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Linked challenge: Build an EnergyPlus HVAC Schedule Retrofit Planner
Format
Code-aware
Lines
65
Sections
11
Linked challenge
Build an EnergyPlus HVAC Schedule Retrofit Planner
Prompt source
Original prompt text with formatting preserved for inspection.
65 lines
11 sections
No variables
1 code block
## Build
Ingest baseline commercial building EnergyPlus epJSON models and adjust occupancy and HVAC availability schedules. Calculate equivalent operational hour reductions to decrease peak cooling demand.
## User
Building Energy Consultant
## Workflow
1. Parse baseline epJSON EnergyPlus models to extract Schedule:Compact and Schedule:Day:Interval objects.
2. Formulate retrofit occupancy schedules reflecting hybrid work occupancy patterns.
3. Synthesize revised epJSON schedule objects with matching hourly fractional multipliers.
4. Simulate operational schedule changes and output thermal comfort boundary checks.
## Interface
Schedule visualizer showing 24-hour profile comparison between baseline and proposed epJSON retrofit.
## Data
Create synthetic epJSON schedule snippets covering weekday and weekend Schedule:Compact blocks.
Starter examples below are synthetic. They are not downloaded records.
## Source
[EnergyPlus](https://raw.githubusercontent.com/NREL/EnergyPlus/develop/README.md)
Utilizes EnergyPlus epJSON input schema conventions for Schedule:Compact and Schedule:Day:Interval objects.
## Tools
dspy, jsonschema, python.
DSPy compiles robust prompt pipelines to transform variable operational requirements into valid epJSON schedule syntax.
Instruction-tuned LLM capable of complex JSON generation without syntax truncation.
## Deliverables
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.
## Acceptance
- Schedule fractional multipliers remain bounded between 0.0 and 1.0 inclusive.
- Modified schedules produce valid epJSON Schedule:Compact schema blocks.
- Weekly operating hours reduction metric is computed accurately.
## Starter Examples
```json
[
{
"input": {
"zone": "Office_Zone_A",
"baseline_daily_hours": 14,
"target_mode": "Hybrid_Workplace",
"schedule_name": "Office_HVAC_Avail"
},
"expected_output": {
"schedule_name": "Office_HVAC_Avail",
"proposed_daily_hours": 9.5,
"weekly_hours_saved": 22.5,
"epjson_valid": true,
"action": "UPDATE_EPJSON"
}
},
{
"input": {
"zone": "Data_Center_Zone",
"baseline_daily_hours": 24,
"target_mode": "Hybrid_Workplace",
"schedule_name": "IT_Cooling_Avail"
},
"expected_output": {
"schedule_name": "IT_Cooling_Avail",
"proposed_daily_hours": 24,
"weekly_hours_saved": 0,
"epjson_valid": true,
"action": "PRESERVE_CRITICAL_SCHEDULE"
}
}
]
```
## Safety
Simulation parameter generator only; changes must be reviewed by professional HVAC engineers before physical BMS deployment.
Use mock external writes. Require human review before any real deployment.Adaptation plan
Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.
Keep stable
Hold the task contract and output shape stable so generated implementations remain comparable.
Tune next
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Verify after
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.