In 2023, net solar power generation in the United States reached its highest point yet at 164.5 terawatt hours of solar thermal and photovoltaic (PV) power. Solar power generation has increased drastically over the past two decades, especially since 2011, when it hovered just below two terawatt hours.

The U.S. solar industry

In the United States, an exceptionally high number of solar-related jobs are based in California. With a boost from state legislation, California has long been a forerunner in solar technology. In the second quarter of 2022, it had a cumulative solar PV capacity of more than 37 gigawatts. Outside of California, Texas, Florida, and North Carolina were the states with the largest solar PV capacity.

Clean energy in the U.S.
In recent years, solar power generation has seen more rapid growth than wind power in the United States. However, among renewables used for electricity, wind has been a more common and substantial source for the past decade. Wind power surpassed conventional hydropower as the largest source of renewable electricity in 2019. While there are major environmental costs often associated with the construction and operation of large hydropower facilities, hydro remains a vital source of electricity generation for the United States.

In this dataset the anther's analysis is based on data from NREL about Solar & Wind energy generation by operation areas.

NASA Prediction of Worldwide Energy Resources

Solar

Monthly averages for global horizontal radiation over 22-year period (Jul 1983

• Jun 2013)

Wind

Monthly average wind speed at 50m above the surface of earth over a 30-year period (Jan 1984 - Dec 2013)Year: Averaged Over 10 to 15 years

COA = central operating area.

EOA = eastern operating area.

SOA = southern operating area.

WOA = western operating area. Source: NRELSource Link

Solar energy accounted for roughly 5.5 percent of electricity generation worldwide in 2023, up from a 4.6 percent share a year earlier. That year, wind and solar generated nearly 12 percent of global electricity.

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.

This table expresses the use of renewable energy as gross final consumption of energy. Figures are presented in an absolute way, as well as related to the total energy use in the Netherlands. The total gross final energy consumption in the Netherlands (the denominator used to calculate the percentage of renewable energy per ‘Energy sources and techniques’) can be found in the table as ‘Total, including non-renewables’ and Energy application ‘Total’. The gross final energy consumption for the energy applications ‘Electricity’ and ‘Heat’ are also available. With these figures the percentages of the different energy sources and applications can be calculated; these values are not available in this table. The gross final energy consumption for ‘Transport’ is not available because of the complexity to calculate this. More information on this can be found in the yearly publication ‘Hernieuwbare energie in Nederland’.

Renewable energy is energy from wind, hydro power, the sun, the earth, heat from outdoor air and biomass. This is energy from natural processes that is replenished constantly.

The figures are broken down into energy source/technique and into energy application (electricity, heat and transport).

This table focuses on the share of renewable energy according to the EU Renewable Energy Directive. Under this directive, countries can apply an administrative transfer by purchasing renewable energy from countries that have consumed more renewable energy than the agreed target. For 2020, the Netherlands has implemented such a transfer by purchasing renewable energy from Denmark. This transfer has been made visible in this table as a separate energy source/technique and two totals are included; a total with statistical transfer and a total without statistical transfer.

Figures for 2020 and before were calculated based on RED I; in accordance with Eurostat these figures will not be modified anymore. Inconsistencies with other tables undergoing updates may occur.

Data available from: 1990

Status of the figures: This table contains definite figures up to and including 2022 and figures of 2023 are revised provisional figures.

Changes as of January 2025 Renewable cooling has been added as Energy source and technique from 2021 onwards, in accordance with RED II. Figures for 2020 and earlier follow RED I definitions, renewable cooling isn’t a part of these definitions.
The energy application “Heat” has been renamed to “Heating and cooling”, in accordance with RED II definitions. RED II is the current Renewable Energy Directive which entered into force in 2021

Changes as of November 15th 2024 Figures for 2021-2023 have been adjusted. 2022 is now definitive, 2023 stays revised provisional. Because of new insights for windmills regarding own electricity use and capacity, figures on 2021 have been revised.

Changes as of March 2024: Figures of the total energy applications of biogas, co-digestion of manure and other biogas have been restored for 2021 and 2022. The final energy consumption of non-compliant biogas (according to RED II) was wrongly included in the total final consumption of these types of biogas. Figures of total biogas, total biomass and total renewable energy were not influenced by this and therefore not adjusted.

When will new figures be published? Provisional figures on the gross final consumption of renewable energy in broad outlines for the previous year are published each year in June. Revised provisional figures for the previous year appear each year in June.

