COLLIDE — AI-Powered BTM Data Center Site Selection

An AI siting platform that jointly scores candidate sites across land viability, gas supply reliability, and BTM power economics — surfacing a ranked set of locations with quantified risk, cost estimates, and sensitivity analysis.

Built for the ASU Energy Hackathon 2026.

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What it does

Hyperscale AI data centers need 50–500 MW of power 24/7. Grid interconnection queues now stretch 3–7 years. The alternative — behind-the-meter (BTM) natural gas generation — requires evaluating three interlocking constraints simultaneously:

• Sub-A: Land viability — zoning, fiber proximity, water access, parcel size, flood zone
• Sub-B: Gas supply reliability — pipeline failure probability, hub distance, curtailment risk
• Sub-C: Power economics — BTM spread (LMP minus generation cost), spread durability

COLLIDE scores every candidate site across all three dimensions in seconds, streams a composite scorecard with 20-year NPV estimates, and lets an AI analyst answer what-if questions in natural language.

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Tech stack

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ML models

Land viability — Random Forest Classifier

• 300 trees, max depth 8, trained on parcel attribute data
• Top features: highway access (28.5%), substation proximity (23.7%), pipeline distance (19.9%), water distance (14.6%), fiber proximity (8.1%)
• SHAP explainer included for per-site feature attribution

Gas reliability — GPU Gaussian KDE

• Bandwidth 0.5, trained on PHMSA pipeline incident coordinates (severity-weighted)
• Scoring: incident density 40% + interstate pipeline proximity 35% + Waha distance 25%
• Falls back to PHMSA density grid when PyTorch unavailable

Power durability — Random Forest Classifier

• 200 trees, max depth 6
• Top features: LMP level (68.4%), market regime encoding (30.9%), Waha price (0.8%)

Market regime — Gaussian Mixture Model

• 3 components, full covariance, trained on ERCOT 5-feature historical data
• Labels: normal (cluster 0), wind_curtailment (cluster 1), stress_scarcity (cluster 2)
• Training mean: LMP $78/MWh, wind penetration 31%, demand 54.9 GW

Composite score — TOPSIS

• Weights: land 30%, gas 35%, power 35% (user-adjustable)
• 20-year NPV via Monte Carlo (10,000 scenarios, 8% WACC, 100 MW plant)

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Features

• Interactive map — click any coordinate for a full scorecard; right-click for context menu
• Scorecard panel — land/gas/power breakdown, SHAP explanations, NPV P10/P50/P90, streaming AI narrative
• Compare mode — up to 5 sites side by side
• Grid optimizer — draw a bounding box, stream top-N candidates
• AI Analyst — LangGraph agent with 5 intents: stress test, compare, timing, explain, configure
• Live market data — Waha gas price, ERCOT + CAISO LMP, regime classification (updated every 5 min)
• 72-hour LMP forecast — Moirai model, P10/P50/P90 confidence bands
• Heat layers — composite / gas / LMP score overlays on the map

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Data sources

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Project structure

collide/
├── src/                    # React frontend
│   ├── components/         # 28 UI components
│   ├── hooks/              # 13 custom hooks (useEvaluate, useAgent, ...)
│   ├── docs/               # /docs/* documentation pages
│   └── data/               # Static site definitions
├── backend/
│   ├── scoring/            # Sub-A, Sub-B, Sub-C + TOPSIS + cost
│   ├── agent/              # LangGraph agent (graph.py, tools.py)
│   ├── features/           # Spatial feature extraction
│   ├── pipeline/           # FastAPI routes + background jobs
│   └── data/               # Site definitions + live cache
├── data/models/            # Trained pkl files (RF, GMM, KDE, SHAP)
├── ingestion/              # Data pipeline (see ingestion/README.md)
└── docs/                   # Problem statement + specs + plans

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Getting started

Frontend

npm install
npm run dev

Backend

python -m venv .venv
.venv/Scripts/activate        # Windows
pip install -r requirements.txt
npm run dev:api

Set environment variables in .env:

ANTHROPIC_API_KEY=sk-ant-...
TAVILY_API_KEY=tvly-...
GRIDSTATUS_API_KEY=...
EIA_API_KEY=...

Data pipeline

See ingestion/README.md for full setup. To run all sources:

cd ingestion
python -m ingestion.run

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API reference

Full schema reference: /docs/schema

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Documentation

Live docs are available at /docs in the app:

• Overview — what COLLIDE does and quick start
• Architecture — system layers, agent graph, data pipeline
• How It Works — ML models, weights, scoring pipeline
• Features — full feature guide
• Data — data sources and pipeline details
• Schema — API contracts, SSE events, data models

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Links

• GitHub
• Ingestion pipeline docs