AI Analytics
Transforming Operational Data into Actionable Energy Intelligence
OpSys AI Analytics is a purpose-built machine learning platform for energy operations. It ingests time-series data from SCADA, meters, weather stations, and market feeds to deliver load forecasting, generation optimisation, fault prediction, and revenue assurance — all from a unified, explainable AI engine.
Key Capabilities
Load & Generation Forecasting
LSTM and transformer-based models deliver day-ahead and intra-day forecasts with MAPE < 2% for solar and < 3% for load.
Revenue Assurance
Automated detection of billing anomalies, meter tampering, and energy theft using unsupervised clustering.
Asset Health Scoring
Physics-informed ML models score transformer, cable, and switchgear health from operational telemetry.
Demand Response Optimisation
Reinforcement learning agent dispatches flexible loads and BESS to minimise peak demand charges.
Explainable AI (XAI)
SHAP-based explanations for every prediction ensure operator trust and regulatory auditability.
AutoML Pipeline
Automated feature engineering, model selection, and retraining triggered by data drift detection.
Why Choose OpSys AI Analytics?
- Improve renewable energy dispatch accuracy by 35% with AI-driven forecasting
- Recover 1–3% of annual revenue through automated billing anomaly detection
- Reduce transformer failure rate by 50% with predictive health scoring
- Cut peak demand charges by 15–25% through AI-optimised demand response
- Explainable AI ensures operator trust and satisfies regulatory requirements
- Continuous AutoML retraining adapts models to seasonal and operational changes
Technical Specifications
- Model Types
- LSTM, Transformer, XGBoost, Isolation Forest, RL (PPO)
- Data Ingestion
- OPC-UA, MQTT, REST, CSV, Modbus, IEC 61850 MMS
- Forecast Horizon
- 15 min to 30 days ahead
- Latency
- < 100ms inference for real-time anomaly detection
- Deployment
- On-premise GPU, edge (NVIDIA Jetson), or OpSys cloud
- MLOps
- MLflow experiment tracking, automated retraining, A/B testing