Benford’s Law in Electric Distribution Network

Jaroslav Petráš (), Marek Pavlík, Ján Zbojovský, Ardian Hyseni and Jozef Dudiak
Additional contact information
Jaroslav Petráš: Department of Electric Power Engineering, Faculty of Electrical Engineering and Informatics, Technical University of Košice, 042 00 Košice-Sever, Slovakia
Marek Pavlík: Department of Electric Power Engineering, Faculty of Electrical Engineering and Informatics, Technical University of Košice, 042 00 Košice-Sever, Slovakia
Ján Zbojovský: Department of Electric Power Engineering, Faculty of Electrical Engineering and Informatics, Technical University of Košice, 042 00 Košice-Sever, Slovakia
Ardian Hyseni: Department of Electric Power Engineering, Faculty of Electrical Engineering and Informatics, Technical University of Košice, 042 00 Košice-Sever, Slovakia
Jozef Dudiak: Východoslovenská distribučná, a.s., Mlynská 31, 042 91 Košice, Slovakia

Mathematics, 2023, vol. 11, issue 18, 1-27

Abstract: Benford’s law can be used as a method to detect non-natural changes in data sets with certain properties; in our case, the dataset was collected from electricity metering devices. In this paper, we present a theoretical background behind this law. We applied Benford’s law first digit probability distribution test for electricity metering data sets acquired from smart electricity meters, i.e., the natural data of electricity consumption acquired during a specific time interval. We present the results of Benford’s law distribution for an original measured dataset with no artificial intervention and a set of results for different kinds of affected datasets created by simulated artificial intervention. Comparing these two dataset types with each other and with the theoretical probability distribution provided us the proof that with this kind of data, Benford’s law can be applied and that it can extract the dataset’s artificial manipulation markers. As presented in the results part of the article, non-affected datasets mostly have a deviation from BL theoretical probability values below 10%, rarely between 10% and 20%. On the other side, simulated affected datasets show deviations mostly above 20%, often approximately 70%, but rarely lower than 20%, and this only in the case of affecting a small part of the original dataset (10%), which represents only a small magnitude of intervention.

Keywords: Benford’s law; electric power engineering; electricity metering (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/18/3863/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/18/3863/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:18:p:3863-:d:1236723

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().