234 citations · 244 across the 3 of their papers we have counts for
5 papers
On the Reduction of Biases in Big Data Sets for the Detection of Irregular Power Usage
Patrick Glauner, Radu State, Petko Valtchev +1
In machine learning, a bias occurs whenever training sets are not representative for the test data, which results in unreliable models. The most common biases in data are arguably…
Identifying Irregular Power Usage by Turning Predictions into Holographic Spatial Visualizations
Patrick Glauner, Niklas Dahringer, Oleksandr Puhachov +4
Power grids are critical infrastructure assets that face non-technical losses (NTL) such as electricity theft or faulty meters. NTL may range up to 40% of the total electricity dis…
Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets
Patrick Glauner, Jorge Meira, Lautaro Dolberg +4
Electricity theft is a major problem around the world in both developed and developing countries and may range up to 40% of the total electricity distributed. More generally, elect…
The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
Patrick Glauner, Jorge Augusto Meira, Petko Valtchev +2
Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineeri…
Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets
Patrick O. Glauner, Andre Boechat, Lautaro Dolberg +4
Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. De…