Integrating Satellite Weather and Vegetation Data with Energy API to Predict and Prioritize Distribution Outages for Field Crews

You need to proactively predict and prioritize distribution outages for field crews—before windstorms, heatwaves, or dry-vegetation corridors push your network over the edge. By the end of this post, you’ll be able to fuse satellite weather and vegetation layers (e.g., wind gust, precipitation, temperature anomalies, drought/NDVI) with wholesale electricity and carbon signals from Energy API to produce a ranked outage risk list your dispatchers can act on.

Introduction

Grid operators and utility engineering teams already track satellite-derived weather and vegetation risk—think storm intensity, drought stress, and canopy density overlaying overhead lines. But when crews are stretched and budgets are fixed, it’s not enough to know “where” a storm is likely to hit. You also need to know “which circuits, if they fail, will hurt the most”—in other words, where the system is most exposed economically or environmentally at this exact moment.

This is where market-awareness matters. When day-ahead electricity prices are spiking, when gas supply risk radiates into power markets, and when grid carbon intensity is elevated, the cost of an outage (financial, regulatory, and ESG) climbs. Pairing satellite weather and vegetation risk with real-time energy market data lets you elevate the right feeders, substations, and spans for pre-staging and patrols.

In this tutorial, we’ll show how to integrate your existing satellite weather and vegetation data with Energy API’s unified JSON interface across electricity, gas, oil, coal, carbon allowances, and carbon intensity. You’ll make a few calls, cache the responses, and roll these features into an outage risk score that your planners can sort and your field crews can trust.

Why Energy API

Weather and vegetation pipelines are already complex—raster tiling, resampling, georeferencing, and thresholds. Don’t add the pain of stitching OMIE, ENTSO-E, EIA/FRED, and ESIOS market endpoints together. Energy API collapses this into one predictable surface.

• One normalized REST interface: Query electricity, gas, oil, coal, carbon allowances, and grid carbon intensity using the same schema, so you spend time building outage scores instead of reconciling CSV quirks or timezones.
• Intraday and daily coverage where available: Pull day-ahead or intraday electricity curves to align with short-fuse weather windows (e.g., next 24 hours of storm probability), enabling hour-by-hour outage prioritization.
• Multi-commodity awareness in one call: Ask for OMIE day-ahead and TTF gas in a single request to capture how upstream fuel risk can amplify outage costs.
• Deterministic provider status: Check provider fetch health before your nightly risk run, cutting false negatives when a government portal stalls.

Quick Start

Base URL and authentication:

• Base: https://energy-api.com/api/v1
• Auth: api_key as a query parameter (?api_key=YOUR_API_KEY)

Let’s fetch the latest electricity and fuel/carbon signals in one request. We’ll use Spain’s day-ahead electricity (OMIE), EU TTF gas, and EU ETS allowances—useful context when high prices and emissions intensity raise the stakes of outages during a storm window.

cURL:

curl -G https://energy-api.com/api/v1/latest \
--data-urlencode "symbols=OMIE_ES_DA,TTF_GAS,EUA_CO2" \
--data-urlencode "api_key=YOUR_API_KEY"

Example JSON (values are illustrative):

{
"success": true,
"date": "2026-06-11",
"base": "MIXED",
"rates": {
"OMIE_ES_DA": 93.12,
"TTF_GAS": 38.15,
"EUA_CO2": 67.40
},
"dates": {
"OMIE_ES_DA": "2026-06-11",
"TTF_GAS": "2026-06-11",
"EUA_CO2": "2026-06-11"
},
"currencies": {
"OMIE_ES_DA": "EUR",
"TTF_GAS": "EUR",
"EUA_CO2": "EUR"
}
}

What matters for outage prioritization:

• rates: numeric values you can weight in your risk function alongside weather and vegetation indices.
• dates: publication dates—useful for cache expiry and to avoid mixing stale energy data with fresh weather tiles.
• currencies: confirm consistent units (EUR/MWh for OMIE_ES_DA and TTF_GAS, EUR/MT for EUA_CO2) before normalization.

Core Endpoints

Below are four endpoints you’ll use to enrich weather-vegetation risk with market awareness. Each includes the path, key params, a working cURL, a JSON response that follows the documented schema, and a short explanation of the fields you’ll actually wire up.

1) Discover electricity symbols: GET /symbols

Path: /symbols

Purpose: Find electricity symbols you’ll align with operational territories (e.g., Spain OMIE, Germany EPEX). This lets you parameterize the pipeline per region in your weather service area.

Key params:

• category: electricity
• base: optional currency filter

cURL:

curl -G https://energy-api.com/api/v1/symbols \
--data-urlencode "category=electricity" \
--data-urlencode "api_key=YOUR_API_KEY"

Example JSON (truncated and illustrative):

{
"success": true,
"count": 3,
"symbols": [
{
"symbol": "OMIE_ES_DA",
"name": "OMIE Spain Day-Ahead",
"category": "electricity",
"country_code": "ES",
"currency_code": "EUR",
"frequency": "daily",
"description": "OMIE day-ahead auction price."
}
]
}

Use symbol, country_code, and frequency to select the right time horizon and to map market signals to the same ISO-2 geography as your weather polygons and vegetation corridors.

