Data-Driven Virtual Replication of Thermostatically Controlled Domestic Heating Systems

Gerard Mor, Jordi Cipriano, Eloi Gabaldon, Benedetto Grillone, Mariano Tur and Daniel Chemisana
Additional contact information
Gerard Mor: Building Energy and Environment Group, Centre Internacional de Mètodes Numèrics a l’Enginyeria, CIMNE-Lleida, Pere de Cabrera 16, Office 2G, 25001 Lleida, Spain
Jordi Cipriano: Building Energy and Environment Group, Centre Internacional de Mètodes Numèrics a l’Enginyeria, CIMNE-Lleida, Pere de Cabrera 16, Office 2G, 25001 Lleida, Spain
Eloi Gabaldon: Building Energy and Environment Group, Centre Internacional de Mètodes Numèrics a l’Enginyeria, CIMNE-Lleida, Pere de Cabrera 16, Office 2G, 25001 Lleida, Spain
Benedetto Grillone: Building Energy and Environment Group, Centre Internacional de Mètodes Numèrics a l’Enginyeria, GAIA Building (TR14), Rambla Sant Nebridi 22, 08222 Terrassa, Spain
Mariano Tur: BAXI.BDR-Thermea, Salvador Espriu, 9, 08908 L’Hospitalet de Llobregat, Spain
Daniel Chemisana: Applied Physics Section of the Environmental Science Department, University of Lleida, Jaume II 69, 25001 Lleida, Spain

Energies, 2021, vol. 14, issue 17, 1-25

Abstract: 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 simplified 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 1 °C and 2 °C, respectively.

Keywords: connected thermostats; forecasting; energy conservation; machine learning; residential buildings (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/17/5430/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/17/5430/ (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:14:y:2021:i:17:p:5430-:d:626856

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 ().