Play 71
Smart Energy Grid AI
AI-driven energy demand prediction, renewable optimization, and grid balancing.
AI-driven energy management combining demand prediction, renewable source optimization, and real-time grid balancing. Azure Digital Twins simulates the grid topology, IoT Hub ingests smart meter and sensor data, Stream Analytics detects load anomalies, and OpenAI generates optimization recommendations for energy trading and storage decisions. Supports solar/wind integration with battery storage scheduling.
Architecture Pattern
Digital twin grid simulation: IoT telemetry → demand prediction → renewable optimization → grid balancing
Azure Services
DevKit (.github Agentic OS)
- agent.md — root orchestrator with builder→reviewer→tuner handoffs
- 3 agents — Grid Builder (gpt-4o), Reviewer (gpt-4o-mini), Tuner (gpt-4o-mini)
- 3 skills — deploy (185 lines), evaluate (133 lines), tune (231 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 — demand prediction and optimization prompts
- config/grid.json — grid topology, renewable sources, storage capacity
- config/guardrails.json — safety margins, reliability requirements
- evaluation/eval.py — Prediction accuracy >90%, Grid stability >99.9%
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.