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

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

In 2024, net solar power generation in the United States reached its highest point yet at 218.5 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 2024, it had a cumulative solar PV capacity of more than 48 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.

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

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

In 2024, the solar energy capacity in Asia amounted to approximately ***** gigawatts. This was a significant increase from 2023, when the solar energy capacity in Asia was about *** gigawatts.

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 2023.

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, figures for 2023 are revised provisional figures and figures for 2024 are provisional.

Changes as of june 2025: Figures for 2024 have been added.

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 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.

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.

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 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.

This dataset contains measured timeseries of renewable energy production and electricity consumption as well as exchange with neighboring countries/continents on hourly time resolution. The timeseries data has been divided into two xml files, one for each of the Danish price regions; DK1 (Western Denmark) and DK2 (Eastern Denmark). The data comes from the Danish TSO Energinet and was used in a flexibility study by Karen Olsen in 2018-19 leading to a paper that is to appear in the proceedings of the ICAE19 conference and is entitled: "Data-driven flexibility requirements for current and future scenarios with high penetration of renewables". A journal paper has also been submitted using the same data.The data has been extracted from a website run by Energinet at the following link where time series data is publicly available:https://www.energidataservice.dk/dataset/electricitybalanceThe present version was extracted in September 2019 and contains installation and production data from 2011 until and including the beginning of September 2019.The data is in the originally downloaded xml files, ready to be parsed by the python code written by Karen Olsen (see reference for Fanfare code).Data used for analysis:- offshore wind power generated (column: "Offshore Wind Power" in the xml file)- onshore wind power generated (column: "Onshore Wind Power" in the xml file)- solar power generated (column: "Solar Power Prod" in the xml file)- gross consumption (column: "Gross Con" in the xml file)Further information and code for analysis can be found under:https://kpolsen.github.io/FANFARE/Contains data used pursuant to 'Conditions for use of Danish public-sector data' from the Energi Data Service portal (www.energidataservice.dk).

The Global Solar Energy Market Size Was Worth $90.4 Billion in 2022 and Is Expected To Reach $215.9 Billion by 2030, CAGR of 11.5%.

Solar Energy Index fell to 37.90 USD on July 11, 2025, down 1.79% from the previous day. Over the past month, Solar Energy Index's price has risen 11.14%, but it is still 15.02% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Solar Energy Index.

Solar penetration in the United States stood at roughly 5.4 percent in 2023, that is, solar accounted for5.4 percent of the electricity generated across the country that year. California and Nevada were the states with the highest percentage of solar in their electricity generation, with 28.2 and 25.9 percent, respectively.

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.

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.

Snapshot img

Solar Panels Market Size 2025-2029

The solar panels market size is forecast to increase by USD 60.1 billion, at a CAGR of 8.2% between 2024 and 2029.

The market is witnessing significant growth, driven by increasing investments in the renewable energy sector. This trend is fueled by governments and businesses worldwide seeking to reduce carbon emissions and promote sustainable energy solutions. Another key driver is the continuous advancements in thin-film solar photovoltaic (PV) modules, making solar energy more cost-effective and accessible. However, the intermittent nature of solar power poses a significant challenge. Solar energy production depends on sunlight availability, which can be unpredictable. This variability necessitates efficient energy storage solutions and smart grid management systems to ensure a consistent energy supply. Companies in the market must focus on developing innovative technologies to address this challenge and capitalize on the growing demand for renewable energy. By investing in research and development, collaborating with energy storage providers, and optimizing solar panel designs, companies can effectively navigate this market landscape and seize opportunities for growth.

What will be the Size of the Solar Panels Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe market continues to evolve, driven by technological advancements and expanding applications across various sectors. Off-grid systems, once a niche application, are increasingly gaining traction as an alternative power solution. Thin-film solar panels, with their flexibility and lower manufacturing costs, are finding new uses in solar powered appliances and buildings. Silicon wafer technology, a mainstay in solar panel manufacturing, faces competition from emerging alternatives. Solar powered refrigeration systems are revolutionizing cold storage solutions, while solar tracking systems optimize panel efficiency. Commercial solar adoption is on the rise, with businesses recognizing the cost savings and sustainability benefits. Solar panel warranty and performance degradation are key considerations for investors and consumers alike. Solar panel manufacturing processes are continuously improving, with a focus on reducing costs and increasing efficiency. Solar energy storage and solar energy policy are crucial components of the renewable energy landscape. Industrial solar applications are expanding, from powering factories to providing clean energy for heavy industry. Solar panel maintenance and cleaning are essential for maximizing system performance and longevity. Battery energy storage and net metering are transforming the way we store and distribute solar energy. Solar farm development and ground-mounted solar installations are shaping the future of utility-scale solar. Solar powered electronics, from calculators to smartphones, are becoming increasingly common. Solar powered homes and solar powered buildings are the future of sustainable living. The market is a dynamic and evolving landscape, with endless possibilities.

How is this Solar Panels Industry segmented?

The solar panels industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. End-userPower utilitiesCommercialResidentialTypeCrystalline panelThin-film panelConnectivityOn-gridOff-gridTechnologySolar PVConcentrated solar power (CSP)GeographyNorth AmericaUSCanadaEuropeFranceGermanyItalyUKAPACChinaIndiaJapanSouth KoreaRest of World (ROW).

By End-user Insights

The power utilities segment is estimated to witness significant growth during the forecast period.The solar panel market experienced significant growth in 2024, with the power utility segment leading the way. Utility-scale solar power plants continued to expand, contributing to the reduction of carbon emissions and the generation of clean energy. According to the International Energy Agency (IEA), renewable capacity additions reached record levels in 2024, with solar photovoltaics (PV) accounting for around 80% of the growth in renewable electricity capacity. China, the US, Germany, Japan, and India were among the major contributors to the utility segment. For instance, China installed 277 GW of solar capacity in 2024, as reported by the National Energy Administration (NEA). Residential solar installations also gained traction, with homeowners seeking to save on energy costs and reduce their carbon footprint. Solar panel financing options, such as leasing and power purchase agreements, made solar adoption more accessible. Solar panel costs continued to decline, making solar energy a cost-effective solution for both

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.