USGS Wind and Solar Energy Data: The Federal Database Behind US Renewable Energy Infrastructure

The United States Geological Survey maintains the most comprehensive public databases of wind turbine locations and utility-scale solar photovoltaic facility data in the United States — 72,000+ wind turbines with GPS coordinates, capacity ratings, hub heights, and rotor diameters, plus a growing solar PV database covering thousands of utility-scale installations.

USGS Energy Resources Program

The United States Geological Survey is best known as the agency that maps earthquakes, tracks streamflow, and publishes topographic maps. Its Energy Resources Program is less widely understood but plays a distinctive role in the national energy data landscape. The program maps and assesses domestic energy resources—coal, oil, natural gas, geothermal, uranium—and publishes open data on the physical infrastructure of the US energy system.

The fossil fuel assessments are the program's longest-running product line. USGS assessments of technically recoverable undiscovered oil and natural gas resources in US sedimentary basins—the Permian Basin, Appalachian Basin, Williston Basin, and dozens of others—are the authoritative federal reference for upstream resource potential. These assessments inform federal royalty policy, leasing decisions on public lands, and long-range supply forecasts published by the Energy Information Administration.

The wind turbine and solar photovoltaic databases represent a different and uniquely valuable data product. Rather than assessing potential resources, they document built infrastructure: where turbines and panels actually exist, their physical characteristics, who operates them, and what capacity they represent. No other federal agency publishes this data at the individual-asset level. The EIA's Form 860 collects plant-level data but not individual turbine coordinates. State energy offices collect varying levels of detail with no national standardization. The USGS databases fill a gap that matters for grid planning, environmental review, academic research, and policy analysis.

Both databases are produced under collaborative agreements with external partners and are published under a CC0 public domain dedication with no restrictions on use. The USGS Energy Resources Program coordinates the collaboration, maintains the data infrastructure at the Energy Resources Science Center (EERSC), and serves the data through the ScienceBase catalog and through dedicated REST APIs at eersc.usgs.gov. All downloads are free with no registration requirement.

The program's collaborative model reflects a recognition that building accurate infrastructure databases requires combining federal data resources with industry knowledge. The American Clean Power Association—the wind and solar industry trade group, formerly the American Wind Energy Association— contributes operational data that fills gaps in public records. Lawrence Berkeley National Laboratory, a Department of Energy national laboratory, contributes analytical capacity and cross-reference against EIA Form 860. The resulting databases are more complete and more accurately georeferenced than any single source could produce independently.

US Wind Turbine Database

The US Wind Turbine Database (USWTDB) is maintained jointly by USGS, Lawrence Berkeley National Laboratory (LBNL), and the American Clean Power Association (formerly AWEA). As of 2023 it contains records for more than 72,000 individual wind turbines installed across the United States. Each turbine is a distinct row with a persistent case ID, allowing year-over-year tracking as new turbines are added, existing turbines are retrofitted, and decommissioned turbines are retired from the database.

The USWTDB is updated quarterly, with new releases incorporating turbines that received FAA obstruction study approval in the preceding quarter, corrections submitted by project operators, and flagging of turbines that have been repowered or decommissioned. The quarterly update cycle means the database typically lags commercial operation dates by one to two quarters, but it is substantially more current than the annual EIA Form 860 reporting cycle.

USWTDB Attribute Schema

Each turbine record in the USWTDB carries a rich set of attributes covering project identification, physical characteristics, siting, and regulatory metadata:

