Data-driven virtual replication of thermostatically controlled domestic heating systems

Thermostatic load control systems are widespread in many countries. Since they provide heat for domestic hot water and space heating on a massive scale in the residential sector, the assessment of their energy performance and the effect of different control strategies requires simpli-fied modeling t...

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Autores: Mor Martínez, Gerad, Cipriano, J., Gabaldon, E., Grillone, B., Tur, M., Chemisana, D.
Tipo de recurso: artículo
Fecha de publicación:2021
País:España
Institución:Consejo General de la Arquitectura Técnica de España (CGATE)
Repositorio:RIARTE
OAI Identifier:oai:www.riarte.es:20.500.12251/2535
Acceso en línea:http://hdl.handle.net/20.500.12251/2535
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114121992&doi=10.3390%2fen14175430&partnerID=40&md5=eb1986cf47775ef529405e8ac1fcb517
Access Level:acceso abierto
Palabra clave:Termostato
Calefacción
Edificación residencial
Agua Caliente Sanitaria (ACS)
Algoritmos
Consumo energético
Simulación energética - herramientas
Ahorro energético
Confort térmico
3305.14 Viviendas
3305.90 Transmisión de Calor en la Edificación
3322.01 Distribución de la Energía
3311.02 Ingeniería de Control
3311.18 Instrumentos Termoestáticos
3311.16 Instrumentos de Medida de la Temperatura
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repository_id_str
spelling Data-driven virtual replication of thermostatically controlled domestic heating systemsMor Martínez, GeradCipriano, J.Gabaldon, E.Grillone, B.Tur, M.Chemisana, D.TermostatoCalefacciónEdificación residencialAgua Caliente Sanitaria (ACS)AlgoritmosConsumo energéticoSimulación energética - herramientasAhorro energéticoConfort térmico3305.14 Viviendas3305.90 Transmisión de Calor en la Edificación3322.01 Distribución de la Energía3311.02 Ingeniería de Control3311.18 Instrumentos Termoestáticos3311.16 Instrumentos de Medida de la TemperaturaThermostatic load control systems are widespread in many countries. Since they provide heat for domestic hot water and space heating on a massive scale in the residential sector, the assessment of their energy performance and the effect of different control strategies requires simpli-fied modeling techniques demanding a small number of inputs and low computational resources. Data-driven techniques are envisaged as one of the best options to meet these constraints. This paper presents a novel methodology consisting of the combination of an optimization algorithm, two auto-regressive models and a control loop algorithm able to virtually replicate the control of thermostatically driven systems. This combined strategy includes all the thermostatically controlled modes governed by the set point temperature and enables automatic assessment of the energy consumption impact of multiple scenarios. The required inputs are limited to available historical readings from smart thermostats and external climate data sources. The methodology has been trained and validated with data sets coming from a selection of 11 smart thermostats, connected to gas boilers, placed in several households located in north-eastern Spain. Important conclusions of the research are that these techniques can estimate the temperature decay of households when the space heating is off as well as the energy consumption needed to reach the comfort conditions. The results of the research also show that estimated median energy savings of 18.1% and 36.5% can be achieved if the usual set point temperature schedule is lowered by 1â—‹ C and 2â—‹ C, respectively. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.MDPI2021info:eu-repo/semantics/articlehttp://hdl.handle.net/20.500.12251/2535https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114121992&doi=10.3390%2fen14175430&partnerID=40&md5=eb1986cf47775ef529405e8ac1fcb517reponame:RIARTEinstname:Consejo General de la Arquitectura Técnica de España (CGATE)Ingléshttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:www.riarte.es:20.500.12251/25352026-06-02T12:44:41Z
dc.title.none.fl_str_mv Data-driven virtual replication of thermostatically controlled domestic heating systems
title Data-driven virtual replication of thermostatically controlled domestic heating systems
spellingShingle Data-driven virtual replication of thermostatically controlled domestic heating systems
Mor Martínez, Gerad
Termostato
Calefacción
Edificación residencial
Agua Caliente Sanitaria (ACS)
Algoritmos
Consumo energético
Simulación energética - herramientas
Ahorro energético
Confort térmico
3305.14 Viviendas
3305.90 Transmisión de Calor en la Edificación
3322.01 Distribución de la Energía
3311.02 Ingeniería de Control
3311.18 Instrumentos Termoestáticos
3311.16 Instrumentos de Medida de la Temperatura
title_short Data-driven virtual replication of thermostatically controlled domestic heating systems
title_full Data-driven virtual replication of thermostatically controlled domestic heating systems
title_fullStr Data-driven virtual replication of thermostatically controlled domestic heating systems
title_full_unstemmed Data-driven virtual replication of thermostatically controlled domestic heating systems
title_sort Data-driven virtual replication of thermostatically controlled domestic heating systems
dc.creator.none.fl_str_mv Mor Martínez, Gerad
Cipriano, J.
