TPADL Theft Detection Engine v3.0 — Rule-based, interpretable, audit-ready
Aggregate Technical and Commercial (AT&C) losses represent the gap between energy a utility purchases and revenue it actually collects. India's national AT&C figure was 22.32% in FY 2020-21, appeared to decline to 15.41% by FY 2022-23, but reversed - the Power Finance Corporation's report for 2023-24 recorded a climb back to 16.12%. Accumulated sector losses stood at nearly ₹6,479 billion as of March 2023, an 11% increase year-over-year.
Commercial losses - the harder kind - are substantially driven by electricity theft. India loses an estimated $17 billion annually to non-technical losses (NTL), accounting for 30-40% of total generation. The global IEA average for comparable utility-level losses sits at around 7%. India is not a marginal outlier.
Traditional detection depends on reactive, field-based approaches: physical inspections triggered by complaints or random audits. There is no systematic mechanism to rank the full consumer base by theft probability. False-positive rates from complaint-driven inspections are high. The approach is expensive per confirmed case, and the fraction of actual theft caught is low. TPADL's estimated AT&C stands at approximately 18% - 3 percentage points above the RDSS national target of 15%.
The rationale of this study is to design a multi-layer, rule-based predictive detection framework that uses 12 months of smart meter data and MDI readings to identify consumers with high theft probability and generate a prioritized inspection list - entirely interpretable, field-auditable, and deployable without data science infrastructure.
12 months of monthly consumption (kWh) and MDI data, one record per consumer. Three rules run before any variable is computed.
Every consumer receives a Final Score from 0–100 by combining three weighted detection layers. All weights, thresholds, and formulas are explicitly documented and field-auditable.
Version 3.0 expanded from four to seven variables. Three new signals fill gaps the original CPI could not catch.
| Code | Weight | Variable | What it Catches |
|---|---|---|---|
| CTS | 20% | Consumption Trend Slope | OLS slope on (month, kWh) pairs. A sustained negative slope across 8–12 months suggests a bypass was installed and the meter has been recording less ever since. Category thresholds: Residential 20 | Commercial 80 | Industrial 250 kWh/month. |
| CVI | 20% | Consumption Volatility Index | Coefficient of Variation. Erratically jumping profiles - artificial spikes followed by near-zero months - are the fingerprint of partial bypass or on/off tampering. Score fires when CV ≥ 0.30. |
| DSR | 15% | Drop Severity Ratio | Gap between peak and trough valid months as % of peak. Catches cases CTS misses: V-shaped recoveries and non-linear declines. Fires when (Max – Min)/Max ≥ 40%. |
| ZSB | 10% | Z-Score Statistical Break | Single-month consumption cliff. Fires when minimum-Z month < –2.0 AND prior month was near-normal (Z ≥ –1.0). Targets the abrupt drop when a bypass is installed mid-year. |
| SCASnew | 15% | Summer Consumption Anomaly Score | Apr–Jul is Rajasthan's peak demand season (AC, coolers, refrigeration). A consumer whose summer mean is materially below their off-season mean is showing the exact pattern field teams associate with summer bypass. Threshold: <0.50 ratio for Residential/Commercial; <0.60 for Industrial. |
| FCSnew | 10% | Flat Consumption Score | Corrects a blind spot: near-zero CoV was being treated as safe. A perfectly flat meter reporting identical readings every month on a large connection is one of the clearest signs of a fixed bypass or tampered register. Fires only when Mean > 50 kWh. |
| PDSnew | 10% | Peer Deviation Score | Groups consumers by Rate Category + sanctioned load bracket (nearest 5 kW). Uses LOW-risk consumer median as the clean baseline. Scores consumers >20% below peer median. The only variable with an external reference point - catches cases where all individual signals look moderate but absolute consumption is anomalous. |
Four variables compare recorded consumption against what the connection's physical parameters imply. MDI (Maximum Demand Indicator) is the primary physical proxy.
