Customer Segmentation Based on the Electricity Demand Signature: The Andalusian Case

Andrés Camero, Gabriel Luque, Yesnier Bravo and Enrique Alba
Additional contact information
Andrés Camero: Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, 29071 Málaga, Spain
Gabriel Luque: Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, 29071 Málaga, Spain
Yesnier Bravo: Bettergy, Parque Tecnológico de Andalucía, 29590 Málaga, Spain
Enrique Alba: Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga, 29071 Málaga, Spain

Energies, 2018, vol. 11, issue 7, 1-14

Abstract: A smart meter enables electric utilities to get detailed insights into their customer needs, allowing them to offer tailored products and services, and to succeed in an increasingly competitive market. While in an ideal world companies would treat every customer as an individual, in practice this is rather difficult. For this reason, companies usually have to target smaller groups of customers that are similar. There are several ways of tackling this matter and finding the right approach is a key to success. Therefore, in this study we introduce the electricity demand signature, a novel approach to characterize and cluster electricity customers based on their demand habits. We test our proposal using the electricity demand of 64 buildings in Andalusia, Spain, and compare our results to the state-of-the-art. The results show that our proposal is useful for clustering customers in a meaningful way, and that it is an easy and friendly representation of the behavior of a customer that can be used for further analysis.

Keywords: clustering; load patterns; customer classes; evolutionary computation; feature selection; demand signature (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/7/1788/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/7/1788/ (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:jeners:v:11:y:2018:i:7:p:1788-:d:156810

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

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