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On the distinctiveness of the electricity load profile

On the distinctiveness of the electricity load profile
On the distinctiveness of the electricity load profile
The recent increasing availability of fine-grained electrical consumption data allows the exploitation of Pattern Recognition techniques to characterize and analyse the behaviour of energy customers. The Pat- tern Recognition analysis is typically performed at group level, i.e. with the of discovering, via clustering techniques, groups of users with a coherent behaviour –this being useful, for example, for targeted pricing or collective energy purchasing. In this paper we took a step forward along this direction, inves- tigating the possibility of discriminating the behaviours of single users –i.e., in a biometrics sense. This aspect has not been properly addressed and would pave the way to crucial operations, such as the deriva- tion of alternative advertising schemes based on behavioural targeting. To investigate the uniqueness of the load profiles (i.e. the daily consumption of electrical energy), in our study we used the raw data (the original energy consumption time series) as well as different types of features such as frequency coeffi- cients and normalized load shape indexes, together with various classification schemes. Results obtained on two real world datasets suggest that the load profile does contain significant distinctive information about the single user.
0031-3203
317-325
Bicego, M.
5ce9ea10-73a1-47a1-bcce-9cc45ba588fa
Farinelli, A.
d2f26070-f403-4cae-b712-7097cb2e3fc6
Grosso, E.
19763d21-d4d3-46f9-b897-aeb21e86713a
Paolini, D.
b45a4c77-9b69-4f3e-9ea0-3bca2fcdbfa1
Ramchurn, S.D.
1d62ae2a-a498-444e-912d-a6082d3aaea3
Bicego, M.
5ce9ea10-73a1-47a1-bcce-9cc45ba588fa
Farinelli, A.
d2f26070-f403-4cae-b712-7097cb2e3fc6
Grosso, E.
19763d21-d4d3-46f9-b897-aeb21e86713a
Paolini, D.
b45a4c77-9b69-4f3e-9ea0-3bca2fcdbfa1
Ramchurn, S.D.
1d62ae2a-a498-444e-912d-a6082d3aaea3

Bicego, M., Farinelli, A., Grosso, E., Paolini, D. and Ramchurn, S.D. (2018) On the distinctiveness of the electricity load profile Pattern Recognition, 74, pp. 317-325. (doi:10.1016/j.patcog.2017.09.039).

Record type: Article

Abstract

The recent increasing availability of fine-grained electrical consumption data allows the exploitation of Pattern Recognition techniques to characterize and analyse the behaviour of energy customers. The Pat- tern Recognition analysis is typically performed at group level, i.e. with the of discovering, via clustering techniques, groups of users with a coherent behaviour –this being useful, for example, for targeted pricing or collective energy purchasing. In this paper we took a step forward along this direction, inves- tigating the possibility of discriminating the behaviours of single users –i.e., in a biometrics sense. This aspect has not been properly addressed and would pave the way to crucial operations, such as the deriva- tion of alternative advertising schemes based on behavioural targeting. To investigate the uniqueness of the load profiles (i.e. the daily consumption of electrical energy), in our study we used the raw data (the original energy consumption time series) as well as different types of features such as frequency coeffi- cients and normalized load shape indexes, together with various classification schemes. Results obtained on two real world datasets suggest that the load profile does contain significant distinctive information about the single user.

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More information

Accepted/In Press date: 24 September 2017
e-pub ahead of print date: 25 September 2017
Published date: February 2018

Identifiers

Local EPrints ID: 416618
URI: https://eprints.soton.ac.uk/id/eprint/416618
ISSN: 0031-3203
PURE UUID: 0beee82a-8229-4da4-a1d2-b5f2e0176b49
ORCID for S.D. Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302

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Date deposited: 03 Jan 2018 17:30
Last modified: 01 Feb 2018 17:31

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Contributors

Author: M. Bicego
Author: A. Farinelli
Author: E. Grosso
Author: D. Paolini
Author: S.D. Ramchurn ORCID iD

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