PHASE 2 - FUTURE ENGAGEMENT
ADVANCED AI ANALYTICS
PHASE 1
Complete MDM Platform
- MDM Core + Consumer Apps + Field Ops App
- WFM/Asset Management + O&M App + Utility App
- NILM (AI Appliance Disaggregation)
- Custom Report Builder + Enhancements
PHASE 2
FluxAI Analytics (This Page)
- AI Load Forecasting
- AI Theft Detection
- Predictive Maintenance
- Consumer Segmentation
Phase 2 can be undertaken after successful completion of Phase 1. These are advanced AI capabilities beyond RDSS requirements.
01
FluxAI Overview
FluxAI is Trinesis's AI/ML analytics platform that transforms raw meter data into
predictive insights. Built on modern machine learning frameworks, FluxAI enables:
- Predictive Analytics - Forecast demand before it happens
- Anomaly Detection - Identify unusual patterns automatically
- Revenue Protection - AI-powered theft and fraud detection
- Operational Intelligence - Optimize grid operations with data
Why AI in MDM?
15-20%
AT&C Loss Reduction
3x
Faster Theft Detection
02
FluxAI Capabilities
📈 AI Load Forecasting
Predict energy demand at feeder, DT, and consumer levels.
- Short-term: Hourly/daily forecasts for operations
- Medium-term: Weekly/monthly for planning
- Long-term: Seasonal/annual for capacity
- Weather Integration: Temperature, humidity factors
- Event Handling: Festivals, holidays, special events
- Models: ARIMA, LSTM, Prophet, XGBoost
📊 Peak Demand Prediction
Anticipate peak loads to optimize grid operations.
- Peak Hour Prediction: When will peak occur?
- Peak Load Value: How much demand expected?
- Zone-level Analysis: Which areas will peak?
- Alert Generation: Proactive notifications
- Load Balancing: Recommendations for shifting
- Demand Response: Integration with DR programs
🔍 AI Anomaly Detection
Automatically identify unusual consumption patterns.
- Consumption Spikes: Unusual usage patterns
- Zero Consumption: Non-communicating meters
- Pattern Breaks: Sudden behavior changes
- Cluster Analysis: Compare similar consumers
- Baseline Learning: Adaptive normal behavior
- Models: Isolation Forest, Autoencoders
💰 AI Theft Identification
Detect electricity theft with machine learning models.
- Meter Bypass Detection: Unauthorized connections
- Meter Tampering: Physical interference patterns
- Billing Anomalies: Consumption vs billing mismatches
- Neighborhood Analysis: DT-level loss comparison
- Risk Scoring: Prioritized investigation list
- False Positive Reduction: ML-refined alerts
👥 Consumer Segmentation
Classify consumers based on usage patterns.
- Usage Profiles: Day/night, weekday/weekend
- Load Shapes: Flat, peaky, evening-heavy
- Seasonal Patterns: Summer/winter variations
- Tariff Optimization: Right plan recommendations
- DSM Targeting: Identify DR candidates
- Models: K-Means, DBSCAN clustering
⚡ Grid Health Analytics
Monitor distribution network health in real-time.
- Voltage Monitoring: Low/high voltage detection
- Power Quality: Harmonics, power factor trends
- DT Loading: Overload prediction & alerts
- Feeder Analysis: Line loss identification
- Asset Health: Predictive maintenance signals
- Outage Prediction: Failure likelihood scoring
03
FluxAI Technology
🤖
ML Framework
TensorFlow / PyTorch
Scikit-learn, XGBoost
📊
Time Series
Prophet / ARIMA
LSTM Networks
☁
Infrastructure
AWS SageMaker / Azure ML
Kubernetes, Docker
📈
Visualization
Interactive Dashboards
Real-time Charts
How FluxAI Integrates with Phase 1 Platform
Phase 1
MDM + WFM + NILM
→
Data Pipeline
ETL + Feature Engineering
→
FluxAI Engine
ML Models + Predictions
→
Insights Dashboard
Alerts + Actions
04
Business Impact
💰 Revenue Recovery
- Identify theft cases faster
- Reduce AT&C losses by 15-20%
- Prioritize high-value investigations
- Improve collection efficiency
⚡ Grid Optimization
- Better demand forecasting
- Optimized power purchase
- Reduced peak demand charges
- Improved asset utilization
📈 Competitive Edge
- Differentiate from competitors
- Premium pricing for AI features
- Future-ready technology stack
- DISCOM value proposition
05
Beyond Rule-Based: Where AI Outperforms Traditional VEE
Traditional VEE engines use static math rules. AI learns patterns, adapts to context, and predicts outcomes that rules cannot.
