Play 83
Building Energy Optimizer
HVAC, lighting, and occupancy optimization via digital twin simulation.
HVAC, lighting, and occupancy optimization via digital twin simulation of commercial buildings. Reduces energy consumption 20-40% through AI-driven scheduling, predictive maintenance, and renewable integration. Digital Twins models building systems, IoT Hub connects HVAC and lighting sensors, OpenAI optimizes scheduling and generates energy reports, Functions execute automation rules, and Cosmos DB stores energy consumption records.
Architecture Pattern
Digital twin optimization: sensor data - simulation - AI scheduling - energy reduction
Azure Services
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Energy Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (194 lines), evaluate (122 lines), tune (245 lines)
- 4 prompts — /deploy, /test, /review, /evaluate with agent routing
- .vscode/mcp.json — FrootAI MCP with OpenAI + IoT Hub inputs + envFile
TuneKit (AI Config)
- config/openai.json - energy optimization and scheduling prompts
- config/energy.json - HVAC rules, occupancy models, renewable config
- config/guardrails.json - comfort thresholds, efficiency targets
- evaluation/eval.py - Energy reduction >20%, Comfort score >90%
Tuning Parameters
Machine evidence
FrootAI evidence lifecycle
This is an internal evidence maturity label, not third-party certification, accreditation, legal compliance, or a production guarantee. Missing or expired evidence demotes automatically; catalog claims cannot promote a play.
This play currently has design evidence only. A runnable scenario, endpoint evaluation, and build receipts are the next contiguous gates.
Repo Intelligence
v1A no-clone, revision-pinned map for agents and humans. Observed evidence is separated from inferred flow so the output stays useful without pretending to be a full call graph.