• case_id — a persistent integer assigned by the database maintainers; stable across quarterly releases, allowing longitudinal tracking of individual turbines.
• p_name — the wind project name as reported to the FAA. Project names are not always consistent across data sources; USWTDB normalizes names against FAA obstruction study records.
• p_year — the year the turbine came online. Derived from FAA records and cross-checked against EIA Form 860 commercial operation dates.
• p_tnum — total number of turbines in the project, useful for computing project-level capacity without aggregating individual records.
• p_cap — total project capacity in megawatts (MW).
• t_state / t_county — state abbreviation and county name for the turbine's physical location. County assignment uses the GPS coordinates, not project mailing address, and may differ from the county reported in project permit applications for turbines near county lines.
• t_fips — five-digit FIPS county code (two-digit state + three-digit county), enabling joins to Census demographic data and other county-level datasets.
• xlong / ylat — WGS84 longitude and latitude in decimal degrees. Positional accuracy is typically within 10 meters for turbines installed after approximately 2005, when GPS-referenced FAA obstruction filings became standard. Older turbines may have lower positional accuracy.
• t_manu — turbine manufacturer. The dominant names in the US fleet are Vestas (Danish), GE Vernova (formerly GE Renewable Energy), Siemens Gamesa (Spanish-German), Nordex (German), and Goldwind (Chinese, used in a small number of US projects). Manufacturer field values are not perfectly standardized across the database history; GE turbines appear under multiple name variants reflecting corporate rebranding.
• t_model — turbine model designation as reported by the manufacturer or derived from FAA documents. Model names encode rotor and generator characteristics in manufacturer-specific conventions.
• t_cap — turbine nameplate capacity in kilowatts (kW). Modern land-based turbines typically range from 2,000 kW (2 MW) to 4,000 kW (4 MW), with some models reaching 5 to 6 MW. Offshore turbines range from 8,000 kW (8 MW) to 15,000+ kW for the largest current models. Turbines installed before 2005 often have capacities below 1,000 kW and reflect the smaller rotor diameters and lower hub heights of early-generation technology.
• t_hh — hub height in meters: the distance from ground level to the center of the rotor. Modern land-based turbines have hub heights of 80 to 120 meters; taller towers allow access to stronger, more consistent wind at altitude. Hub height selection is driven by the wind shear profile at the specific site—sites with favorable wind at 80 meters may not justify the additional cost of a 120-meter tower.
• t_rd — rotor diameter in meters: the diameter swept by the turbine blades. Modern land-based rotors range from 90 to 150+ meters in diameter; larger rotors capture more wind energy at a given wind speed, which is particularly valuable at low-wind sites where the capacity factor improvement from a larger rotor can offset the higher cost. Total height (tip height) is approximately hub height plus half the rotor diameter.
• t_ttlh — total height in meters from ground to blade tip at maximum extension.
• t_conf_loc / t_conf_atr — location confidence and attribute confidence codes (1–3), indicating the quality of the GPS coordinates and turbine attribute data respectively. Confidence 3 indicates highest quality; confidence 1 indicates data derived from lower-quality sources or estimation.
• t_img_date — date of satellite or aerial imagery used to verify turbine location. USGS and LBNL staff verify turbine coordinates against imagery for a subset of records, particularly those with lower confidence codes.
• faa_asn — FAA Obstruction Study Number. Every wind turbine in the United States must obtain FAA aeronautical study review under 14 CFR Part 77 (Objects Affecting Navigable Airspace) if it exceeds certain height thresholds above ground or above mean sea level. The FAA Aeronautical Study Number is a stable identifier that links USWTDB records to the FAA Obstruction Evaluation/Airport Airspace Analysis (OE/AAA) database, which contains the full record of the FAA's obstruction analysis including any lighting or marking requirements.
• retrofit — a flag indicating the turbine has been repowered since original installation. Repowering typically involves replacing nacelle components, rotor blades, or both to increase capacity or extend operational life while reusing the original tower and foundation. Repowered turbines often qualify for renewed federal production tax credits under IRS rules, which has made repowering economically attractive for projects installed in the early 2000s that have passed their original credit eligibility period.

US Solar Photovoltaic Database

The US Solar Photovoltaic Database (USPVDB) is a newer addition to the USGS energy infrastructure catalog. LBNL and USGS launched the database in approximately 2021, extending the collaborative data model that had proven successful for the wind turbine database to the utility-scale solar sector. The USPVDB covers utility-scale PV installations—generally those at or above 1 MW of nameplate capacity—and does not include distributed or residential solar installations.

The geographic coverage encompasses thousands of utility-scale solar sites across the contiguous United States and Hawaii. The primary data source is EIA Form 860, which collects annual generator-level data from facilities with 1 MW or more of capacity. LBNL analysts cross-reference EIA 860 records against satellite and aerial imagery to assign precise GPS coordinates to each site, a process the wind turbine database pioneered and that the solar database adopted.

