12 citations · 26 across the 7 of their papers we have counts for
5 papers · 1 filter
Impact of Biases in Big Data
Patrick Glauner, Petko Valtchev, Radu State
The underlying paradigm of big data-driven machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at…
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…
The Top 10 Topics in Machine Learning Revisited: A Quantitative Meta-Study
Patrick Glauner, Manxing Du, Victor Paraschiv +5
Which topics of machine learning are most commonly addressed in research? This question was initially answered in 2007 by doing a qualitative survey among distinguished researchers…
Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?
Patrick Glauner, Angelo Migliosi, Jorge Meira +3
Non-technical losses (NTL) occur during the distribution of electricity in power grids and include, but are not limited to, electricity theft and faulty meters. In emerging countri…