AI-Based Energy Theft Detection

Inspiration

Power distribution networks frequently face severe revenue loss and grid instability caused by non-technical losses (NTLs) and unaccounted energy diversion. Traditional inspection methods rely on delayed, manual audits that are inefficient and fail to detect sophisticated, gradual consumption tampering. We built this platform to automate irregular consumption detection through scalable time-series anomaly modeling and explainable insights for utility operators.

What It Does

Automated Anomaly Scoring: Evaluates high-frequency smart meter consumption records to flag abnormal consumption behavior and potential theft patterns.
Explainable Analytics: Provides feature attribution breakdowns for flagged accounts, giving grid operators clear justifications behind every flagged anomaly.
Interactive Monitoring Dashboard: A full-stack operational dashboard featuring temporal consumption trends, risk heatmaps, and actionable inspection priority queues.

How We Built It

Data Pipeline: Preprocessed and normalized multi-meter time-series telemetry to account for temporal noise, missing readings, and seasonal baseline demand.
Analytical Engine: Implemented unsupervised machine learning algorithms tailored for multi-dimensional consumption profile modeling and outlier detection.
Explainability Layer: Integrated post-hoc model explainability techniques to map statistical deviations directly to human-interpretable risk metrics.
Full-Stack Application: Built a modular Flask REST backend to serve real-time inferences and integrated it with a responsive React frontend interface.

Challenges We Ran Into

Balancing anomaly sensitivity against false-positive rates caused by legitimate consumer behavior spikes.
Optimizing inference latency and feature aggregation over large, high-frequency temporal datasets.

Accomplishments & What's Next

Successfully designed an end-to-end prototype pipeline that translates raw time-series meter telemetry into prioritized utility inspection lists.
Future work involves integrating automated AMI alert pipelines, geospatial clustering for localized feeder monitoring, and multi-tenant utility role management.