Play 83
Building Energy Optimizer
Very High✅ Ready
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
Azure Digital TwinsAzure IoT HubAzure OpenAIAzure FunctionsAzure Cosmos DB
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
HVAC scheduling rulesOccupancy prediction modelLighting automation profilesRenewable integration configComfort vs efficiency balance
Estimated Cost
Dev/Test
$120-300/mo
Production
$4K-12K/mo