In November all figures on the consumption of renewable energy in the previous year will be published. These figures remain revised provisional, definite figures appear in November two years after the reporting year. Most important (expected) changes between revised provisional figures in November and definite figures a year later are the figures on solar photovoltaic energy. The figures on the share of total energy consumption in the Netherlands could also still be changed by the availability of adjusted figures on total energy consumption.

The solar energy capacity in Malaysia was approximately 1,933 megawatts in 2023, the same amount as the previous year. The capacity for solar energy in the country has been increasing over the last decade, from 205 megawatts in 2014.

The Report Covers On-Site Photovoltaic Solar Power for Data Centers Market Size & Share and It is Segmented by Geography (North America, Europe, Asia-pacific, South America, And Middle-East and Africa). The Report Offers the Market Size and Forecasts for On-Site Photovoltaic Solar Power for Data Centers in Revenue (USD) for all the Above Segments.

Solar Footprints in CaliforniaThis 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, 2023Solar 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 FootprintsAbstract: 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;=advancedThe Solar Energy Industries Association’s “Project Location Map” which can be found here: https://www.seia.org/map/majorprojectsmap.phpalso 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.cfmThere 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 were located by sleuthing around for proposals and company websites that had images of the completed facility. These helped to locate the most recently developed sites and these sites were digitized based on landmarks such as ditches, trees, roads and other permanent structures.Metadata: (2) UC Berkeley Solar PointsUC Berkeley report containing point location for energy facilities across the United States.2022_utility-scale_solar_data_update.xlsm (live.com)Metadata: (3) Kruitwagen et al. 2021Abstract: Photovoltaic (PV) solar energy generating capacity has grown by 41 per cent per year since 2009. Energy system projections that mitigate climate change and aid universal energy access show a nearly ten-fold increase in PV solar energy generating capacity by 2040. Geospatial data describing the energy system are required to manage generation intermittency, mitigate climate change risks, and identify trade-offs with biodiversity, conservation and land protection priorities caused by the land-use and land-cover change necessary for PV deployment. Currently available inventories of solar generating capacity cannot fully address these needs. Here we provide a global inventory of commercial-, industrial- and utility-scale PV installations (that is, PV generating stations in excess of 10 kilowatts nameplate capacity) by using a longitudinal corpus of remote sensing imagery, machine learning and a large cloud computation infrastructure. We locate and verify 68,661 facilities, an increase of 432 per cent (in number of facilities) on previously available asset-level data. With the help of a hand-labelled test set, we estimate global installed generating capacity to be 423 gigawatts (−75/+77 gigawatts) at the end of 2018. Enrichment of our dataset with estimates of facility installation date, historic land-cover classification and proximity to vulnerable areas allows us to show that most of the PV solar energy facilities are sited on cropland, followed by arid lands and grassland. Our inventory could aid PV delivery aligned with the Sustainable Development GoalsEnergy Resource Land Use Planning - Kruitwagen_etal_Nature.pdf - All Documents (sharepoint.com)Metadata: (4) BLM Renewable ProjectTo identify renewable energy approved and pending lease areas on BLM administered lands. To provide information about solar and wind energy applications and completed projects within the State of California for analysis and display internally and externally. This feature class denotes "verified" renewable energy projects at the California State BLM Office, displayed in GIS. The term "Verified" refers to the GIS data being constructed at the California State Office, using the actual application/maps with legal descriptions obtained from the renewable energy company. https://www.blm.gov/wo/st/en/prog/energy/renewable_energy https://www.blm.gov/style/medialib/blm/wo/MINERALS_REALTY_AND_RESOURCE_PROTECTION_/energy/solar_and_wind.Par.70101.File.dat/Public%20Webinar%20Dec%203%202014%20-%20Solar%20and%20Wind%20Regulations.pdfBLM CA Renewable Energy Projects | BLM GBP Hub (arcgis.com)Metadata: (5) Quarterly Fuel and Energy Report (QFER) California Power Plants - Overview (arcgis.com)

The UK's energy use from renewable and waste sources, by source (for example, hydroelectric power, wind, wave, solar, and so on) and industry (SIC 2007 section - 21 categories), 1990 to 2022.

