Search Results for author:
Found 4 papers, 1 papers with code
Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?
We apply it to a commercial data set from Brazil that consists of 3. 6M customers and 820K inspection results.
Neighborhood Features Help Detecting Non-Technical Losses in Big Data Sets
Using the neighborhood features instead of only analyzing the time series has resulted in appreciable results for Big Data sets for varying NTL proportions of 1%-90%.
The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey
Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science.
Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets
We believe that the considerations and observations made in this contribution are necessary for future smart meter research in order to report their effectiveness on imbalanced and large real world data sets.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
