Commercial Real Estate Energy & Space Optimization Pipeline
Smart commercial real estate demands active integration between occupancy sensors and HVAC energy automation. Build a dynamic space and energy optimization agent using Scikit-Learn and FastAPI to adjust building cooling based on real-time headcount predictions. Achieve reduced energy consumption while preserving environmental comfort scores.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an IoT HVAC energy optimization agent using Scikit-Learn and FastAPI for commercial office towers.
How work is evaluated
Evaluates predicted occupancy accuracy and HVAC setpoint recommendation validity.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
comfort_bounds_check
Ensures temperature setpoint remains strictly between 20.0C and 24.0C.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
forecast_mae
Mean Absolute Error of occupancy prediction model. • target: 1.2 • range: 0-5
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Train occupancy forecasting models using Scikit-Learn regression algorithms
Expose real-time dynamic temperature set-point endpoints using FastAPI
Balance indoor air quality (CO2 levels) against chiller energy draw
Implement automated alert triggers for HVAC maintenance anomalies
Reference links and supporting material
Synthetic commercial office space IoT dataset containing temperature, CO2, motion sensor readings, and energy draw.
How this agent runs
Evaluates predicted occupancy accuracy and HVAC setpoint recommendation validity.
Challenge input
JSON sensor snapshot
FastAPI
Standard high-performance Python web framework.
Evaluated output
JSON setpoint control signal
- Ensures temperature setpoint remains strictly between 20.0C and 24.0C.
- Mean Absolute Error of occupancy prediction model. • target: 1.2 • range: 0-5
- Benchmark: SmartBuilding-HVAC-Bench
- Forecast Mae target: 1.2
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Shakudo · Required
- AgentFlow · Optional
- Ray Tune · Optional
Evaluation contract
- comfort_bounds_check · Weight 1
- forecast_mae · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.