Pulsix Research Program
Data Centers, Grid, and Community Optimization
How can existing buildings, data centers, distributed batteries, and compute operate together more efficiently — while maintaining reliability and sharing value with owners and communities?
From roofs to coupled infrastructure
Observe the Texas data-center fleet
"The Invisible Tax" examined whether aging roof performance creates a cooling, water, and peak-demand burden that conventional planning overlooks — tracking roof geometry and nine years of satellite spectral history across a cohort of 403 Texas data centers.
Model the facility as a system
Two facilities with similar roofs can respond differently. Phase II links weather and envelope behavior to cooling demand, fans and pumps, heat rejection, electrical losses, facility power, and productive IT capacity — with carbon and water attached through explicit boundaries.
Replace assumptions with owner data
Equipment schedules and 60–90 days of read-only BAS/BMS history establish which configuration actually applies. Predictions are locked before an intervention, then compared with normalized measured results.
Learn across facilities
Repeated cases build a structured record — configuration, conditions, predicted and measured response, uncertainty — toward a hybrid physical-infrastructure intelligence model.
Every case is labeled by its evidence
Cases start with public information and become more specific as authorized owner data replaces assumptions. We never present a screening result as a verified one.
| Tier | Typical inputs | Appropriate outputs |
|---|---|---|
Public screening Screening | Satellite imagery, weather, public grid and price data, geometry, engineering references | Observable exterior state, configuration hypotheses, scenario ranges |
Owner-enriched Owner data required | As-builts, equipment schedules, maintenance history, tariffs, operating limits | Corrected topology, equipment-specific baselines, a targeted measurement plan |
Operationally calibrated Modeled | BAS/BMS, meters, IT load, battery or node telemetry | Normalized residuals, constraint-aware operating opportunities |
Intervention-validated Verified | Locked prediction, treatment event, controls, post-event measurements | Verified physical response, persistence, attributable value |
How a case works
- 01Register the site, systems, and owner
- 02Agree data rights, access, and publication scope
- 03Ingest public observations and authorized owner records
- 04Reconstruct the physical asset graph
- 05Build a versioned expected-state baseline
- 06Evaluate interventions and operating scenarios
- 07Rank opportunities by value, confidence, and risk
- 08Issue an approved scope or bounded recommendation
- 09Measure the result against the locked prediction
- 10Update the evidence, publish the approved case, monitor persistence
Research controls
- Every material result carries an evidence label; missing or stale sources stay visible.
- Predictions are locked before treatment and compared with normalized measurements.
- Equipment or control changes start a new operating regime instead of rewriting history.
- Negative results and corrected assumptions remain part of the record.
- Satellite correlations are treated as associations; savings claims require intervention evidence.
Featured Case Study
Public dataModeledCrusoe Abilene (Lancium Clean Campus)
Our principal example of moving from remotely observed exterior conditions to a whole-system model, built entirely from public data: satellite spectral and thermal observations, weather, ERCOT pricing, manufacturer baselines, and explicitly labeled configuration assumptions. A key methodological result: roof condition did not automatically become the highest-value intervention — screening scenarios for mechanical maintenance were far larger, and remain hypotheses until validated with operating data.
Pulsix is not affiliated with the campus owner or operator. Figures shown in the case study are public-data screening and modeled values, not measured facility performance. Additional Texas and Midwest facilities are under study.
Open the live case studyThe distributed energy and compute track
Beyond individual facilities, we're researching a community digital twin — homes, commercial buildings, batteries, compute nodes, and the campus interface — each with its own baseline, telemetry, constraints, and economic ledger. The sequence is home validation, a small multi-site pilot, then an aggregated community case. This work is R&D in progress.
Distributed compute
We're in discussion with a distributed solar-powered GPU network on workload requirements, measured node power, availability, and economics for pairing compute with suitable sites.
Residential energy storage
We're in discussion with a residential battery network operator on a household battery and fleet pathway for home-to-community flexibility.
Campus power & grid
We're in discussion with a gigawatt-scale campus power developer on campus-scale power orchestration, interconnection limits, and grid-coordination constraints.
Mechanical & water treatment
We're in discussion with an industrial water-treatment and mechanical specialist on cooling-loop chemistry and maintenance evidence for measured-facility pilots.
These collaborations are in active discussion; no agreements are formalized.