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ENGINEERING JOURNAL

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RESEARCH & PROJECTS

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01Manuscript in preparation

Methodology study

Prepaid energy reconstruction

From irregular recharge records to a transparent estimate of monthly energy use.

Utility analytics
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Methodology study

Prepaid energy reconstruction

From irregular recharge records to a transparent estimate of monthly energy use.

Manuscript in preparation

Research question

How can monthly used energy be estimated when recharge transactions are available but independent meter readings are absent?

Approach

Combine personal rolling recharge patterns, tariff seasonality and an activity-weighted blend with a balance carry-forward simulation. Estimated usage is capped by available prepaid credit.

Contribution

An auditable reconstruction method for prepaid consumer analysis that distinguishes purchased energy from estimated energy use.

Evidence & limitations

Reconstructed kWh is an estimate, not metered ground truth. Opening-balance assumptions and small peer groups affect the result. No unreleased numerical results are presented here.

Next step

Validate against independent meter or balance observations and document sensitivity to initial balances.

Methodology stages

01 / Recharge history

02 / Seasonal target

03 / Balance simulation

04 / Estimated used kWh

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02Manuscript in preparation

Consumer analytics study

Hybrid utility analytics

A common analytical view of prepaid and postpaid electricity records, with evidence types kept visible.

Utility analytics
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Consumer analytics study

Hybrid utility analytics

A common analytical view of prepaid and postpaid electricity records, with evidence types kept visible.

Manuscript in preparation

Research question

How can a utility compare consumer behavior across prepaid and postpaid metering without treating recharges as measured consumption?

Approach

Construct a consumer-month panel, normalize feeder and tariff categories, label observed postpaid energy separately from reconstructed prepaid usage, and compare seasonal and peer-group behavior.

Contribution

A reproducible route from routine workbooks to consumption profiles and data-quality screening.

Evidence & limitations

Observed billing records and reconstructed prepaid values have different uncertainty. Entity collisions and aggregation need reconciliation. Behavioral patterns alone do not establish causal drivers.

Next step

Complete identity reconciliation and strengthen independent validation of reconstructed prepaid usage.

Methodology stages

01 / Billing & vending

02 / Panel & source labels

03 / Peer-group profiles

04 / Research screening

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03Manuscript in preparation

Unsupervised ML study

Prepaid anomaly screening

Unsupervised behavioral screening to prioritize records for verification.

Loss & anomaly detectionUtility analytics
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Unsupervised ML study

Prepaid anomaly screening

Unsupervised behavioral screening to prioritize records for verification.

Manuscript in preparation

Research question

Which unusual recharge and identity patterns warrant further examination when confirmed theft labels are unavailable?

Approach

Engineer behavioral and integrity features, combine Isolation Forest and Local Outlier Factor signals, and apply a separate rule-based identity and tariff consistency layer.

Contribution

An interpretable screening framework that can guide subsequent record review and field verification.

Evidence & limitations

An anomaly is not proof of theft, meter bypass or a cyberattack. Risk tiers need inspection-based validation; recharge-only records cannot diagnose a physical mechanism.

Next step

Link authorized inspection outcomes to model flags and evaluate precision, false positives and stability.

Methodology stages

01 / Behavioral features

02 / IF + LOF ensemble

03 / Integrity checks

04 / Verification priority

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TOOLS & TECHNOLOGY

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