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...
| Autores: | , , , , , |
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| 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 |
| Sumario: | 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. |
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