| Code | Weight | Variable | Formula & Signal |
|---|---|---|---|
| LFR | 30% | Load Factor Ratio | Annual_LFR = MEAN(kWh_m / (Sanctioned_kW × Hours_m)). Flags consumers systematically below category floor: Residential 6% | Commercial 8% | Industrial 10%. A 50 kW connection at 1% LFR is either dormant or bypassed. |
| MOI | 25% | MDI Overdrawal Index | MDI_Ratio = Highest_MDI_kW / Sanctioned_Load_kW. Score fires above 1.05 (5% tolerance). Score=100 when MDI ≥ 2× sanctioned. A consumer whose consumption falls but MDI remains high is running load the meter is not recording. |
| MCGI | 30% | MDI-Consumption Gap Index | Most innovative variable. Expected_kWh = MDI_m × Hours_m × LF_typical (0.20 Res / 0.35 Com / 0.50 Ind). MCGI = mean gap between expected and actual kWh. Score fires above 20% gap; reaches 100 at 80% gap. Closest available proxy for V-I mismatch without electrical parameter data. |
| ZCSS | 15% | Zero Coverage Suspicion Score | ZCSS = (1 – Coverage_Ratio) × Load_Percentile × 100. Weights missing months by connection size. 8 missing months on a 1 kW household = low score. 8 missing months on a 50 kW industrial = high score. Separates genuine inactivity from suspicious gaps. |
One variable. A step function, not linear - the jump from zero to one violation is large. Beyond that, each additional violation adds progressively less because the consumer is already in the highest-risk tier.
| Violation Count | VHS Score | Interpretation |
|---|---|---|
| 0 | 0 | No recorded history. Score driven entirely by CPI and LCI. |
| 1 | 40 | One prior flag. Enough to push a medium-CPI consumer into HIGH territory. |
| 2 | 65 | Twice flagged. A known offender who has been penalised before. |
| 3 | 82 | Three violations. Systematic repeat offender. Treat as HIGH regardless of consumption patterns. |
| ≥ 4 | 100 | Maximum VHS. Any consumer at this level goes to the top of the inspection queue. |
Dataset context: 111,277 of 111,792 scored consumers (99.5%) carry zero prior violations. Only 515 consumers have any enforcement history (428 with one violation, 72 with two, 5 with three, 10 with four or more). Override O1 fired zero times because the joint condition - prior violations AND current CPI anomaly - requires a history that does not yet exist at scale. This finding validates the core argument: detection systems depending on historical violation records will systematically fail in Indian DISCOM contexts before systematic inspection coverage has been established.
TOP 10,000 priority consumers: combined 7.15 MU of estimated stolen energy and ₹5.25 Crore gross annual revenue loss. The highest-ranked consumer (Rank 1) is an MP-LT6 connection with 12 prior violations - estimated monthly theft of 1,607.8 kWh, gross annual loss ₹1,35,059, inspection ROI 2,601%.
All tariff rates sourced from AVVNL Tariff for Supply of Electricity 2023. Financial module uses peer-benchmark and AT&C pool methodologies. Recovery factor: 60%.
| Scenario | AT&C Target | Annual Saving | 5-Year NPV | 10-Year NPV | PAT Impact | ARR Improve |
|---|---|---|---|---|---|---|
| Conservative | 16.5% | ₹3.49 Cr | ₹13.25 Cr | ₹21.47 Cr | ₹2.62 Cr | ₹0.11/unit |
| Base RDSS TARGET | 15.0% | ₹6.99 Cr | ₹26.49 Cr | ₹42.94 Cr | ₹5.24 Cr | ₹0.21/unit |
| Optimistic | 13.0% | ₹11.65 Cr | ₹44.15 Cr | ₹71.57 Cr | ₹8.74 Cr | ₹0.36/unit |
| Best Case | 12.0% | ₹13.98 Cr | ₹52.98 Cr | ₹85.88 Cr | ₹10.48 Cr | ₹0.43/unit |
The framework deviates from existing studies in three specific, documented ways - not incremental adjustments but architectural choices driven by field-operational constraints.
Programme result summary: 111,792 consumers scored → 49,981 HIGH (44.71%) → ₹6.99 Crore annual saving under base scenario → 32.6% TPADL EBITDA improvement → ₹24.02 Crore net 10-year NPV → 2.91× BCR → payback in 8.68 months. Every sensitivity scenario across all sixteen parameter combinations tested produces positive NPV.