The solar dataset contains approximately 6000 simulated time series representing 5-minute solar power and hourly day-ahead forecasts of photovoltaic (PV) power plants in United States in 2006.

The uploaded dataset contains the aggregated version of a subset of the original dataset used by Lai et al. (2017). It contains 137 time series representing the solar power production recorded per every 10 minutes in Alabama state in 2006.

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.

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.

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.

A machine readable collection of documented solar siting ordinances at the state and local (e.g., county, township) level throughout the United States. The data were compiled based on a locality-by-locality review zoning ordinances after completing an initial review of scholarly legal articles. The citations for each ordinance are included in the Solar Ordinances spreadsheet resource below.

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

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

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%.

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 contains a subset of data from our Building Permit Application dataset. The data has been filtered to only include permit applications for solar (permit type = PVRS).To learn more about solar energy in Cary check out our Solar Energy webpage.This file is created from the Town of Cary permit application data. It has been created to conform to the BLDS open data specification for building permit data (permitdata.org). In the Town of Cary a permit application may result in the creation of several permits. Rows in this table represent applications for permits, not individual permits. Individual permits may be released as a separate dataset. With the exception of a few fields, we have applied all of the required and preferred fields of the required dataset for permits. This data is updated daily.Used as a part of the Solar, Cary, and You Dashboard

Solar energy accounted for some 5.6 percent of electricity generation in the United States in 2023, up from a 4.8 percent share a year earlier. California was the state with the largest percentage of its electricity generation covered by solar, with approximately 27.3 percent.

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)

Solar Farm Market size was valued at USD 93.84 Billion in 2024 and is projected to reach USD 275.54 Billion by 2031, growing at a CAGR of 15.90% from 2024 to 2031.

Global Solar Farm Market Drivers

Declining Costs: As a result of technological improvements, economies of scale, and heightened market rivalry, the cost of solar photovoltaic (PV) technology has been progressively dropping over time. As a result, solar energy is becoming more and more competitive with conventional fossil fuels. Environmental Concerns: As people's awareness of and concern for environmental degradation and climate change grows, governments, businesses, and consumers are looking for greener, renewable energy sources, such solar power. With less greenhouse gas emissions than fossil fuels, solar farms provide a sustainable energy source. Government Policies and Incentives: To encourage the use of solar energy, numerous governments throughout the world are putting supportive policies and incentives into place. These consist of feed-in tariffs, tax credits, renewable energy goals, and financial assistance for solar power projects. These regulations aid in lowering up-front expenses and promote solar farm investment. Energy Security: By broadening the energy mix and lowering reliance on imported fossil fuels, solar energy helps to provide energy security. This is especially crucial for nations that depend substantially on energy imports or are susceptible to supply disruptions. Technological Advancements: The efficiency, robustness, and scalability of solar PV systems have all improved as a result of ongoing research and development activities in solar technology. The possible uses and viability of solar farms are growing because to innovations like bifacial panels, floating solar farms, and energy storage systems.

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.

In 2023, China was the leading country in the world based on solar energy consumption share, at 35.6 percent. Meanwhile, the United States accounted for approximately 14.7 percent of the world's solar consumption that year, making it the second-largest solar power consumer worldwide.

In 2024, solar PV demand is expected to total ***** gigawatts around the world. The United States has started a process to implement taxes on solar products from China and Taiwan, which has initiated trade disputes around the world. Solar energy demand – additional information Worldwide solar photovotalic (PV) power demand has been experiencing exponential growth in the last decade. During this period, PV evolved from a niche market of small scale applications to becoming one of the main renewable electricity sources. Solar photovoltaics systems today are recognized as a promising renewable energy technology. As of 2018, the largest solar PV power plants were predominantly from the India. The Bhadla Industrial Solar Park in India represented one the world’s largest solar photovoltaic power plant with a capacity of **** gigawatts. For several years, the growth of solar PV was mainly driven by Germany and other pioneering European countries. Cost of solar declined significantly due to improvements in technology and economies of scale when production of solar cells and modules began to ramp up around the world due to rising solar PV demand. As of 2018, China and the United States were the world’s leader in terms of newly installed solar PV capacity. China accounted for around ** percent of the world’s total new installed grid-connected PV capacity, with United States and India ranked second at ** percent each. Global cumulative installed solar PV capacity increased more than twofold between 2015 and 2018 to approximately ******* megawatts. China, the United States, Japan, and Germany were the most important markets for solar photovoltaic installations at the end of 2018.

This dataset provides historical values of global, direct and diffuse solar irradiation, as well as direct normal irradiation, on a latitude/longitude grid covering land surfaces and coastal areas of Europe, Africa, Oceania, Eastern South America, the Middle East and South-East Asia. It is created from 15 minute resolved timeseries at each grid point. These timeseries were calculated by the CAMS Solar Radiation Time Series Service and use information on aerosol, ozone and water vapour from the CAMS global forecasting system. Other properties, such as ground albedo and ground elevation, are also taken into account. Data is provided for both clear-sky and observed cloud conditions. For cloudy conditions high-resolution cloud information is directly inferred from satellite observations provided by the Meteosat Second Generation (MSG) and Himawari 8 satellites. It is the Himawari satellite that provides the Asian coverage, which is only available from 2016 and v4.6 (rev2) onwards. The aim of the dataset is to fulfil the needs of European and national policy development and the requirements of both commercial and public downstream services, e.g. for planning, monitoring, efficiency improvements and the integration of solar energy systems into energy supply grids. Data is offered in monthly netCDF formatted files.

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.

Solar Panel Infrared Images

## Overview

Solar Panel Infrared Images is a dataset for object detection tasks - it contains Defected Solar Panels annotations for 377 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

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.