Big-data mining-based identification of abnormal electricity consumption behavior for urban transport-energy distribution networks

Le résumé fourni par la source

Urban distribution networks that support residential areas, commercial districts and electric vehicle (EV) charging facilities generate large volumes of advanced metering infrastructure (AMI) data. Abnormal electricity consumption behavior, including meter bypassing, partial load scaling, flat-line reporting and false data injection, causes non-technical loss and can also disturb automated feeder operation. This paper proposes a big-data mining framework that combines chronological AMI cleaning, feeder-relative consistency analysis, temporal convolutional representation, gradient-boosted tabular learning and isolation-based anomaly scoring. The benchmark uses the UK Power Networks Low Carbon London dataset, which contains half-hourly electricity-use records from 5,567 Greater London households collected between November 2011 and February 2014. A fully specified 24-month interval from 1 March 2012 to 28 February 2014 is aggregated to hourly resolution and divided chronologically into 438 training days, 146 validation days and 146 test days. Six abnormal consumption templates are injected only after the split. Under this 60%-20%-20% protocol, the proposed model reaches an area under the receiver operating characteristic curve (AUC) of 0.957 and an F1-score of 0.873, outperforming logistic regression, random forest, extreme gradient boosting (XGBoost) and long short-term memory autoencoder (LSTM-AE) baselines. In a fixed 42-day holdout containing 5,567-meter identifiers and 5,611,536 expected hourly meter-time positions before quality filtering, the top-5% ranked list recovers 103 of 126 injected abnormal windows (81.7%). The framework therefore provides a reproducible decision-support approach for abnormal-consumption identification in urban low-voltage networks.

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