HumanEnerDIA — AI-Powered Industrial Energy Management Platform

Problem

Industrial energy management is labor-intensive, technically demanding, and fragmented. Facility managers manually interpret complex energy data, navigate ISO 50001 regulatory requirements without guidance, and make optimization decisions blind — no voice interaction, no conversational assistance, no intelligent analytics. Existing EnMS platforms are built for engineers, not operators. I built a platform that makes industrial energy intelligence accessible through voice, chat, and visual dashboards.

My Role & Responsibilities

• Core Developer and System Integrator for the entire HumanEnerDIA platform
• Built and integrated the OVOS voice assistant — custom skills for hands-free energy data queries on the factory floor
• Implemented the RASA chatbot for text-based EnMS guidance, ISO 50001 workflow assistance, and compliance documentation
• Developed the FastAPI analytics backend with time-series aggregation, anomaly detection, ML model training, and forecasting
• Built 6 KPI dashboards with real-time metrics, radar charts, and trend analysis
• Implemented Sankey energy flow visualization — Grid → Factory → Departments → individual Machines
• Built ARIMA/Prophet forecasting with model performance tracking (R², RMSE, drift detection)
• Developed machine comparison analytics — identified 468.2% energy efficiency differences and $1,136 savings potential between machines
• Created 10 SOTA Grafana dashboards (Factory Overview, Executive Summary, Anomaly Detection, Real-Time Production, Energy Cost Analytics, Environmental Impact, ISO 50001 EnPI, Machine Health, Operational Efficiency, Predictive Analytics)
• Designed Node-RED data pipelines — MQTT → Parse → Route → Process → PostgreSQL
• Built the Factory Simulator REST API (Swagger/OpenAPI) for testing and development
• Integrated LLM capabilities for natural language energy report generation

Architecture

• Interface Layer: OVOS voice assistant, RASA chatbot, web dashboard, and 10 SOTA Grafana views
• Intelligence Layer: FastAPI analytics, ARIMA/Prophet forecasting, anomaly detection, Sankey flow engine, machine comparison, LLM reporting
• Pipeline Layer: Node-RED processing chain (MQTT → parse → route → process) and simulator API
• Data Layer: TimescaleDB with 19.2M+ readings and 138 baseline models
• IoT Layer: smart plugs, meters, and environmental sensors

Voice / Chat / Dashboard / Grafana
               ↓
        FastAPI Intelligence Core
               ↓
Node-RED Pipeline + Simulator + MQTT
               ↓
 TimescaleDB (readings + baseline models)
               ↓
     KPIs, forecasts, anomalies, reports

Tech Stack

• Voice AI: OVOS (Open Voice OS) — custom skills for hands-free energy queries
• Chatbot: RASA — domain-specific NLU for EnMS guidance and ISO 50001 compliance workflows
• Backend: FastAPI (Python) — analytics, ML training, anomaly detection, forecasting
• ML/Forecasting: ARIMA, Prophet — with R², RMSE tracking and drift detection
• LLM Integration: Natural language report generation and operator query handling
• Data Pipeline: Node-RED (MQTT → Parse → Route → Process → PostgreSQL), Factory Simulator API
• Database: TimescaleDB — time-series optimized PostgreSQL handling 19.2M+ readings
• Visualization: 10 Grafana SOTA dashboards + custom web analytics (Sankey diagrams, KPI charts, radar charts, heatmaps)
• Infrastructure: Docker, Docker-Compose
• Standards: ISO 50001 framework alignment

Platform Screenshots

Core Platform Views

Analytics & Grafana

Full Portal Capture

Results & Impact

• 19.2M energy readings processed across 8 active machines — 1.8M total kWh tracked over 90 days continuous operation
• 138 baseline ML models trained for energy pattern detection across all monitored equipment
• 468.2% energy efficiency gap identified between best and worst-performing machines — $1,136 annual savings potential surfaced automatically
• 10 SOTA Grafana dashboards — Factory Overview (222 kW live gauge), Executive Summary (56 MWh, $7K cost, 25,385 kg CO₂), Real-Time Production, Cost Analytics (€10.2K monthly, -23.7% variance, €2.44K savings), and 6 more
• Targets 30% reduction in technical effort through voice and chat interfaces that replace manual data interpretation
• ISO 50001 compliance support — chatbot provides guided workflows for documentation and audit preparation
• Human-centric design — reduced barrier to entry for facility operators who lack deep technical expertise in energy analytics

Challenges & Lessons Learned

• OVOS custom skill development — sparse documentation required reading source code, engaging with the community, and building deep understanding of the voice pipeline and intent handling system
• Balancing RASA and LLM approaches — used RASA for structured, deterministic workflows (ISO compliance steps) and LLMs for flexible natural language queries and report generation. The boundary between them required careful design.
• Time-series data volume at scale — 19.2M readings required TimescaleDB's chunking, compression, and continuous aggregates to maintain sub-second query performance
• ML model lifecycle — managing 138 baseline models with drift detection, retraining schedules, and performance monitoring (R², RMSE) across 8 machines simultaneously

How AI/Agents Were Used

LLM integration serves three purposes: (1) end-user facing — generating natural language energy reports and answering operator queries about consumption patterns; (2) ML pipeline — ARIMA/Prophet forecasting with automated drift detection and model retraining; (3) development acceleration — using agentic VS Code workflows to rapidly prototype FastAPI endpoints, Node-RED flow configurations, and Grafana dashboard JSON before manual validation and production hardening.