⚙️ How-To Wind turbines Coastal & marine Notebook By

How to Map US Offshore Wind Turbines

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Problem statement

The United States has only a few offshore wind turbines, but they are the largest machines in the country's fleet: rotors of 200 m and more on generators of 11 to 13 MW, most of them standing 14 to 64 km off the coasts of New England, New York and Virginia. The US Wind Turbine Database marks them with t_offshore and records them as they appear on imagery, so projects still under construction show only the turbines already standing. Most offshore rows have no state or county, because they stand in federal waters, so any grouping by state quietly drops them. This page maps the offshore turbines of version 9.1, assigns each project to its nearest coast and measures how far out they are.

Quick answer

Select t_offshore == 1, summarise by project, and compare the turbines in the file with the project's planned count p_tnum:

off = wt[wt.t_offshore == 1]
proj = off.groupby("p_name").agg(turbines=("case_id", "size"), planned=("p_tnum", "first"), year=("p_year", "first"),
                                 model=("t_model", "first"), mw_each=("t_cap", lambda s: s.iloc[0] / 1e3), state=("t_state", "first"))
print(proj.to_string())
print(f"offshore turbines: {len(off)}; with no state: {off.t_state.isna().sum()}")
                                turbines  planned    year           model  mw_each state
p_name                                                                                  
Block Island                           5        5  2016.0   Haliade 150-6      6.0    RI
Coastal Virginia Offshore Wind         7        7  2020.0     SWT-6.0-154      6.0   NaN
Empire Wind I                          5        5  2026.0             NaN      NaN   NaN
Revolution Wind                       28       65  2025.0  SG-11.0-200-DD     11.0   NaN
South Fork Wind Farm                  12       12  2024.0        SG11-200     11.0   NaN
Sunrise Wind                          10       10  2026.0  SG-11.0-200-DD     11.0   NaN
Vineyard Wind                         61       62  2025.0       Haliade-X     13.0   NaN
unknown NA                             1        1  2026.0             NaN      NaN   NaN
offshore turbines: 129; with no state: 124

The file has 129 offshore turbines in seven named projects and one unnamed turbine. Block Island, Coastal Virginia's turbines, South Fork, Sunrise and Empire Wind I show as many turbines as planned in p_tnum. Revolution Wind has 28 of its 65 and Vineyard Wind 61 of 62: USGS adds a turbine when it is seen standing on imagery, so a project under construction fills in version by version. Empire Wind I and the unnamed turbine are too new for specifications.

Map of the sea south of Long Island and southern New England with offshore turbines coloured by project: Block Island off Rhode Island, South Fork, Revolution and Sunrise further out, and Vineyard Wind's grid south of Martha's Vineyard. Map of the sea south of Long Island and southern New England with offshore turbines coloured by project: Block Island off Rhode Island, South Fork, Revolution and Sunrise further out, and Vineyard Wind's grid south of Martha's Vineyard.
Offshore turbines south of New England and Long Island.

No state, partly built

1. Why there is no state

print(wt[wt.t_state.isna()].t_offshore.value_counts().to_string())
print(f"offshore capacity that a groupby('t_state') drops: {off[off.t_state.isna()].t_cap.sum() / 1e3:,.0f} MW")
t_offshore
1    124
offshore capacity that a groupby('t_state') drops: 1,008 MW

Only Block Island stands in Rhode Island's own waters, within three nautical miles of the coast. Every other project is in federal waters on the outer continental shelf, outside any state or county, so the spatial join USGS uses to fill t_state and t_fips finds nothing.

2. Nearest coast

g = gpd.GeoDataFrame(off, geometry=gpd.points_from_xy(off.xlong, off.ylat), crs=4269).to_crs(5070)
land = counties.to_crs(5070)
near = gpd.sjoin_nearest(g, land[["NAME", "STUSPS", "geometry"]], distance_col="shore_m")
print(near.groupby("p_name").agg(state=("STUSPS", lambda s: s.mode().iloc[0]), county=("NAME", lambda s: s.mode().iloc[0]),
                                 km_min=("shore_m", lambda s: s.min() / 1e3), km_max=("shore_m", lambda s: s.max() / 1e3)).round(1).to_string())
                               state          county  km_min  km_max
p_name                                                              
Block Island                      RI      Washington     2.8     4.7
Coastal Virginia Offshore Wind    VA  Virginia Beach    38.3    63.5
Empire Wind I                     NY          Nassau    23.9    39.3
Revolution Wind                   MA           Dukes    13.5    25.4
South Fork Wind Farm              RI      Washington    27.8    32.2
Sunrise Wind                      RI      Washington    24.6    36.2
Vineyard Wind                     MA       Nantucket    23.7    37.0
unknown NA                        MA           Dukes    16.0    16.0

Measured to the nearest county boundary, Block Island's turbines stand 2.8 to 4.7 km offshore, and the federal-water projects 13.5 to 63.5 km out, Coastal Virginia furthest. The nearest county is not where the power lands or which state leases the area. It is only a label for grouping, so keep it in its own column rather than writing it into t_state.