USPVDB attributes include:

• Site name — the facility name as reported to EIA. Commercial-scale solar sites often carry project names assigned by the developer or operator that may differ from local colloquial references.
• Capacity (MWdc and MWac) — nameplate capacity in both DC and AC terms. Solar PV systems are rated in DC watts at standard test conditions (STC), but the inverters that convert DC power to grid-compatible AC power introduce losses of approximately 10 to 15 percent, depending on inverter loading ratio (DC:AC ratio). The DC:AC ratio for US utility-scale solar has trended above 1.2 as developers oversize the panel array relative to the inverter to increase energy production without proportionally increasing interconnection capacity. EIA Form 860 reports both DC and AC capacity for solar generators, and USPVDB preserves both figures.
• Technology type — fixed-tilt or single-axis tracking. Fixed-tilt systems orient panels at a fixed angle toward the south; single-axis trackers rotate panels east-to-west throughout the day to follow the sun. Single-axis tracking adds 5 to 10 percent of system cost but typically increases annual energy production by 15 to 25 percent compared to fixed-tilt at the same location, making it the dominant technology choice for new utility-scale projects where ground coverage ratio allows tracker row spacing.
• Year online — derived from EIA Form 860 commercial operation date. The solar build-out visible in this field shows a near-vertical acceleration beginning in 2018–2019 as module costs fell below $0.25/watt and utility-scale PPA prices reached grid parity in most US markets.
• Site area (km²) — estimated from satellite imagery. Area measurements are approximate and reflect the full site boundary, not the panel-covered area specifically. Land use per MW for utility-scale solar typically ranges from 4 to 10 acres per MW (roughly 1.6 to 4 km² per 100 MW), depending on panel density, row spacing, setbacks, and access roads.
• Latitude/longitude — GPS coordinates for the site centroid or primary access point, verified against imagery.
• State and county — enabling the same geographic joins available for USWTDB records.

The USPVDB is updated less frequently than the USWTDB, reflecting the younger age of the database and the different regulatory infrastructure around solar siting. Wind turbines require FAA obstruction review for most utility-scale installations, providing a federal administrative record that USWTDB can index. Solar installations have no equivalent mandatory federal registration point below the EIA Form 860 threshold, which introduces a one-year lag between commercial operation and database coverage.

US Energy Build-Out Context

The scale visible in the USWTDB and USPVDB reflects an energy transition that is among the most significant infrastructure build-outs in US history. Wind and solar together have gone from marginal contributors in 2010 to the primary source of new generation capacity additions in the early 2020s.

Wind energy: the United States had approximately 140 gigawatts (GW) of installed wind capacity as of end 2022, generated by more than 3,900 distinct wind projects. That capacity is sufficient to supply approximately 43 million US homes at average consumption levels. Texas leads all states by a wide margin with approximately 37 GW of installed wind capacity—more than any country except China and the United States as a whole—followed by Iowa, Oklahoma, Kansas, and Illinois. The USWTDB documents the geographic concentration of US wind in the Great Plains wind resource corridor, where high capacity factors (35 to 45 percent annually) and available land made wind the lowest-cost form of new generation before the solar cost decline of the late 2010s.

Offshore wind is the next frontier visible in the USWTDB. The first commercial US offshore wind projects—South Fork Wind and Vineyard Wind 1—began operations in 2023 and 2024 respectively, representing a decade-long permitting and financing process compared to the 18-to-24-month development timeline for land-based projects. The USWTDB is the primary public database tracking offshore turbine locations as they are installed on the Outer Continental Shelf. Offshore turbines in the database carry the same attribute schema as land-based turbines but with hub heights of 100 to 150 meters and rotor diameters of 200+ meters for the largest current offshore models.

