Powering AI Agents with Geothermal & Renewable Energy Data via Katzilla

Why Energy Data Belongs in Your AI Agent Stack

As of June 2026, the energy transition is generating more structured data than ever before — and most of it is scattered across dozens of siloed government portals, research archives, and utility databases. Katzilla aggregates 300+ free public data sources, including a rich collection of geothermal, wind, and electricity pricing datasets, so your AI agents can query all of it from one endpoint.

In this tutorial, we'll walk through three practical use cases using real datasets available on Katzilla today.

---

Dataset Spotlight: What's Available

Katzilla's energy category includes some genuinely powerful datasets:

• Brady's Geothermal Field — DTS and DAS Survey Metadata: Distributed Temperature Sensing and Distributed Acoustic Sensing data from one of the most instrumented geothermal sites in the western U.S. Perfect for subsurface modeling agents.
• Life Cycle Water Consumption for Utility-Scale Geothermal Systems: An in-depth analysis covering historical and forthcoming Enhanced Geothermal System (EGS) projects — ideal for sustainability scoring models.
• April 2014 Dixie Valley Binary Cycle Production Data: Granular plant-level production records useful as historical baselines when training forecasting models.
• Annual Average Electricity Price by State by Provider (1990–Latest): A longitudinal pricing dataset spanning over three decades, covering every U.S. state and provider type.
• Wind for Schools Wind Turbine Data: Real turbine performance records from educational installations across the country.

Together, these datasets let you build agents that reason across the full energy stack — from underground heat extraction to retail electricity pricing.

---

Use Case: Building a Geothermal Feasibility Agent

Imagine you're building an AI agent that evaluates geothermal project feasibility. It needs to cross-reference subsurface survey metadata, water consumption benchmarks, and regional electricity prices to produce a site score. With Katzilla, that's three API calls.

Step 1 — Install the Katzilla SDK

npm install @katzilla/sdk

Step 2 — Query the Datasets

import { KatzillaClient } from '@katzilla/sdk';

const client = new KatzillaClient({ apiKey: process.env.KATZILLA_API_KEY });

async function fetchGeothermalFeasibilityData(state = 'NV') {
  // 1. Pull Brady's Field DTS/DAS survey metadata
  const surveyMeta = await client.datasets
    .get('bradys-geothermal-field-dts-das-metadata')
    .fetch({ limit: 50 });

  // 2. Pull life cycle water consumption benchmarks for EGS projects
  const waterData = await client.datasets
    .get('lifecycle-water-consumption-geothermal-egs')
    .fetch({ projectType: 'EGS', limit: 20 });

  // 3. Pull electricity pricing for the target state
  const electricityPricing = await client.datasets
    .get('annual-avg-electricity-price-state-provider')
    .fetch({ state, yearFrom: 2020 });

  return {
    surveyMeta: surveyMeta.results,
    waterBenchmarks: waterData.results,
    pricingTrend: electricityPricing.results,
  };
}

fetchGeothermalFeasibilityData('NV').then((data) => {
  console.log('Feasibility inputs loaded:', JSON.stringify(data, null, 2));
});

Feed this structured output directly into your LLM agent's context window, and it can reason about whether a Nevada EGS project is economically viable given current grid pricing and known water constraints.

---

Combining Wind Data for a Broader Renewable Picture

Don't stop at geothermal. Append the Wind for Schools Turbine Data to give your agent a comparative baseline against small-scale wind installations:

const windData = await client.datasets
  .get('wind-for-schools-turbine-data')
  .fetch({ limit: 100, sortBy: 'capacity_kw', order: 'desc' });

Now your agent can benchmark geothermal output against wind performance data in the same geographic region — a multi-source analysis that would have taken weeks to assemble manually.

---

Why Katzilla Makes This Fast

Every dataset on Katzilla shares a consistent query interface — same pagination, same filtering syntax, same authentication. You're not writing custom scrapers or wrangling inconsistent CSV schemas. You write one pattern and reuse it across 300+ sources.

Head to katzilla.dev to browse the full energy catalog and grab your free API key. Your agents deserve better data infrastructure.