HPL ELECTRIC & POWER

Limited | Gurugram, Haryana

Phase 2B: FluxAI

Advanced Analytics Platform

FUTURE ENGAGEMENT ← Back to Proposal
PHASE 2 - FUTURE ENGAGEMENT OVER & ABOVE RDSS
PHASE 2A Platform Enhancements
  • Field Operations Mobile App
  • Custom Report Builder
  • Multi-Language Support
  • CRM Integration
PHASE 2B 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 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
95%+
Forecast Accuracy
3x
Faster Theft Detection
Real-time
Anomaly Alerts

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 MDM

Phase 1 MDM
VEE + Data Store
→
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 Phase 2 Engagement

Prerequisites

  • Phase 1 Complete: Core MDM platform operational
  • Data Availability: Minimum 6 months historical data
  • Infrastructure: Cloud or GPU-enabled on-premise servers
  • Pilot Scope: Initial deployment for select feeders/DTs

Engagement Model

  • Timeline: To be determined post Phase 1
  • Investment: Separate commercial proposal
  • Approach: Pilot + Scale deployment
  • Support: Model retraining & optimization included

Interested in Phase 2? Let's discuss during the Phase 1 engagement.

Phase 2 scope and commercial terms will be finalized based on Phase 1 outcomes and HPL's requirements.

🔒 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/2026-02-P2  |  Classification: Confidential

🔒

Trinesis Technologies

Confidential Proposal