Solar energy: the United States had more than 100 GW of utility-scale and distributed solar PV installed as of end 2022, with utility-scale PV representing approximately 70 GW of that total. Utility-scale solar is the fastest-growing segment of the US generation fleet, with annual additions exceeding 20 GW in recent years. The USPVDB captures the utility-scale portion: large projects in the Southwest—California, Arizona, Nevada, Texas—that benefit from high irradiance, but also an increasingly distributed national footprint as PPA pricing has reached parity with fossil generation across most of the continental US.

The Inflation Reduction Act of 2022 (IRA) restructured the federal tax incentives that drive renewable energy investment. The Production Tax Credit (PTC) for wind and the Investment Tax Credit (ITC) for solar were extended through 2032 at their full rates for projects meeting domestic content and prevailing wage requirements, and made permanent at reduced rates for projects coming online after 2032. The IRA also introduced new credits for offshore wind and standalone battery storage. The USWTDB and USPVDB will document the infrastructure response to these incentives over the coming decade; the quarterly and annual update cycles will trace the capacity additions the IRA is expected to stimulate.

The Department of Energy's clean electricity goal—a carbon-free electricity system by 2035—requires approximately tripling current wind and solar capacity from 2022 levels within 13 years. The USWTDB and USPVDB provide the baseline measurement of existing capacity from which that build-out must proceed, and will serve as the primary federal record of progress toward the goal.

Data Applications

The USWTDB and USPVDB support a wide range of analytical applications beyond simple inventory counts:

Siting conflict analysis. The GPS coordinates in USWTDB enable proximity analysis against other spatially referenced datasets. FAA obstruction study numbers link turbines to the aeronautical study database, where analysts can identify turbines that received “determinations of hazard” and were built anyway (rare but documented cases), or that were conditioned on specific lighting schemes that may affect viewshed sensitivity. Avian migration corridor databases from the US Fish and Wildlife Service can be overlaid against turbine locations to quantify exposure of specific turbine populations to migratory bird flyways. Viewshed analysis—computational modeling of which turbines are visible from specific vantage points—is used in National Historic Preservation Act Section 106 review and NEPA environmental impact assessments, both of which require spatial precision that the USWTDB provides.

Decommissioning tracking. The USWTDB includes a decommissioning flag and, where known, the year a turbine was removed from service. This enables tracking of the first generation of US commercial wind turbines, installed in California's Altamont Pass, Tehachapi, and San Gorgonio mountain passes in the 1980s and 1990s, many of which have been decommissioned or repowered as their 20-to-25-year design lives expired. The decommissioning record also supports economic analysis of end-of-life costs for project finance models, which must set aside reserves for turbine removal and site restoration under most state permit conditions and power purchase agreement terms.

Manufacturer market share analysis. The manufacturer and model fields support competitive analysis of the US wind turbine supply chain. Vestas and GE have historically dominated the US market by unit count, each holding roughly 20 to 30 percent share across the full installed fleet. Siemens Gamesa, formed by the 2017 merger of Siemens Wind Power and Gamesa, holds significant share in both onshore and offshore segments. Nordex and Goldwind represent smaller but growing presences. The shift in market share over time—visible by filtering on installation year—reflects technology competition (larger rotors, taller towers), supply chain disruptions including the COVID-era shipping bottlenecks and the US trade dispute over tower tariffs, and the concentration of new offshore projects with Siemens Gamesa and Vestas Offshore.

Capacity factor estimation by geography. Hub height and rotor diameter, combined with location coordinates, allow estimation of the wind resource accessible to each turbine. Wind resource maps from the National Renewable Energy Laboratory (NREL)—the Wind Integration National Dataset (WIND) Toolkit—provide modeled annual average wind speeds at multiple hub heights. Combining USWTDB coordinates with WIND Toolkit wind speed data allows estimation of theoretical capacity factors at each turbine location, which can be compared against actual generation from EIA Form 923 at the project level to calibrate capacity factor models.

Property value studies. Academic literature on the relationship between wind turbine proximity and residential property values has used USWTDB coordinates as the primary source of turbine locations since the database's public release. Studies typically construct buffers of 1, 2, 3, and 5 miles around turbines using the GPS coordinates and examine whether home sale prices within those buffers differ from comparable homes outside them, controlling for hedonic characteristics. The USWTDB's GPS precision and complete national coverage make it the preferred dataset for this application.

