Data Imputation in Electricity Consumption Profiles through Shape Modeling with Autoencoders

Oscar Duarte, Javier E. Duarte () and Javier Rosero-Garcia
Additional contact information
Oscar Duarte: Department of Electrical and Electronic Engineering, Faculty of Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia
Javier E. Duarte: EM&D Research Group, Department of Electrical and Electronic Engineering, Faculty of Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia
Javier Rosero-Garcia: EM&D Research Group, Department of Electrical and Electronic Engineering, Faculty of Engineering, Universidad Nacional de Colombia, Bogotá 111321, Colombia

Mathematics, 2024, vol. 12, issue 19, 1-19

Abstract: In this paper, we propose a novel methodology for estimating missing data in energy consumption datasets. Conventional data imputation methods are not suitable for these datasets, because they are time series with special characteristics and because, for some applications, it is quite important to preserve the shape of the daily energy profile. Our answer to this need is the use of autoencoders. First, we split the problem into two subproblems: how to estimate the total amount of daily energy, and how to estimate the shape of the daily energy profile. We encode the shape as a new feature that can be modeled and predicted using autoencoders. In this way, the problem of imputation of profile data are reduced to two relatively simple problems on which conventional methods can be applied. However, the two predictions are related, so special care should be taken when reconstructing the profile. We show that, as a result, our data imputation methodology produces plausible profiles where other methods fail. We tested it on a highly corrupted dataset, outperforming conventional methods by a factor of 3.7.

Keywords: data imputation; electricity consumption profiles; autoencoders; electrical profiles; smart meters; advanced metering infrastructure; synthetic profiles (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/19/3004/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/19/3004/ (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:12:y:2024:i:19:p:3004-:d:1486633

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 ().