Non-technical electrical losses detection is a complex task, with high economic impact. Due to the diversity and large number of consumption records, it is very important to find an efficient automatic method to detect the largest number of frauds with the least amount of experts’ hours involved in preprocessing and inspections. This article analyzes the performance of a strategy based on a semisupervised method, that starting from a set of labeled data, extends this labels to unlabeled data, and then allows to detect new frauds at consumptions. Results show that the proposed framework, improves performance in terms of the Fmeasure against manual methods performed by experts and previous supervised methods, avoiding hours of experts/inspection labeling.
CITATION STYLE
Tacón, J., Melgarejo, D., Rodríguez, F., Lecumberry, F., & Fernández, A. (2014). Semisupervised approach to non technical losses detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8827, pp. 698–705). Springer Verlag. https://doi.org/10.1007/978-3-319-12568-8_85
Mendeley helps you to discover research relevant for your work.