Range bars of distance from shore by project, from Block Island at 2.8 to 4.7 km to Coastal Virginia Offshore Wind at 38.3 to 63.5 km. Range bars of distance from shore by project, from Block Island at 2.8 to 4.7 km to Coastal Virginia Offshore Wind at 38.3 to 63.5 km.
How far out each offshore project stands.

The machines and the map

The data used on this page

The US Geological Survey's US Wind Turbine Database, version 9.1 of 28 September 2026 (Hoen and others, doi:10.5066/F7TX3DN0), public domain, saved on this site on 19 October 2026 because USGS replaces the file every quarter, and the Census Bureau's 2024 cartographic boundary counties (1:20 million, so distances to the coast are approximate to a few hundred metres).

from pathlib import Path
import zipfile

import geopandas as gpd
import pandas as pd
import requests

Path("data").mkdir(exist_ok=True)
# USGS US Wind Turbine Database v9.1 (28 September 2026) with its metadata and changelog, Census 2024 cartographic
# boundary counties and the EIA-860 2025 wind table. Saved 19 October 2026: USGS replaces the file every quarter.
# Live: https://energy.usgs.gov/uswtdb/assets/data/uswtdbCSV.zip
ARCHIVE = "usgs-uswtdb-v9-1-2026-10-19.zip"
if not Path(f"data/{ARCHIVE}").exists():
    Path(f"data/{ARCHIVE}").write_bytes(requests.get(f"https://data.spatialworkflow.io/{ARCHIVE}", timeout=600).content)
Z = zipfile.ZipFile(f"data/{ARCHIVE}")
wt = pd.read_csv(Z.open("uswtdb_V9_1_20260928.csv"), dtype={"t_fips": str})
Path("data/cb_2024_us_county_20m.zip").write_bytes(Z.read("cb_2024_us_county_20m.zip"))
counties = gpd.read_file("data/cb_2024_us_county_20m.zip")
print(f"US Wind Turbine Database v9.1: {len(wt):,} turbines, {int(wt.t_offshore.sum())} offshore")
US Wind Turbine Database v9.1: 77,839 turbines, 129 offshore

Example 1: offshore against onshore

on = wt[(wt.t_offshore == 0) & (wt.p_year >= 2024)]
print(pd.DataFrame({"offshore": off[["t_cap", "t_rd", "t_hh", "t_ttlh"]].median(), "onshore since 2024": on[["t_cap", "t_rd", "t_hh", "t_ttlh"]].median()}).to_string())
        offshore  onshore since 2024
t_cap    11000.0              3400.0
t_rd       200.0               140.0
t_hh       137.5               101.0
t_ttlh     239.9               177.4

The median offshore turbine is rated at 11 MW, more than three times the 3.4 MW of onshore turbines from the same years, with a rotor 60 m wider and a hub 36.5 m higher. Its blade tips reach 239.9 m.

Scatter of rotor diameter against rated capacity for every turbine, onshore turbines in grey up to about 6 MW and 160 m, offshore turbines in blue at 6 MW and 150 m and at 11 to 14 MW and 200 to 222 m. Scatter of rotor diameter against rated capacity for every turbine, onshore turbines in grey up to about 6 MW and 160 m, offshore turbines in blue at 6 MW and 150 m and at 11 to 14 MW and 200 to 222 m.
Rotor against rating: offshore turbines stand apart.

Example 2: the map

ne = near.to_crs(4326)
ax = counties[counties.STUSPS.isin(["MA", "RI", "CT", "NY", "NJ"])].plot(color="#e5e7eb", edgecolor="white", figsize=(8, 6))
ne.plot(ax=ax, column="p_name", markersize=6, legend=True)
ax.set_xlim(-74.5, -69.8); ax.set_ylim(40.3, 42.2)
print(f"turbines drawn south of New England and Long Island: {(ne.geometry.y > 40).sum()}")
turbines drawn south of New England and Long Island: 122

122 of the turbines lie in the waters south of Martha's Vineyard, Nantucket, Rhode Island and Long Island, where the federal lease areas are. The other seven are Coastal Virginia's, off Virginia Beach. On the map the projects form tidy grids, the subject of the spacing page.