Play 71
Smart Energy Grid AI
Very High✅ Ready
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
Azure IoT HubAzure OpenAIStream AnalyticsAzure Digital TwinsAzure Functions
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
Demand prediction horizonRenewable source mixBattery storage scheduleGrid stability marginsPeak shaving thresholds
Estimated Cost
Dev/Test
$150–350/mo
Production
$5K–15K/mo