Electricity Theft Detection using Machine Learning
arXiv:1708.05907v114 citations
Originality Synthesis-oriented
AI Analysis
This addresses non-technical losses in power grids for suppliers and economies, but appears incremental.
The paper tackles electricity theft detection by improving feature extraction from data to enhance prediction quality, though no concrete numbers are provided.
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.