data that show current and recent trends of the PV market.

Data collected is maintained by contributors and are always changing to provide an evolving, up-to-date snapshot of the U.S. solar power market.

Statistics of the number of mosques where the solar energy system is installed

The National Solar Radiation Database (NSRDB) is a serially complete collection of meteorological and solar irradiance data sets for the United States and a growing list of international locations for 1998-2023. The NSRDB is updated annually and provides foundational information to support U.S. Department of Energy programs, research, industry and the general public. The NSRDB provides time-series data at 30-minute resolution of resource averaged over surface cells of 0.038 degrees in both latitude and longitude, or nominally 4 km in size. Additionally time series data at 5 minutes for the US and 10 minutes for North, Central and South America at 2 km resolution are produced from the next generation of GOES satellites and made available from 2019. The solar radiation values represent the resource available to solar energy systems. The data was created using cloud properties which are generated using the AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) algorithms developed by the University of Wisconsin. Fast all-sky radiation model for solar applications (FARMS) in conjunction with the cloud properties, and aerosol optical depth (AOD) and precipitable water vapor (PWV) from ancillary source are used to estimate solar irradiance (GHI, DNI, and DHI). The Global Horizontal Irradiance (GHI) is computed for clear skies using the REST2 model. For cloud scenes identified by the cloud mask, FARMS is used to compute GHI and FARMS DNI is used to compute the Direct Normal Irradiance (DNI). The PATMOS-X model uses radiance images in visible and infrared channels from the Geostationary Operational Environmental Satellite (GOES) series of geostationary weather satellites. Ancillary variables needed to run REST2 and FARMS (e.g., aerosol optical depth, precipitable water vapor, and albedo) are derived from NASA's Modern Era-Retrospective Analysis (MERRA-2) dataset. Temperature and wind speed data are also derived from MERRA-2 and provided for use in NREL's System Advisor Model (SAM) to compute PV generation.

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.

This dataset was used to support machine learning model development for wildfire impacts on utility-scale solar in the United States. The dataset includes information regarding energy generation, PM2.5, clearness index, temperature, wind speed, precipitation, and site size.

This dataset consists of grid connected solar installations. Their installed capacities in kilowatts (kW) are also provided. This dataset was last updated in June 2014. Citation: Ghana Energy Commission & Negawatt challenge. A curated list of datasets for the World Bank Negawatt Challenge competition in Accra and Nairobi cities: https://datahub.io/organization/negawatt-challenge

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.

Charlottesville is well located for onsite solar power energy generation. This dataset shows an estimate of solar energy generation potential for rooftop solar PV systems in Charlottesville, VA. This data was created using LiDAR data, the Area Solar Radiation tool available with the ArcGIS Spatial Analyst extension, and local market estimations for cost and PV panel production potentials.

For more information check out our website, as well as this ESRI page describing the process.

This dataset is based on solar interconnection data drawn from the publicly posted inventories of New York State’s electric utilities. This dataset represents the most comprehensive source of installed distributed solar projects, including projects that did not receive State funding, for all of New York State since 2000. This dataset does not include utility-scale projects that participate in the NYISO wholesale market. The interactive map at https://www.nyserda.ny.gov/All-Programs/Programs/NY-Sun/Solar-Data-Maps/Statewide-Projects provides information on Statewide Distributed Solar Projects since 2000 by county. 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 nyserda.ny.gov or follow us on X, Facebook, YouTube, or Instagram.

Database of solar small-area images disturbed by turbulent atmosphere. Together with short series of images, an MFBD-assisted recovered image is provided. Paper describing the dataset as well as utilized instrumentation can be found in the original work at https://www.mdpi.com/1424-8220/22/20/7902

Released to the public as part of the Department of Energy's Open Energy Data Initiative, the National Solar Radiation Database (NSRDB) is a serially complete collection of hourly and half-hourly values of the three most common measurements of solar radiation – global horizontal, direct normal, and diffuse horizontal irradiance — and meteorological data. These data have been collected at a sufficient number of locations and temporal and spatial scales to accurately represent regional solar radiation climates.