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 NSRDB update is a collection of hourly values of the three most common measurements of solar radiation (i.e., global horizontal, direct normal, and diffuse horizontal) over a period of time adequate to establish means and extremes and at a sufficient number or locations to represent regional solar radiation climates. Nearly all of the solar data in the NSRDB are modeled, and only 40 sites have measured solar data - none of them with a complete period of record. Because of the data-filling methods used to accomplish the goal of serial completeness, NSRDB meteorological data are not suitable for climatological work. The meteorological fields in the NSRDB should be used only as ancillary data for solar deployment and sizing applications. Filled/interpolated meteorological data should not be used for climatic applications. (All such data are flagged.) 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 1991-2010 NSRDB is an update of the 1991-2005 NSRDB released in 2006 and archived at NCDC. The updated NSRDB dataset an hourly ground-based data set of solar and meteorological fields for 1454 stations. The primary provider for ground-based data is NCDC, which are stored as site-year files in comma-separated value (CSV) American Standard Code for Information Interchange (ASCII) format. Station identification numbers use the six-digit United States Air Force (USAF) station ID numbering scheme. The measured solar radiation data came from multiple sources, including: Atmospheric Radiation Measurement Program, Department of Energy Florida Solar Energy Center, State of Florida Integrated Surface Irradiance Study and Surface Radiation Budget Measurement Networks, National Oceanic and Atmospheric Administration Air Resources Laboratory and Earth System Research Laboratory Global Monitoring Division Measurement and Instrumentation Data Center, National Renewable Energy Laboratory University of Oregon Solar Radiation Monitoring Laboratory Network University of Texas Solar Energy Laboratory. All meteorological data were provided by the National Climatic Data Center from its Integrated Surface Hourly Database (ISD) product. The NSRDB Statistics Files hold summary statistics for all Class I and Class II stations. The Daily Statistics provide monthly and annual averages of solar radiation and several meteorological parameters for both annual and a 20 year roll-up. The Hourly Statistics provide average diurnal profiles by hour for each station year for each solar parameter. The Persistence Statistics provide multiple levels of persistence for up to 30 days for each station for each solar parameter. These Summary Statistics files are documented in the NSRDB User's Manual.

The Global Solar Power Tracker is a worldwide dataset of utility-scale solar PV facilities. It includes solar farm phases with capacities of 20 megawatts (MW) or more (10 MW or more in Arabic-speaking countries). A solar project phase is generally defined as a group of one or more solar units that are installed under one permit, one power purchase agreement, and typically come online at the same time. The Global Solar Power Tracker catalogs every solar farm phase at these capacity thresholds of any status, including operating, announced, under development, under construction, shelved, cancelled, mothballed, or retired. Each solar farm included in the tracker is linked to a wiki page on the GEM wiki.

Architecture

Global Energy Monitor’s Global Solar Power Tracker uses a two-level system for organizing information, consisting of both a database and wiki pages with further information. The database tracks individual solar farm phases and includes information such as project owner, status, and location. A wiki page for each solar farm is created within the Global Energy Monitor wiki. The database and wiki pages are updated annually.

Status Categories

• Announced: Proposed projects that have been described in corporate or government plans but have not yet taken concrete steps such as applying for permits.
• Development: Projects that are actively moving forward in seeking governmental approvals, land rights, or financing.
• Construction: Site preparation and equipment installation are underway.
• Operating: The project has been formally commissioned; commercial operation has begun.
• Shelved: Suspension of operation has been announced, or no progress has been observed for at least two years.
• Cancelled: A cancellation announcement has been made, or no progress has been observed for at least four years.
• Retired: The project has been decommissioned.
• Mothballed: The project is disused, but not dismantled.

Research Process

The Global Solar Power Tracker data set draws on various public data sources, including: - Government data on individual power solar farms (such as India Central Electricity Authority’s “Plant Wise Details of All India Renewable Energy Projects” and the U.S. EIA 860 Electric Generator Inventory), country energy and resource plans, and government websites tracking solar farm permits and applications; - Reports by power companies (both state-owned and private); - News and media reports; - Local non-governmental organizations tracking solar farms or permits.

Wiki Pages

For each solar farm, a wiki page is created on Global Energy Monitor’s wiki. Under standard wiki convention, all information is linked to a publicly-accessible published reference, such as a news article, company or government report, or a regulatory permit. In order to ensure data integrity in the open-access wiki environment, Global Energy Monitor researchers review all edits of project wiki pages.

Mapping

To allow easy public access to the results, Global Energy Monitor worked with GreenInfo Network to develop a map-based and table-based interface using the Leaflet Open-Source JavaScript library. In the case of exact coordinates, locations have been visually determined using Google Maps, Google Earth, Wikimapia, or OpenStreetMap. For proposed projects, exact locations, if available, are from permit applications, or company or government documentation. If the location of a solar farm or proposal is not known, Global Energy Monitor identifies the most accurate location possible based on available information.

