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

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

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

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

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

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

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.

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.

optimization methods

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.

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

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

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

The PUDL Repository on GitHub

PUDL Documentation

Other Catalyst Cooperative data archives

One-minute averaged values and one-second instantaneous values for 2015 through 2018 for three grid-connected photovoltaic arrays on the NIST campus in Gaithersburg, Maryland USA. The arrays are built from monocrystalline silicon modules and range from 73 kW to 217 kW. Each array has a different tilt, orientation, and configuration. Irradiance, temperature, wind, and electrical measurements are recorded at each array. One-minute average data and one-second instantaneous data from a nearby weather station and images of the sky and arrays at one-hour and five-minute intervals are also included.

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

A database of quantum mechanical calculations on organic photovoltaic candidate molecules. Related Publications: Peter C. St. John, Caleb Phillips, Travis W. Kemper, A. Nolan Wilson, Michael F. Crowley, Mark R. Nimlos, Ross E. Larsen. (2018) Message-passing neural networks for high-throughput polymer screening arXiv:1807.10363

Lawrence Berkeley National Laboratory (Berkeley Lab) estimates hourly project-level generation data for utility-scale solar projects and hourly county-level generation data for residential and non-residential distributed photovoltaic (PV) systems in the seven organized wholesale markets and 10 additional Balancing Areas. To encourage its broader use, Berkeley Lab has made this data file public here at OEDI, covering the years 2012-2020. The public project-level dataset is updated annually with data from the previous calendar year. For more information about the research project, including a technical report, briefing material, visualizations, and additional data, please visit the project homepage linked in this submission.

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

The increase in power electronic based generation sources require accurate modeling of inverters. Accurate modeling requires experimental data over wider operation range. We used 20 kW off-the-shelf grid following PV inverter in the experiments. We used controllable AC supply and controllable DC supply to emulate AC and DC side characteristics. The experiments were performed at NREL's Energy Systems Integration Facility. Due to the limitations of the DC supply used, inverter is tested under 75%, 50%, 25% load conditions (This dataset does not contain 100% load condition). In the first dataset, for each operating condition, controllable AC source voltage is varied from 0.88 to 1.09 per unit (p.u) with a step value of 0.025 p.u while keeping the frequency at 60 Hz. In the second dataset, under similar load conditions (75%, 50%, 25% ), the frequency of the controllable AC source voltage was varied from 59.4 Hz to 60.45 Hz with a step value of 0.2 Hz. Voltage and frequency range is chosen based on inverter protection. Voltages and currents on DC and AC side are included in the dataset.

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 is the data set named "Supplementary Data 1" for the research paper "Solar PV competitiveness in mature markets without subsidy". This data set also contains the raw data for reproducing Figure 1 through to Figure 4. The paper is currently under review and access is for peer-review purposes only.

The global PV Data Collector market is poised for significant growth, projected to reach $360.1 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 4.0% from 2025 to 2033. This expansion is fueled by the increasing adoption of renewable energy sources, driven by governmental policies promoting solar energy and rising environmental concerns. The market is segmented by type (Commercial Photovoltaic Data Collector and Distributed PV Data Collector) and application (Ground Power Station and Distributed Photovoltaic Power Station). The growing demand for efficient monitoring and management of large-scale solar power plants is a key driver for the Commercial Photovoltaic Data Collector segment. Conversely, the Distributed PV Data Collector segment benefits from the proliferation of rooftop solar installations in residential and commercial settings, requiring sophisticated monitoring solutions. Technological advancements leading to improved data analytics capabilities and the integration of smart grid technologies are further contributing to market growth. While challenges exist, such as the initial high investment costs associated with data collector implementation and the need for robust cybersecurity measures, these are being mitigated by decreasing hardware prices and the development of more sophisticated and cost-effective data security solutions. The market is geographically diverse, with North America, Europe, and Asia Pacific representing key regions, each exhibiting varying growth rates influenced by distinct regulatory landscapes and solar energy adoption patterns. Key players, including Taoke, INVT, Delta Electronics, Huawei, and SMA Solar Technology AG, are actively shaping the market through innovation and strategic partnerships. The competitive landscape is characterized by both established players and emerging companies striving for market share. Established companies leverage their extensive experience and strong distribution networks, while innovative startups introduce cutting-edge technologies. Future market growth will be strongly influenced by government incentives, technological breakthroughs, and the overall expansion of the solar power industry. Continued investment in research and development of more advanced data collection and analysis methods, as well as enhanced data security protocols, will be crucial for sustaining the market’s projected growth trajectory. Furthermore, the increasing demand for real-time data monitoring and predictive maintenance capabilities will propel demand for sophisticated data collectors. The integration of Artificial Intelligence (AI) and machine learning (ML) in these devices is expected to further enhance their functionality and efficiency, driving market expansion in the coming years.