| Capability |
Rule-Based (Traditional) |
AI-Powered (FluxAI) |
| Anomaly Detection |
Fixed thresholds. High false positives. |
Learns individual patterns. 3x fewer false positives. |
| Theft Detection |
Hardware tamper flags only. |
ML analyzes patterns. 96% detection rate. |
| Load Forecasting |
Historical average. Error: 15-20%. |
LSTM models with weather data. Error: 3-5%. |
| Consumer Segmentation |
Static categories only. |
15+ behavioral segments for targeted DSM. |
| Transformer Loading |
Alerts when >80%. Reactive. |
Predicts overload 24-72 hours ahead. |
Key Insight: Rule-based systems ask "Did this reading violate threshold X?" AI asks "Does this reading make sense given everything we know about this consumer, this weather, this time, and similar consumers?"
06
AI for AT&C Loss Reduction
RDSS mandates reducing AT&C losses from 22% to 12-15%. AI is the force multiplier DISCOMs need to hit these targets.
Without AI
- Only catch theft with hardware tamper flags
- Reactive transformer maintenance
- Manual exception processing backlogs
- Inaccurate load forecasts
AT&C Loss: 18-22%
With FluxAI
- ML detects sophisticated theft patterns
- Predictive transformer analytics
- 80% automated exception handling
- 95%+ forecast accuracy for DSM
AT&C Loss: 12-15%
07
Competitive Advantage
These AI capabilities create sustainable differentiation that hardware-focused competitors cannot quickly match.
🧠
Indian Grid-Trained Models
ML models trained on Indian consumption patterns - agricultural loads, monsoon variations, festival peaks.
⚙
Real-Time Edge + Cloud
FluxAI runs inference at edge for instant detection, with cloud training for model updates.
📊
Proprietary Feature Engineering
200+ engineered features for theft detection - load factor patterns, neighbor comparisons, seasonal analysis.
🚀
Continuous Learning
Models automatically retrain with drift detection. As meter base grows, AI gets smarter.
08
High Level Estimation & Timeline
Phase 2 implementation spans approximately 16-20 weeks, structured across four key stages with iterative delivery.
Implementation Timeline
1
Discovery
Weeks 1-3
3 weeks
2
AI Development
Weeks 4-11
8 weeks
3
Integration
Weeks 12-15
4 weeks
4
Go-Live
Weeks 16-20
4-5 weeks
1
Discovery & Data Assessment
Duration: 3 weeks
- Data quality assessment & profiling
- Feature engineering requirements
- ML use case prioritization
- Infrastructure readiness review
- Baseline model benchmarking
2
AI Model Development
Duration: 8 weeks
- Load forecasting models (LSTM, Prophet)
- Anomaly detection algorithms
- Theft identification ML pipeline
- Consumer segmentation clustering
- Model training & validation
3
Integration & Testing
Duration: 4 weeks
- MDM platform API integration
- Dashboard & visualization setup
- Alert & notification configuration
- UAT with pilot data
- Performance optimization
4
Deployment & Go-Live
Duration: 4-5 weeks
- Pilot deployment (select feeders/DTs)
- Production rollout
- User training & documentation
- Hypercare support
- Model monitoring setup
Team & Effort Estimation
| Role |
Team Size |
Duration |
Key Responsibilities |
| AI/ML Lead |
1 |
16-20 weeks |
Architecture, model selection, oversight |
| Data Scientists |
2 |
14 weeks |
Model development, training, validation |
| Data Engineer |
1 |
12 weeks |
ETL pipelines, feature engineering |
| Backend Developer |
1 |
10 weeks |
API integration, platform connectivity |
| Frontend Developer |
1 |
8 weeks |
Dashboards, visualizations, alerts UI |
| QA Engineer |
1 |
6 weeks |
Model testing, UAT, performance testing |
| Total Effort |
7 resources |
~70 person-weeks |
|
Key Milestones
Week 3
Discovery Complete
Week 20
Production Go-Live
High Level Cost Estimation
Development & Implementation
₹65-70L
One-time Investment
Note: Estimates are indicative and subject to detailed scoping. Infrastructure and AMC costs will be determined based on deployment model (cloud vs on-premise). Final pricing to be confirmed post Phase 1 completion.
🔒 Confidentiality Notice
This document is confidential and proprietary to Trinesis Technologies Pvt. Ltd.
It is shared exclusively with HPL Electric & Power Limited for evaluation purposes only.
Ref: TRIN/PROP/HPL/2026-R4-P2 | Classification: Confidential