Example: ERCOT Energy vs Dallas Weather
A real research investigation run on Lomita, demonstrating data discovery, agent chain execution, and statistical analysis.
The question
"Does ERCOT energy demand correlate with Dallas weather extremes?"
What happened
Time: ~5 minutes from question to completed report
Discovery Agent (30 seconds)
Searched the catalog and found:
ercot-public-api— ERCOT locational marginal pricing and grid load datanoaa-cdo-weather— Historical temperature and weather for Dallaseia-electricity-generation— Real-time electricity generation by fuel typeeia-steo-forecasts— Short-term energy outlook
Integration Engineer (2 minutes)
Built data pipelines for each source. Three Integration Engineers ran in parallel — each building a separate pipeline.
Quant Analyst (2 minutes)
Ran statistical analysis:
- Pearson correlation between temperature and ERCOT load
- Granger causality tests (does temperature predict demand?)
- Lag analysis (how many hours of lead time?)
- Regime analysis (does the relationship differ in summer vs winter?)
Research Narrator (1 minute)
Compiled findings into an executive report.
The verdict
SUPPORTED (Green)
Key findings:
- Strong positive correlation between temperature extremes and ERCOT load (r = 0.67)
- Bidirectional Granger causality (p < 0.01) — temperature predicts demand
- Strongest effect at 1-hour lag in summer (AC load)
- Winter heating demand also significant but weaker
What the graph shows
After this research completed, the knowledge graph showed:
- Hypothesis node (green) connected to 4 source nodes
- Entity nodes: "Dallas Temperature", "ERCOT Load", "ERCOT LZ_NORTH Price"
- CORRELATES_WITH edges with r-values between entities
- Cross-domain connections (weather → energy) visible in the graph layout
Delivery
The report was auto-delivered to email and manually delivered to Slack — both via the chat:
Deliver this report to [email protected]
Try it yourself
This exact research can be reproduced on any Lomita instance. Go to the Explore page and type:
Research whether ERCOT energy demand correlates with Dallas weather extremes