# Electricity Theft Detection using Machine Learning

arXiv: https://arxiv.org/abs/1708.05907 · HTML: https://mlsanity.com/paper/1708.05907/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2017.

## Built on by (most cited first, 1 of 1)

- [Evaluating Privacy-Utility Tradeoffs in Synthetic Smart Grid Data](https://mlsanity.com/paper/2506.11026/) (2025)

## Abstract (arXiv)

Non-technical losses (NTL) in electric power grids arise through electricity theft, broken electric meters or billing errors. They can harm the power supplier as well as the whole economy of a country through losses of up to 40% of the total power distribution. For NTL detection, researchers use artificial intelligence to analyse data. This work is about improving the extraction of more meaningful features from a data set. With these features, the prediction quality will increase.