Grid interconnection planning. The USWTDB and USPVDB spatial data supports analysis of transmission infrastructure requirements for serving future wind and solar development. Regional transmission organizations (RTOs) and independent system operators (ISOs) use the existing project locations as anchor points for modeling transmission congestion, identifying grid constrained regions where the interconnection queue is longest relative to available capacity, and planning transmission expansion under FERC Order 1920.

NEPA environmental review support. Federal and federally permitted wind projects on public lands must complete National Environmental Policy Act environmental review. USWTDB coordinates for existing projects within and around the study area provide context for cumulative impact analysis—the NEPA requirement to assess the combined environmental effects of the proposed project together with existing and reasonably foreseeable future projects. Analysts use the USWTDB to identify the existing turbine population within a defined radius of a proposed project site and quantify the cumulative shadow flicker, noise, and visual impact envelopes.

Relationship to EIA Form 860

EIA Form 860 (Annual Electric Generator Report) is the authoritative federal census of utility-scale electricity generating capacity. Every facility with 1 MW or more of capacity must file Form 860 annually, reporting generator characteristics, operational status, and planned changes. For wind and solar, this means every project above the threshold is captured in Form 860, making it the most complete official record of installed capacity.

The relationship between Form 860 and the USWTDB/USPVDB is complementary rather than duplicative. Form 860 operates at the plant and generator level: a wind project with 100 turbines appears in Form 860 as a single generator record (or in some cases multiple generator records if different turbine models are used within the same project). Form 860 records capacity (MW), county, state, and latitude/longitude of the project access point, but does not report individual turbine locations, hub heights, rotor diameters, or turbine-level capacity.

The USWTDB extends Form 860 data in two critical ways. First, it provides individual turbine GPS coordinates rather than a single project centroid, enabling the proximity and spatial analyses described above. A project coordinate in Form 860 is typically the project substation or access road entrance; individual turbines may be distributed across several miles of terrain in any direction from that point. Second, the USWTDB captures physical turbine characteristics—hub height, rotor diameter, manufacturer, model—that are not reported in Form 860 but are essential for wind resource analysis and mechanical performance modeling.

LBNL maintains a crosswalk between USWTDB case IDs and EIA plant codes, enabling analysts to link individual turbine records in the USWTDB to plant-level generation data from EIA Form 923 and retail sales data from EIA Form 861. The crosswalk is published alongside the USWTDB quarterly releases. This linkage makes it possible to compute realized capacity factors at the project level (generation from Form 923 divided by nameplate capacity from USWTDB) and examine how capacity factors vary by turbine vintage, manufacturer, hub height, and rotor diameter across the national fleet.

For solar, the USPVDB relationship to Form 860 is structurally identical. Form 860 Schedule 3 covers solar generators; USPVDB adds GPS precision and site area measurements derived from imagery analysis that Form 860 does not capture. The DC capacity reported in USPVDB comes directly from Form 860 Schedule 3, making USPVDB essentially a spatially enriched derivative of Form 860 solar records rather than an independent data collection.

Data Access

The USWTDB is accessible through multiple channels:

• REST API — https://eersc.usgs.gov/api/uswtdb/v1/turbineswith format parameters supporting CSV, GeoJSON, and JSON output. The CSV endpoint returns the full turbine table without pagination limits; the JSON endpoints support filtering by state, year, capacity, and other fields.
• ScienceBase catalog — the USGS ScienceBase data repository at sciencebase.gov hosts the USWTDB as a catalog item with attached file downloads for each quarterly release, including CSV, GeoJSON, and ESRI shapefile formats. ScienceBase assigns a persistent DOI to each release, enabling formal citation in academic publications.
• DOI Open Data portal — data.doi.gov indexes USGS datasets including both the USWTDB and USPVDB, linking to the ScienceBase catalog entries and providing additional metadata in DCAT format for automated catalog harvesting.
• Direct download — the EERSC website at eersc.usgs.gov/USWindTurbineDatabase provides HTML landing pages with direct links to the current and archived quarterly releases in all formats.

