climagrid 0.1.0

Climate data, grid-ready — open NOAA/NASA/USDA inputs for electric utility predictive maintenance

climagrid

Free, open-source environmental stress features for electric utility predictive maintenance —
turning public NOAA, NASA, USDA, and USFS data into ML-ready inputs for the tools utilities already run.

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Why this exists

Power outages cost the U.S. economy about $67 billion every year on average, according to Oak Ridge National Laboratory. Most of those outages trace back to weather, vegetation, and environmental stress on aging grid equipment: transformers, power lines, and circuit breakers.

Large utilities can afford expensive software to monitor this risk. The roughly 900 rural electric cooperatives and 2,000 municipal utilities that serve 42 million Americans across 56% of U.S. landmass mostly cannot. They have small engineering teams, no data scientists, and no budget for six-figure software contracts.

climagrid is built for them. It is free, open source (Apache 2.0), and runs on a single laptop. It takes weather and environmental data that the U.S. government already publishes for free, from NOAA, NASA, the USDA, and the U.S. Forest Service, and turns it into the kind of information a maintenance engineer can actually use: which of my transformers are under the most stress this month? Which power-line spans are at the highest fire risk? Which areas need a crew sent out first?

The U.S. Department of Energy reports that predictive maintenance can cut equipment costs by 25-30% and return roughly $10 for every $1 invested. climagrid aims to make those benefits available to the smaller utilities that serve rural America.

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What it does

flowchart LR
    subgraph sources["Public Data Sources"]
        direction TB
        hrrr["NOAA HRRR\natmospheric · 3 km"]
        power["NASA POWER\nsatellite-derived"]
        ncei["NOAA NCEI\nsurface stations"]
        nrcs["USDA NRCS\nsoil sensors"]
        wfigs["USFS WFIGS\nfire perimeters"]
    end

    assets["Your assets\nCSV or GeoJSON\nasset_id · lat · lon"]

    run(["climagrid.run()"])

    subgraph features["Stress Features — one row per asset per hour"]
        direction TB
        thermal["feat_thermal_aging_factor"]
        sag["feat_conductor_sag_index"]
        ice["feat_ice_loading_risk"]
        ft["feat_freeze_thaw_cycles"]
        soil["feat_soil_saturation_index"]
        fire["feat_wildfire_proximity"]
    end

    df[("ML-ready DataFrame")]

    sources --> run
    assets --> run
    run --> features
    features --> df

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Who this is for

• Rural electric cooperatives and municipal utilities that want to start using weather and environmental data in their maintenance planning but don't have a data-science team to build the pipeline.
• Utility engineers who already run predictive-maintenance or anomaly-detection models and want to add environmental features to improve them.
• Researchers and journalists studying grid resilience and rural energy equity.

If you serve fewer than 100,000 meters and your weather "data integration" today is "we check the National Weather Service app before a storm," this toolkit is built for you.

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Quick start

pip install climagrid

import climagrid
from datetime import datetime, timezone

df = climagrid.run(
    "my_transformers.csv",          # CSV with asset_id, lat, lon columns
    start_dt=datetime(2024, 7, 1,  tzinfo=timezone.utc),
    end_dt=datetime(2024, 7, 31, tzinfo=timezone.utc),
    sources=["nasa_power"],         # no API key required
    features="all",
)
df.to_parquet("stress_features.parquet")

You now have a file you can join to your maintenance records and feed into whatever model or spreadsheet you already use. See the quickstart notebook for a worked example.

For the lower-level adapter API (fetching individual data sources, custom feature computation) see the documentation.

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Data sources

Agency	Dataset	Variables	Frequency
NOAA	HRRR (3 km CONUS)	Temperature, wind, precipitation, humidity, solar	Hourly
NOAA	NCEI CDO (stations)	Temperature, wind, precipitation	Hourly/Daily
NASA	POWER API (MERRA-2)	Surface meteorology, irradiance	Hourly/Daily
USDA NRCS	SCAN / SNOTEL	Soil moisture, soil temperature, snow water equivalent	Hourly
USFS / NIFC	WFIGS	Active wildfire perimeters, fire area	Daily

All sources are free and publicly accessible. NOAA NCEI requires a free API token (register at ncdc.noaa.gov/cdo-web/token, then set the NOAA_CDO_TOKEN environment variable). All other sources work without credentials.

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Environmental stress features

Feature	Output column	Target assets	Standard
Transformer thermal aging factor	feat_thermal_aging_factor	Transformers	IEEE C57.91
Heat accumulation hours	feat_heat_hours_above_35c	Transformers, switchgear	IEEE C57.91
Freeze-thaw cycles	feat_freeze_thaw_cycles	Conductors, insulators, poles	—
Ice loading risk	feat_ice_loading_risk	Overhead T&D lines	ASCE 7-22
Soil saturation index	feat_soil_saturation_index	Underground cables, poles	—
Wildfire proximity score	feat_wildfire_proximity	All overhead assets	—
Conductor sag index	feat_conductor_sag_index	Overhead T&D lines	IEEE 738-2012

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How this is different from existing tools

Tool	What it does	How climagrid is different
atlite (PyPSA)	Turns weather into renewable-generation forecasts	climagrid focuses on equipment failure stress, not generation
ERAD (NREL)	Estimates damage from one-time disasters (hurricanes, floods)	climagrid produces continuous stress features for day-to-day maintenance
OpenSTEF	Forecasts feeder load using weather inputs	climagrid produces asset-health features, not load forecasts
NRECA OMF	Simulates rural-coop feeders to evaluate new technologies	climagrid feeds maintenance models, not feeder simulators
EEweather (OpenDSM)	Pulls NOAA temperature for energy-efficiency baselines	climagrid integrates five federal sources and produces grid-stress features

See the full related-work review.

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National impact

• About 42 million Americans are served by rural electric cooperatives, covering 56% of U.S. landmass (NRECA).
• Major U.S. power outages cost roughly $67 billion per year on average (Oak Ridge National Laboratory).
• Predictive maintenance reduces equipment-maintenance costs by 25-30% with about a 10-to-1 return on investment (U.S. Department of Energy).
• climagrid is released under the Apache 2.0 license so that any utility, regardless of size or budget, can use, modify, and redistribute it freely and permanently.

For more on how climagrid ties into U.S. grid resilience priorities, see docs/national_impact.md.

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Citation

If you use climagrid in research, regulatory filings, or utility planning, please cite it using the metadata in CITATION.cff, or:

Adesiji, T. (2026). climagrid: Open-source environmental stress feature toolkit
for electric utility predictive maintenance (v0.1.0). Apache 2.0.
https://doi.org/10.5281/zenodo.XXXXXXX

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Contributing

climagrid welcomes contributions from utility engineers, data scientists, and researchers. See CONTRIBUTING.md for how to add a new data source adapter or stress feature. Project governance is documented in GOVERNANCE.md.

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License

Apache License 2.0. See LICENSE for full text.

Copyright 2026 Temidire Adesiji