TLC Pointer – THE USE OF GEOSPATIAL DATA FOR NON TECHNICAL LOSSES DETECTION
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Paper number
1798
Working Group Number
Conference name
CIRED 2019
Conference date
3-6 June 2019
Conference location
Madrid, Spain
Peer-reviewed
Yes
Short title
Convener
Authors
Santi, Paolo, Senseable City Lab- Massachusetts Institute of Technology, USA
Zerbi, Massimo, Enel Global Infrastructures And Networks, Italy
Ratti, Carlo, Senseable City Lab Massachusetts Institute of Technology, USA
Tresoldi, Domenico, Enel Global Infrastructures And Networks, Italy
Papa, Carlo, Enel Foundation, Italy
Montesano, Giuseppe, Enel Foundation, Italy
Zerbi, Massimo, Enel Global Infrastructures And Networks, Italy
Ratti, Carlo, Senseable City Lab Massachusetts Institute of Technology, USA
Tresoldi, Domenico, Enel Global Infrastructures And Networks, Italy
Papa, Carlo, Enel Foundation, Italy
Montesano, Giuseppe, Enel Foundation, Italy
Abstract
Several machine-learning models specialized in providing revenue protection for power distribution companies can be found in the literature. However, traditional approaches present some limits: those models, relying solely on internal Company data, can be blind compared to some types of frequent anomalies, e.g. illegal connections, absence of consumption drops, etc. An innovative approach is the combination of proprietary data with third party data sets, preferably linked to geographical coordinates, thus representing a modeling of the territory. By integrating multiple sources of data, it is possible in principle to identify inconsistencies between activity patterns across data sets that would otherwise be impossible to identify by solely relying on proprietary data.In this paper, we instantiate this idea based on a combination of fine-grained smart meter consumption data with cellular phone data records. The rational for combining power consumption and cellular phone data is that both data sets have been proved to be good proxies of human activity. Hence, the identification of emergent activity patterns in the two data sets, and their spatio-temporal comparison, holds potential of substantially increasing the effectiveness of non-technical loss detection with respect to standard machine learning practices.
Table of content
Keywords
Publisher
AIM
Date
2019-06-03
Published in
Permanent link to this record
https://cired-repository.org/handle/20.500.12455/580
http://dx.doi.org/10.34890/805
http://dx.doi.org/10.34890/805
ISSN
2032-9644
ISBN
978-2-9602415-0-1