Gabaldon, E.
Grillone, B.
Tur, M.
Chemisana, D.
author Mor Martínez, Gerad
author_facet Mor Martínez, Gerad
Cipriano, J.
Gabaldon, E.
Grillone, B.
Tur, M.
Chemisana, D.
author_role author
author2 Cipriano, J.
Gabaldon, E.
Grillone, B.
Tur, M.
Chemisana, D.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Termostato
Calefacción
Edificación residencial
Agua Caliente Sanitaria (ACS)
Algoritmos
Consumo energético
Simulación energética - herramientas
Ahorro energético
Confort térmico
3305.14 Viviendas
3305.90 Transmisión de Calor en la Edificación
3322.01 Distribución de la Energía
3311.02 Ingeniería de Control
3311.18 Instrumentos Termoestáticos
3311.16 Instrumentos de Medida de la Temperatura
topic Termostato
Calefacción
Edificación residencial
Agua Caliente Sanitaria (ACS)
Algoritmos
Consumo energético
Simulación energética - herramientas
Ahorro energético
Confort térmico
3305.14 Viviendas
3305.90 Transmisión de Calor en la Edificación
3322.01 Distribución de la Energía
3311.02 Ingeniería de Control
3311.18 Instrumentos Termoestáticos
3311.16 Instrumentos de Medida de la Temperatura
description Thermostatic load control systems are widespread in many countries. Since they provide heat for domestic hot water and space heating on a massive scale in the residential sector, the assessment of their energy performance and the effect of different control strategies requires simpli-fied modeling techniques demanding a small number of inputs and low computational resources. Data-driven techniques are envisaged as one of the best options to meet these constraints. This paper presents a novel methodology consisting of the combination of an optimization algorithm, two auto-regressive models and a control loop algorithm able to virtually replicate the control of thermostatically driven systems. This combined strategy includes all the thermostatically controlled modes governed by the set point temperature and enables automatic assessment of the energy consumption impact of multiple scenarios. The required inputs are limited to available historical readings from smart thermostats and external climate data sources. The methodology has been trained and validated with data sets coming from a selection of 11 smart thermostats, connected to gas boilers, placed in several households located in north-eastern Spain. Important conclusions of the research are that these techniques can estimate the temperature decay of households when the space heating is off as well as the energy consumption needed to reach the comfort conditions. The results of the research also show that estimated median energy savings of 18.1% and 36.5% can be achieved if the usual set point temperature schedule is lowered by 1â—‹ C and 2â—‹ C, respectively. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
publishDate 2021
dc.date.none.fl_str_mv 2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12251/2535
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114121992&doi=10.3390%2fen14175430&partnerID=40&md5=eb1986cf47775ef529405e8ac1fcb517
url http://hdl.handle.net/20.500.12251/2535
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114121992&doi=10.3390%2fen14175430&partnerID=40&md5=eb1986cf47775ef529405e8ac1fcb517
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:RIARTE
instname:Consejo General de la Arquitectura Técnica de España (CGATE)
instname_str Consejo General de la Arquitectura Técnica de España (CGATE)
reponame_str RIARTE
collection RIARTE
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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