NREL quickstart — NSRDB solar + WIND Toolkit (live)¶

The nrel backend fetches solar and wind resource time series from the US NREL/NLR Developer Network keyed CSV download API. Unlike the keyless backends it needs a free API key + the email that registered it (api_key= / email=, or the NREL_API_KEY / NREL_EMAIL environment variables) — register at https://developer.nlr.gov/signup/.

This notebook pulls one year of hourly NSRDB solar data and one year of WIND Toolkit wind data for a single point (Denver, Colorado) and inspects the returned pandas.DataFrames.

In [ ]:

import os
import tempfile

import matplotlib.pyplot as plt

from earthlens.core import EarthLens

# Credentials come from the environment; they are never printed.
API_KEY = os.environ["NREL_API_KEY"]
EMAIL = os.environ["NREL_EMAIL"]

OUT = tempfile.mkdtemp()  # downloads also write a CSV here

import os import tempfile import matplotlib.pyplot as plt from earthlens.core import EarthLens # Credentials come from the environment; they are never printed. API_KEY = os.environ["NREL_API_KEY"] EMAIL = os.environ["NREL_EMAIL"] OUT = tempfile.mkdtemp() # downloads also write a CSV here

Download one year of NSRDB hourly solar¶

data_source="nrel" uses the default nsrdb-psm3 product (NSRDB GOES Aggregated PSM v4). variables= lists the attributes; a single point=(lat, lon) over one year is one keyed CSV call. download() returns the hourly table and writes it to path as CSV.

In [ ]:

solar = EarthLens(
    data_source="nrel",
    variables=["ghi", "dni", "dhi", "air_temperature"],
    start="2020-01-01",
    end="2020-12-31",
    point=(39.74, -105.18),  # Denver, Colorado
    api_key=API_KEY,
    email=EMAIL,
    path=OUT,
).download(progress_bar=False)

print(solar.shape)
solar.head()

solar = EarthLens( data_source="nrel", variables=["ghi", "dni", "dhi", "air_temperature"], start="2020-01-01", end="2020-12-31", point=(39.74, -105.18), # Denver, Colorado api_key=API_KEY, email=EMAIL, path=OUT, ).download(progress_bar=False) print(solar.shape) solar.head()

Each row is one hour. GHI / DNI / DHI are the global / direct / diffuse irradiance components (W/m²), Temperature the air temperature (°C); the lat / lon / year / product columns tag the sampled point. The time column is assembled from the CSV's Year/Month/Day/Hour/Minute fields.

In [ ]:

solar.dtypes

solar.dtypes

Plot a few days of irradiance¶

A short summer window shows the diurnal cycle of GHI.

In [ ]:

window = solar[(solar["time"] >= "2020-06-01") & (solar["time"] < "2020-06-04")]

fig, ax = plt.subplots(figsize=(9, 3))
ax.plot(window["time"], window["GHI"])
ax.set_ylabel("GHI  [W/m²]")
ax.set_title("NSRDB GHI — 1–3 June 2020 (Denver, 39.74°N, -105.18°E)")
fig.autofmt_xdate()
plt.show()

window = solar[(solar["time"] >= "2020-06-01") & (solar["time"] < "2020-06-04")] fig, ax = plt.subplots(figsize=(9, 3)) ax.plot(window["time"], window["GHI"]) ax.set_ylabel("GHI [W/m²]") ax.set_title("NSRDB GHI — 1–3 June 2020 (Denver, 39.74°N, -105.18°E)") fig.autofmt_xdate() plt.show()

Download WIND Toolkit hourly wind¶

The "wind-toolkit" alias selects the WTK product (product="wtk"). WTK attributes are per hub height (windspeed_100m, winddirection_100m, …); the CONUS archive covers roughly 2007–2014.

In [ ]:

wind = EarthLens(
    data_source="wind-toolkit",
    variables=["windspeed_100m", "winddirection_100m", "temperature_100m"],
    start="2012-01-01",
    end="2012-12-31",
    point=(39.74, -105.18),
    api_key=API_KEY,
    email=EMAIL,
    path=OUT,
).download(progress_bar=False)

print(wind.shape)
wind.head()

wind = EarthLens( data_source="wind-toolkit", variables=["windspeed_100m", "winddirection_100m", "temperature_100m"], start="2012-01-01", end="2012-12-31", point=(39.74, -105.18), api_key=API_KEY, email=EMAIL, path=OUT, ).download(progress_bar=False) print(wind.shape) wind.head()

Inspect the product catalog¶

The bundled catalog maps each product id to its source, endpoint, default attributes, and columns. Select a product with product= or the nsrdb / wind-toolkit facade aliases.

In [ ]:

from earthlens.nrel import Catalog

catalog = Catalog()
print(catalog.available())
catalog.get("nsrdb-psm3")

from earthlens.nrel import Catalog catalog = Catalog() print(catalog.available()) catalog.get("nsrdb-psm3")

Takeaway¶

One keyed call per (point, year) returns a tidy hourly DataFrame you can resample or plot directly. NREL already returns the resolved hourly / TMY series, so the facade rejects aggregate=; use pandas (df.set_index("time").resample("1D").mean()) for a coarser cadence. The CSV API is rate-limited (≤1 req/s, 5000/day), so a bbox × multi-year request fans out into many throttled calls — coarsen spacing_deg or narrow the years.