The rapid proliferation of solar energy adoption presents a substantial environmental challenge concerning end-of-life solar panels. A primary issue involves the presence of hazardous materials like cadmium and lead in these panels [2]. Proper handling and disposal of these toxic materials are imperative to prevent environmental contamination and health risks. Furthermore, inadequate recycling infrastructure in various regions results in a significant portion of panels ending up in landfills, leading to the wastage of valuable materials and environmental harm. To surmount these challenges, the implementation of recycling technologies is crucial. This includes designing panels for easy material recovery and establishing comprehensive regulations and policies that incentivize recycling practices and ensure responsible disposal. Embracing a circular economy approach proves beneficial in mitigating the escalating concerns surrounding solar panel waste. This strategy embodies a sustainable and environmentally friendly methodology for the entire life cycle of solar panels. When solar panels reach the end of their operational life, adopting this approach involves their collection and disassembly to retrieve valuable materials like silicon, glass, and metals. These reclaimed materials can then be utilized by manufacturers to produce new panels, thereby diminishing reliance on virgin resources and mitigating the environmental impact associated with mining and production. Figure AA illustrates the schematic diagram of the circular economy approach for managing end-of-life solar panels. By fostering efficient collection and recycling systems, this approach ensures that aging panels are not discarded as waste but rather transformed into valuable sources for new solar panel production. This reduction in the demand for virgin resources contributes to addressing the environmental challenges posed by the industry. This dataset is not publicly accessible because: example it's not owned by the EPA. It can be accessed through the following means: see journal article. Format: PI-update. example data is owned and managed by partners. If website to data source is available change option to data is publicly available and provide link. This dataset is associated with the following publication: Sahle-Demessie, E., and B. Mezgebe. Renewable Energy Waste Management: Solar Panel Circular Economy. EM: AIR AND WASTE MANAGEMENT ASSOCIATION'S MAGAZINE FOR ENVIRONMENTAL MANAGERS. Air & Waste Management Association, Pittsburgh, PA, USA, 08-12, (2024).

In December 2024, the power generation from solar energy in China amounted to almost 31 terawatt-hours. Over the last three years, the monthly solar power production had increased substantially every year. Nationwide, the government invested in the development of solar farms to increase the countries energy independence. The result was that in some cities, the price of solar electricity was similar to electricity generated from coal.

We present the results of a major crowd-sourcing campaign to create open geographic data for over 260,000 solar PV installations across the UK, covering the vast majority of the capacity in the country. We focus in particular on capturing small-scale domestic solar PV, which accounts for a significant fraction of generation but was until now very poorly documented.

The data we introduce will enable decarbonisation at national scales, through forecasting and management of generation, and also serves as a training dataset for machine vision detection of new PV.

For a complete description please see the research paper describing the dataset. Please cite this paper in any academic use of the data.

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.

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

Monthly and annual data on renewable energy, i.e., biomass, geothermal, hydropower, solar, and wind. Also data on alternative transportation fuels, i.e., hydrogen, natural gas, propane, ethanol, and electricity. Data on renewable energy production, consumption, electricity generation, and consumption by end-use sector.

Context

This dataset accompanies the paper "Machine Learning Modeling of Horizontal Photovoltaics Using Weather and Location Data" submitted to the Journal of Renewable Energy. This file contains power output from horizontal photovoltaic panels located at 12 Northern hemisphere sites over 14 months. Independent variables in each column include: location, date, time sampled, latitude, longitude, altitude, year and month, month, hour, season, humidity, ambient temperature, power output from the solar panel, wind speed, visibility, pressure, and cloud ceiling.

Acknowledgements

Williams, Jada; Wagner, Torrey (2019), “Northern Hemisphere Horizontal Photovoltaic Power Output Data for 12 Sites”, Mendeley Data, V5, doi: 10.17632/hfhwmn8w24.5

The Procurement Analysis Tool (PAT) was developed at NREL to help organizations explore renewable energy options that align with their goals. Users input facility data and answer goal-oriented questions. PAT analyzes this information to identify potential wind, solar, or storage resources and suitable procurement options (PPA, Green Tariffs) that align with their budget, location, and sustainability goals. For more information see the "Procurement Analysis Tool" resource below.

The Renewable Electricity Procurement Options Data (RE-POD) was an aggregated dataset meant to help local jurisdictions and utility customers within those jurisdictions understand the options that may be available to them to procure renewable electricity or renewable energy credits to meet energy goals. RE-POD has been discontinued and replaced with the PAT.

This data is part of a suite of state and local energy profile data available at the "State and Local Energy Profile Data Suite" link below and builds on Cities-LEAP energy modeling, available at the "EERE Cities-LEAP Page" link below. Examples of how to use the data to inform energy planning can be found at the "Example Uses" link below.