TLC Pointer – THE USE OF GEOSPATIAL DATA FOR NON TECHNICAL LOSSES DETECTION

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CIRED 2019 - 1798.pdf (267.82 KB)

Paper number

1798

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Conference name

CIRED 2019

Conference date

3-6 June 2019

Conference location

Madrid, Spain

Peer-reviewed

Yes

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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

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.

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Publisher

AIM

Date

2019-06-03

Published in

CIRED 2019 Conference

Permanent link to this record

https://cired-repository.org/handle/20.500.12455/580
http://dx.doi.org/10.34890/805

ISSN

2032-9644

ISBN

978-2-9602415-0-1