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The National Renewable Energy Laboratory provides datasets on solar irradiance, wind resources, building energy use, electric vehicles, and grid stability. Used in data engineering for clean energy analytics pipelines, resource assessment systems, and building renewable energy forecasting models in Python.
NREL provides APIs for the National Solar Radiation Database (NSRDB) and Wind Integration National Dataset (WIND). Engineers use the `NREL Developer Network` API key with `requests` to query solar and wind resource data by GPS coordinates, getting hourly irradiance and wind speed time-series.
NREL's renewable energy datasets train AI models for solar output prediction, wind farm optimization, and grid stability analysis. RAG systems built on NREL data can answer 'What is the average solar capacity factor for Phoenix, AZ?' to support AI-assisted renewable energy planning.
# pip install requests pandas
import requests, pandas as pd
resp = requests.get(
"https://developer.nrel.gov/api/solar/nsrdb_psm3_download.json",
params={"api_key": "YOUR_API_KEY", "lat": 33.45, "lon": -111.85,
"year": 2022, "attributes": "ghi,dhi,dni", "interval": 60}
)
df = pd.read_csv(pd.io.common.StringIO(resp.text), skiprows=2)
print(df[["Year", "Month", "GHI", "DNI"]].head(10))Official dataset source
More datasets used by Python data engineers.
The Federal Reserve Bank of St. Louis FRED database provides over 800,000 economic time series from 100+ sources, including interest rates, inflation, GDP, and employment data. Widely used in financial and economic data pipelines via the fredapi Python library for loading macro data into analytical systems.
The FEC provides access to campaign finance data, including information on political contributions, campaign expenditures, fundraising activities and financial disclosures filed by political candidates, parties and committees in the United States.
The US Federal Aviation Administration publishes datasets on aircraft registrations, pilot certifications, airport data, accident reports, and air traffic statistics. Used in data engineering for aviation analytics pipelines, safety analysis systems, and building aeronautical intelligence dashboards in Python.