Phase 2: FluxAI Advanced Analytics

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