Build an Industrial Chiller Thermal Storage Pre-Cooling Optimizer

Build an application to monitor Sparkplug B chiller telemetry and determine optimal thermal storage pre-cooling windows. Avoid peak utility tariffs by calculating required refrigeration ramp-up times using thermal inertia constraints and outputting setpoint adjustment schedules.

Industrial Energy Systems Architect

Parse Sparkplug B telemetry payloads to extract chilled water loop temperatures and active factory cooling load demands.

Calculate the thermal mass energy required to reach the target setpoint using the configured energy-per-degree coefficient.

Model the required pre-cooling duration using Semantic Kernel to orchestrate the calculation against available chiller capacity.

Generate a precise schedule of automated setpoint adjustment start times designed to complete pre-cooling before peak tariff hours.

JSON API endpoint returning calculated pre-cooling start times and duration metrics.

Generate synthetic JSON records modeling Sparkplug B metric streams for chilled water supply temperatures and operational capacities. Starter examples below are synthetic. They are not downloaded records.

Eclipse Sparkplug Leverages Sparkplug B metric properties and complex data types from Source 2 to parse industrial thermal states.

semantic-kernel, pydantic, fastapi. Semantic Kernel orchestrates the integration of AI-driven scheduling heuristics with deterministic thermal storage calculation tools. Local or hosted LLM capable of function calling and strict JSON output 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.

Accurately calculates required pre-cooling duration using the formula: (delta_temp_c * energy_per_c_kwh) / available_cooling_kw.

Properly deducts the calculated pre-cooling duration from the peak tariff start hour to determine the optimal start command time.

Exports a structured JSON schedule containing the required cooling hours and the setpoint start time for the HVAC control system.

```json [ { "input": { "delta_temp_c": 4, "energy_per_c_kwh": 50, "available_cooling_kw": 100, "peak_tariff_start_hour": 15 }, "expected_output": { "required_precool_hours": 2, "start_command_hour": 13, "record_type": "simplified synthetic app record" } }, { "input": { "delta_temp_c": 3, "energy_per_c_kwh": 50, "available_cooling_kw": 100, "peak_tariff_start_hour": 14 }, "expected_output": { "required_precool_hours": 1.5, "start_command_hour": 12.5, "record_type": "simplified synthetic app record" } } ] ```

Read-only planning tool; all generated pre-cooling setpoints require manual review by plant facility engineers before being applied to live equipment. Use mock external writes. Require human review before any real deployment.