2) Pull latest multi-commodity signals: GET /latest

Path: /latest

Purpose: Grab the freshest electricity, gas, and carbon allowance metrics in one call to modulate outage risk when severe weather is imminent.

Key params:

• symbols: comma-separated list (e.g., OMIE_ES_DA,TTF_GAS,EUA_CO2)
• base: optional currency filter

cURL:

curl -G https://energy-api.com/api/v1/latest \
--data-urlencode "symbols=OMIE_ES_DA,TTF_GAS,EUA_CO2" \
--data-urlencode "api_key=YOUR_API_KEY"

Example JSON (illustrative):

{
"success": true,
"date": "2026-06-11",
"base": "MIXED",
"rates": {
"OMIE_ES_DA": 93.12,
"TTF_GAS": 38.15,
"EUA_CO2": 67.40
},
"dates": {
"OMIE_ES_DA": "2026-06-11",
"TTF_GAS": "2026-06-11",
"EUA_CO2": "2026-06-11"
},
"currencies": {
"OMIE_ES_DA": "EUR",
"TTF_GAS": "EUR",
"EUA_CO2": "EUR"
}
}

Wire rates.* directly into your risk model. Example: outage_risk_score = f(weather_gusts, vegetation_density, drought_index, price_factor(OMIE_ES_DA), emissions_factor(EUA_CO2)).

3) Anchor historical comparisons: GET /historical

Path: /historical

Purpose: During storm season, baseline the current price/emission regime against relevant historical analog days (e.g., last year’s heatwave week) and use that delta to upscale or downscale your crew pre-staging thresholds.

Key params:

• date: YYYY-MM-DD (if non-publishing, API returns the most recent value before it)
• symbols: comma-separated
• base: optional

cURL:

curl -G https://energy-api.com/api/v1/historical \
--data-urlencode "date=2025-09-15" \
--data-urlencode "symbols=OMIE_ES_DA,TTF_GAS,EUA_CO2" \
--data-urlencode "api_key=YOUR_API_KEY"

Example JSON (illustrative):

{
"success": true,
"date": "2025-09-15",
"base": "MIXED",
"rates": {
"OMIE_ES_DA": 101.25,
"TTF_GAS": 36.20,
"EUA_CO2": 64.10
},
"currencies": {
"OMIE_ES_DA": "EUR",
"TTF_GAS": "EUR",
"EUA_CO2": "EUR"
}
}

This lets you compute a simple context factor, e.g., price_context = current(OMIE_ES_DA) / historical(OMIE_ES_DA), which you can multiply by a weather severity index from your satellite ingestion.

4) Build trend-aware features: GET /timeseries

Path: /timeseries

Purpose: Extract a window of values to compute rolling volatility or moving averages. Time alignment with your weather forecast horizon is key when ranking feeders for patrols.

Key params:

• start: YYYY-MM-DD
• end: YYYY-MM-DD
• symbols: comma-separated (e.g., OMIE_ES_DA,TTF_GAS)
• base: optional

cURL:

curl -G https://energy-api.com/api/v1/timeseries \
--data-urlencode "start=2025-01-01" \
--data-urlencode "end=2025-03-31" \
--data-urlencode "symbols=OMIE_ES_DA,TTF_GAS" \
--data-urlencode "api_key=YOUR_API_KEY"

Example JSON (illustrative, truncated dates):

{
"success": true,
"base": "MIXED",
"start_date": "2025-01-01",
"end_date": "2025-03-31",
"rates": {
"OMIE_ES_DA": {
"2025-01-02": 76.30,
"2025-01-03": 75.90
},
"TTF_GAS": {
"2025-01-02": 46.80,
"2025-01-03": 47.10
}
},
"frequencies": {
"OMIE_ES_DA": "daily",
"TTF_GAS": "daily"
},
"currencies": {
"OMIE_ES_DA": "EUR",
"TTF_GAS": "EUR"
}
}

You’ll likely compute features such as 7-day z-scores, rolling standard deviation, and percentile ranks from the rates.* maps. These features can be multiplied by weather risk grids (wind, lightning density, soil moisture deficit) and vegetation layers (NDVI or canopy height) to produce circuit-level outage scores.

How to fuse satellite weather + vegetation with Energy API signals

This is the minimal glue layer you need to go from data to prioritized crew actions:

1. Weather ingestion: Pull your satellite-derived forecasts and observations (e.g., wind gust max, convective storm probability, cumulative precipitation, wet snow risk). Align to the next 24–72 hours per feeder polygon.
2. Vegetation risk: Ingest NDVI/NDMI and canopy density near conductors. Generate a vegetation stress index (e.g., drought x canopy density) normalized to 0–1 per span.
3. Energy API features: Fetch OMIE_ES_DA and TTF_GAS via /latest for “right now” scoring, and /timeseries for rolling context. Optionally pull EUA_CO2 if your ESG or emergency procurement policy requires emitting cost weights.
4. Score assembly: outage_score = w1*weather_risk + w2*vegetation_risk + w3*price_context + w4*volatility_context. Calibrate weights with historical outage tickets aligned to similar weather regimes.
5. Prioritization: Sort by score, break ties with criticality (e.g., hospital feeders), and export a crew routing list.

