Limited | Gurugram, Haryana
MDM Platform | Appendix F
Sell AI analytics as premium add-on to DISCOMs beyond basic MDM
Proprietary ML models that competitors can't easily replicate
Address AT&C loss reduction mandate with proven AI tools
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 (e.g., >200% variation = alert). High false positives. | Learns individual consumer patterns. Adapts to seasonal, behavioral context. 3x fewer false positives. |
| Theft Detection | Tamper flags from meter hardware only. Catches obvious bypass. | ML analyzes consumption patterns to detect sophisticated theft. 96% detection rate with 3% false positive (XGBoost models). |
| Load Forecasting | Historical average + simple growth factor. Error margin: 15-20%. | LSTM/CNN models with weather, events, economic data. Error margin: 3-5%. |
| Data Estimation | Linear interpolation when data missing. | Context-aware estimation using similar consumers, weather, time-of-use patterns. |
| Consumer Segmentation | Static categories: Residential, Commercial, Industrial. | Dynamic clustering by actual usage patterns. 15+ behavioral segments for targeted DSM programs. |
| Transformer Loading | Alerts when >80% capacity. Reactive. | Predicts overload 24-72 hours ahead. Identifies degradation patterns. Prevents failures. |
| Billing Disputes | Manual review of each complaint. | Auto-validates bills against learned patterns. Flags genuine anomalies vs consumer gaming. |
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?"
Each use case represents a monetizable capability that goes beyond basic MDM functionality.
Machine learning models analyze consumption patterns at individual and feeder level to identify sophisticated theft that hardware tamper flags miss.
Aggregate smart meter data at transformer level to predict overloads, detect degradation, and optimize maintenance scheduling.
Deep learning models deliver feeder-level and system-level load forecasts with accuracy far beyond traditional statistical methods.
Cluster consumers by actual usage patterns (not just tariff category) to enable targeted demand-side management programs.
Disaggregate total meter reading into individual appliance consumption without additional sensors - enabling appliance-level insights from existing smart meters.
Reduce billing exceptions and consumer disputes through AI-powered validation that understands context, not just thresholds.
These AI capabilities create sustainable differentiation that hardware-focused competitors cannot quickly match.
Our ML models are trained on Indian consumption patterns - agricultural pump loads, monsoon variations, festival peaks, urban/rural differences. Global MDM vendors use Western-trained models that don't understand Indian grid behavior.
FluxAI runs inference at the edge (HES level) for instant anomaly detection, with cloud training for model updates. Competitors rely on batch processing that misses real-time theft and tampering events.
Years of utility domain expertise encoded into 200+ engineered features for theft detection - load factor patterns, time-of-use anomalies, neighbor comparisons, seasonal decomposition. This IP took years to develop.
Models automatically retrain on new data with drift detection. As DISCOM meter base grows, AI gets smarter. Competitors offer static models that degrade over time without expensive manual updates.
AI is embedded in VEE, analytics, and operations - not a bolt-on module. Competitors selling point solutions (just theft detection, just forecasting) can't deliver the compound value of integrated intelligence.
RDSS mandates reducing AT&C losses from 22% to 12-15%. AI is the force multiplier DISCOMs need to hit these targets.
| Technical Losses | Transformer loading optimization, capacitor bank placement recommendations | 1-2% reduction |
| Commercial Losses (Theft) | ML-based theft detection, bypass identification, meter tampering analytics | 3-5% reduction |
| Billing Inefficiency | Automated exception handling, accurate estimation, dispute reduction | 1-2% reduction |
| Collection Losses | Payment default prediction, targeted collection prioritization | 0.5-1% reduction |
Include AI analytics in MDM license at premium pricing. Position as "Intelligent MDM" vs competitor's basic MDM.
Sell AI capabilities as separate licensed modules: Theft Detection, Load Forecasting, Consumer Analytics.
Revenue share on recovered theft. HPL gets % of additional revenue collected through AI-identified cases.