Estimating energy consumption in households for non-intrusive elderly monitoring
Authors
Hernández Alonso, Álvaro; Diego Otón, Laura De; Pizarro Pérez, Daniel; Pérez Rubio, María Del Carmen; Villadangos Carrizo, José Manuel; [et al.]Identifiers
Permanent link (URI): http://hdl.handle.net/10017/60391DOI: 10.1109/MetroLivEnv56897.2023.10164049
ISBN: 978-1-6654-5692-0
Publisher
IEEE
Date
2023-07-04Funders
Agencia Estatal de Investigación
Universidad de Alcalá
Bibliographic citation
Á. Hernández, L. de Diego, D. Pizarro, M. C. Pérez-Rubio, J. M. Villadangos and R. Nieto, "Estimating energy consumption in households for non-intrusive elderly monitoring" in 2023 IEEE International Workshop on Metrology for Living Environment (MetroLivEnv), Milano, Italy, 2023, pp. 191-195.
Keywords
Assisted Living
NILM techniques
LSTM
Description / Notes
2023 IEEE International Workshop on Metrology for Living Environment (MetroLivEnv), 29-31 May 2023, Milano, Italy.
Project
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-105470RA-C33/ES/MEJORANDO Y FOMENTANDO LA VIDA ACTIVA Y BIENESTAR DE LAS PERSONAS CON DEMENCIA Y DETERIORO COGNITIVO LEVE MEDIANTE EL USO DE TECNICAS DE LOCALIZACION/
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/TED2021-131773B-100
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-115995RB-I00/ES/TECNICAS DE APRENDIZAJE PARA RESOLVER LA RECONSTRUCCION Y REGISTRO DEFORMABLES APLICADOS A IMAGENES DE LAPAROSCOPIA/
info:eu-repo/grantAgreement/UAH//CM-JIN-2021-016
Document type
info:eu-repo/semantics/bookPart
Version
info:eu-repo/semantics/acceptedVersion
Publisher's version
https://doi.org/10.1109/MetroLivEnv56897.2023.10164049Rights
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
© 2023 IEEE
Access rights
info:eu-repo/semantics/openAccess
Abstract
Population ageing is becoming a key social issue
in recent decades, particularly in Western countries, where this
fact, together with the increase of life expectancy, has posed a
significant strain on public finances and health services. In this
context, many technological developments are often proposed to
promote and support the independent living of elderly at their
own homes, thus avoiding or postponing possible entries into
social residences. Among them, smart meters provide a nonintrusive
way to monitor and estimate the tenants" daily
activities, by only using a single-point measurement in the mains
at the entrance of the household. This work describes a
regression approach to estimate the energy consumption of a
house by means of a LSTM neural network. For that purpose, a
pilot has been run on a house during six months in order to
collect the electrical data, which will be used later to train the
neural network. After that training, the network tries to
estimate the energy consumption every 15 minutes, so any
deviation between the predicted sample and the measured one
might be used to detect anomalies in the daily routine of the tenant.
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