Because Energy API responses include dates and unified units, you can cache for the duration of each operational cycle and only refresh when new auction or publication timestamps land, reducing load and avoiding stale energy overlays on fresh weather tiles.

Code: Query timeseries and compute a simple context factor

Below is a short JavaScript example requesting a timeseries window and computing a 7-day average for OMIE day-ahead. You’ll use these features next to your satellite-derived weather severity indices.

<script>
// Note: run with node or in your backend. Values are illustrative.
const fetch = require('node-fetch');

async function fetchTimeseries() {
const url = new URL('https://energy-api.com/api/v1/timeseries');
url.searchParams.set('start', '2025-01-01');
url.searchParams.set('end', '2025-01-31');
url.searchParams.set('symbols', 'OMIE_ES_DA');
url.searchParams.set('api_key', 'YOUR_API_KEY');

const res = await fetch(url.toString());
if (!res.ok) throw new Error('Request failed: ' + res.status);
const data = await res.json();

const series = data.rates['OMIE_ES_DA']; // { "YYYY-MM-DD": value }
const dates = Object.keys(series).sort();
const values = dates.map(d => series[d]);

// 7-day simple moving average for the last available date
const N = 7;
if (values.length < N) return { sma7: null, last: values.at(-1) };
const last7 = values.slice(-N);
const sma7 = last7.reduce((a, b) => a + b, 0) / N;

return { sma7, last: values.at(-1), start: data.start_date, end: data.end_date };
}

fetchTimeseries()
.then(r => console.log('OMIE_ES_DA 7-day avg context:', r))
.catch(err => console.error(err));
</script>

Practical details that save time

• Units and normalization: OMIE_ES_DA and TTF_GAS are in EUR-based units (EUR/MWh for electricity, EUR/MWh for TTF as documented). EUA_CO2 is EUR/MT. Normalize to a unitless feature before combining with weather-vegetation scores.
• Timestamps and non-publishing days: If a requested date is a non-publishing day, /historical returns the most recent value before it. For nightly jobs, persist the dates field from /latest to detect freshness.
• Caching: Cache /latest responses alongside their dates for the length of your operational cycle (e.g., until the next auction result). This prevents running weather nowcasts against stale market signals.
• Batching: Use /latest with multiple symbols to cut latency and keep your risk computation synchronous with weather model windows.
• Error handling: Watch for HTTP 401 (missing or invalid api_key), 404 (no data for given date/symbols), 422 (validation errors), and 429 (rate limiting). Implement exponential back-off for 429.

Real-World Use Cases

• Storm pre-staging prioritization: Combine severe wind and ice accretion forecasts with /latest for OMIE_ES_DA and TTF_GAS to boost priority on feeders where an outage during peak price hours is most costly.
• Vegetation corridor patrol optimization: Cross NDVI-derived canopy density and drought stress against /timeseries volatility for OMIE_ES_DA to elevate spans where both failure probability and market impact are high.
• ESG-aware outage mitigation: When severe weather targets a high-emissions window, add EUA_CO2 from /latest and use /historical to baseline, nudging preventative switching and mobile generation toward minimizing emissions exposure.

FAQ

How often should I refresh Energy API data when running weather-driven outage models?

Use /latest for each operational cycle and refresh when its dates field changes. For day-ahead electricity, align refreshes with auction publications; for intraday adjustments, fetch as needed and cache based on the publication timestamp.

Can I query multiple commodities in one call to keep my weather pipeline simple?

Yes. With /latest you can request electricity, gas, and carbon allowance symbols together. This reduces orchestration complexity and keeps feature extraction synchronized with your weather-vegetation scoring window.

What happens if I request a non-publishing day while validating against past weather events?

/historical returns the most recent value before the requested date. Capture the returned date and store it with your weather archive to make comparisons explicit.

Do responses include currencies and frequencies so I can normalize features alongside weather values?

Yes. /symbols includes frequency and currency_code; /latest and /timeseries include currencies and, for timeseries, frequencies. Use these to standardize before mixing with satellite-derived indices.

How do I monitor data availability before kicking off a storm run?

Use the /status endpoint to check the last fetch status per provider. If a source lags, fall back to cached values while still running your weather-vegetation model.

Conclusion + CTA

Satellite weather and vegetation data tell you where the grid might break; Energy API tells you when it matters most. By adding a few compact market features—current price level, historical context, and simple volatility—you can rank circuits not just by failure probability, but by the operational and ESG consequence of that failure.

Use the examples above to wire /symbols, /latest, /historical, and /timeseries into your storm workflows. Keep your weather forecasts front and center, but let market-aware features sharpen the final sort order your crews depend on.

Energy API gives you one normalized JSON surface across electricity, gas, oil, coal, carbon allowances, and grid carbon intensity. Cut the integration work and focus on field impact. Try Energy API for free and ship your weather-aware outage prioritization this week.