Power and Energy Conference, Illinois 23-24 February 2017
Yes
No
International
Power and Energy Conference at Illinois 2017
from 23-02-2017 to 24-02-2017
[en] Artificial intelligence ; big data ; electricity theft ; feature engineering ; mechine learning ; non-technical losses
[en] Non-technical losses (NTL) in electricity distribution are caused by different reasons, such as poor equipment maintenance, broken meters or electricity theft. NTL occurs especially but not exclusively in emerging countries. Developed countries, even though usually in smaller amounts, have to deal with NTL issues as well. In these countries the estimated annual losses are up to six billion USD. These facts have directed the focus of our work to the NTL detection. Our approach is composed of two steps: 1) We compute several features and combine them in sets characterized by four criteria: temporal, locality, similarity and infrastructure. 2) We then use the sets of features to train three machine learning classifiers: random forest, logistic regression and support vector vachine. Our hypothesis is that features derived only from provider-independent data are adequate for an accurate detection of non-technical losses.
[University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
