Build a Blower-Door Infiltration Delta Reporter

Process field blower-door ACH50 test data against EnergyPlus ZoneInfiltration elements. Compute the variance between empirical natural leakage and baseline epJSON assumptions to highlight necessary model adjustments.

Building Commissioning and Energy Modeler

Upload empirical blower door test results including structural dimensions.

Extract baseline ZoneInfiltration:DesignFlowRate estimates from project epJSON.

Compute actual natural infiltration flow rates using empirical formulas.

Output a delta variance report prioritizing zones that require calibration.

Calibration workbench presenting blower door curve charts, current versus calculated infiltration rates, and delta reports.

Utilize open-source DOE commercial reference building EnergyPlus epJSON files from the public NREL repository. Starter examples below are synthetic. They are not downloaded records.

EnergyPlus Extracts ZoneInfiltration:DesignFlowRate baseline data from the EnergyPlus epJSON schema for comparison against field tests.

dspy, scipy, pydantic. DSPy facilitates systematic logic structuring and schema-aligned reporting generation for epJSON infiltration comparisons. Hosted reasoning model capable of technical building physics reasoning and strict JSON generation.

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.

Converts ACH50 empirical figures to operational design flow rate (m3/s/m2) using natural infiltration heuristics.

Calculates variance delta percentages accurately against baseline epJSON flow parameters.

Produces valid structured JSON variance reports without directly mutating the source epJSON files.

```json [ { "input": { "building_zone": "Main_Office_Floor1", "zone_volume_m3": 3600, "envelope_area_m2": 1000, "measured_ach50": 5, "current_epjson_flow_per_exterior_area": 0.0002 }, "expected_output": { "building_zone": "Main_Office_Floor1", "measured_ach50": 5, "calculated_natural_leakage_m3_s_m2": 0.00025, "calibration_delta_pct": 25, "status": "CALIBRATION_REQUIRED", "note": "simplified synthetic app record" } }, { "input": { "building_zone": "Retail_Entry_Vestibule", "zone_volume_m3": 1440, "envelope_area_m2": 400, "measured_ach50": 2, "current_epjson_flow_per_exterior_area": 0.0001 }, "expected_output": { "building_zone": "Retail_Entry_Vestibule", "measured_ach50": 2, "calculated_natural_leakage_m3_s_m2": 0.0001, "calibration_delta_pct": 0, "status": "ACCEPTABLE_VARIANCE", "note": "simplified synthetic app record" } } ] ```

Data processing focuses on diagnostic reporting; model adjustment and subsequent simulation runs require manual validation by engineers. Use mock external writes. Require human review before any real deployment.