For EIA Form 860 data, the primary access point is eia.gov/electricity/data/eia860/. EIA publishes the full Form 860 annual release in Excel workbook format with separate schedules for plant data (Schedule 1), generator data (Schedules 3A through 3E by fuel type, including 3C for wind and 3D for solar), ownership data (Schedule 2), and energy storage data (Schedule 6). The Form 860 release also includes the “860M” monthly update files that track planned generator additions and retirements with more current status than the annual release.

NREL's Open Energy Data Initiative (OEDI) at data.openei.org hosts supplementary wind and solar datasets that complement the USWTDB and USPVDB, including the WIND Toolkit modeled wind speed data, the National Solar Radiation Database (NSRDB), and various project-level data compilations from NREL's own research programs.

Python: Analyzing the Wind Turbine Database

The following Python script downloads the USWTDB directly from the EERSC API in CSV format and performs three core analyses: state-level capacity totals, manufacturer market share by turbine count, and capacity tier distribution. The EERSC API returns the full turbine table in a single response without requiring pagination; the only dependency beyond the standard library is pandas and requests, both of which are available in the default Anaconda and pip environments.

import requests, pandas as pd, io

# US Wind Turbine Database — CSV download
url = "https://eersc.usgs.gov/api/uswtdb/v1/turbines?&format=csv"
# Alternative direct download:
# url = "https://www.sciencebase.gov/catalog/file/get/57bdfd8fe4b03fd6b7df5ff9?f=__disk__..."
resp = requests.get(url, timeout=60)
df = pd.read_csv(io.StringIO(resp.text))

print(f"US Wind Turbine Database: {len(df):,} turbines")
print(f"Columns: {list(df.columns[:8])}")

# State totals
state_cap = df.groupby('t_state').agg(
    turbines=('case_id', 'count'),
    total_mw=('t_cap', lambda x: x.sum() / 1000)
).sort_values('total_mw', ascending=False)
print("\nTop 10 states by installed wind capacity:")
print(state_cap.head(10).to_string())

# Manufacturer market share
mfr = df['t_manu'].value_counts().head(10)
print("\nTop 10 turbine manufacturers by unit count:")
print(mfr.to_string())

# Capacity distribution (kW)
df['t_cap'] = pd.to_numeric(df['t_cap'], errors='coerce')
bins = [0, 1000, 2000, 3000, 4000, 6000, 20000]
labels = ['<1MW', '1-2MW', '2-3MW', '3-4MW', '4-6MW', '>6MW']
df['cap_tier'] = pd.cut(df['t_cap'], bins=bins, labels=labels)
print("\nCapacity tier distribution:")
print(df['cap_tier'].value_counts().sort_index().to_string())

The state capacity totals (total_mw column) convert the per-turbine capacity in kilowatts to megawatts by dividing the sum by 1,000. Texas will appear at the top with approximately 37,000 MW; the next tier of Iowa, Oklahoma, Kansas, and Illinois will each show 10,000 to 20,000 MW. The manufacturer analysis will surface Vestas and GE as the dominant suppliers by unit count, though the specific ranking depends on the release date of the download; GE has consolidated market share in recent years as Vestas focused on international markets.

The capacity tier distribution reveals the technology evolution of the US wind fleet. The oldest turbines in the database—installed in California in the 1980s and 1990s—fall in the under-1 MW category, many with capacities of 65 to 750 kW. The dominant cohort for 2005–2015 installations is 1–2 MW and 2–3 MW. Projects installed after 2018 are overwhelmingly in the 3–4 MW and 4–6 MW tiers, reflecting the rapid scaling of turbine technology as manufacturers competed to reduce the levelized cost of energy through larger rotors and higher hub heights. The over-6 MW category represents offshore turbines, a small but growing portion of the database.

To extend the script toward USPVDB data, replace the USWTDB URL with the USPVDB endpoint at https://eersc.usgs.gov/api/uspvdb/v1/plants?&format=csv. The attribute schema differs from the wind database—no hub height or rotor diameter fields, but DC and AC capacity, technology type (fixed/tracking), and site area are present. The same state-level aggregation logic applies.