Build a District Cooling Low Delta-T Network Telemetry Analyzer

Monitor real-time interval telemetry across customer branches to detect bypass leakage and low delta-T syndrome. Calculate parasitic pumping energy waste and trigger automated hydraulic inspection tickets.

District cooling customer services engineer

Ingest 15-minute interval energy meter telemetry including branch flow rates and return temperatures.

Identify sustained thermal deviations where delta-T drops significantly below network baselines.

Calculate parasitic pumping power overhead caused by excessive chilled water flow.

Generate diagnostic tickets targeting specific customer secondary pumps or two-way bypass valves.

Real-time telemetry scatter plot showing customer delta-T versus flow, highlighting hydraulic outliers and wasted pumping kilowatt-hours.

Generate synthetic 15-minute interval telemetry records for multiple commercial branches over 30 days, simulating normal load cycles and bypass leakage anomalies. Starter examples below are synthetic. They are not downloaded records.

Water Network Tool for Resilience Applies hydraulic network pressure-dependent flow calculations modeled via WNTR simulation concepts to analyze continuous flow metrics.

pydantic-ai, pandas, numpy. Guarantees strict type casting and validation on continuous telemetry streams and complex thermodynamic calculations. Local or hosted LLM with strong numerical reasoning for drafting technical diagnostic tickets based on time-series anomalies.

Submit runnable source, pinned dependencies, setup commands, a working interface, example outputs, and a recorded end-to-end demo. Include tests and an action log.

Identifies periods where branch delta-T falls below 8.0 degrees Celsius for more than 4 consecutive hours.

Computes excess flow and estimates parasitic pumping energy using hydraulic resistance equations.

Generates an actionable field diagnostic ticket for suspected bypass valve failure.

Preserves normal tracking for compliant buildings experiencing expected partial-load thermal drops.

```json [ { "input": { "branch_id": "Branch-A", "baseline_delta_t_c": 9, "current_delta_t_c": 4.5, "flow_rate_l_s": 150, "consecutive_hours_below_threshold": 5 }, "expected_output": { "anomaly_detected": true, "excess_flow_l_s": 75, "estimated_parasitic_kwh_per_hour": 55.2, "ticket_action": "dispatch_inspection" } }, { "input": { "branch_id": "Branch-B", "baseline_delta_t_c": 9, "current_delta_t_c": 8.5, "flow_rate_l_s": 120, "consecutive_hours_below_threshold": 0 }, "expected_output": { "anomaly_detected": false, "excess_flow_l_s": 0, "estimated_parasitic_kwh_per_hour": 0, "ticket_action": "monitor" } } ] ```

Produces advisory technical tickets. All maintenance dispatches must undergo engineering review before execution. Use mock external writes. Require human review before any real deployment.