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Data-Driven Virtual Replication of Thermostatically Controlled Domestic Heating Systems

Author

Listed:
  • 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
    Applied Physics Section of the Environmental Science Department, University of Lleida, Jaume II 69, 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)

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.

Suggested Citation

  • Gerard Mor & Jordi Cipriano & Eloi Gabaldon & Benedetto Grillone & Mariano Tur & Daniel Chemisana, 2021. "Data-Driven Virtual Replication of Thermostatically Controlled Domestic Heating Systems," Energies, MDPI, vol. 14(17), pages 1-25, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5430-:d:626856
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    References listed on IDEAS

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    1. Vincenzo Trovato & Antonio De Paola & Goran Strbac, 2020. "Distributed Control of Clustered Populations of Thermostatic Loads in Multi-Area Systems: A Mean Field Game Approach," Energies, MDPI, vol. 13(24), pages 1-26, December.
    2. Abdulrahman Alanezi & Kevin P. Hallinan & Rodwan Elhashmi, 2021. "Using Smart-WiFi Thermostat Data to Improve Prediction of Residential Energy Consumption and Estimation of Savings," Energies, MDPI, vol. 14(1), pages 1-16, January.
    3. Brabec, Marek & Konár, Ondrej & Pelikán, Emil & Malý, Marek, 2008. "A nonlinear mixed effects model for the prediction of natural gas consumption by individual customers," International Journal of Forecasting, Elsevier, vol. 24(4), pages 659-678.
    4. Scrucca, Luca, 2013. "GA: A Package for Genetic Algorithms in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 53(i04).
    5. Zhang, Xi & Strbac, Goran & Teng, Fei & Djapic, Predrag, 2018. "Economic assessment of alternative heat decarbonisation strategies through coordinated operation with electricity system – UK case study," Applied Energy, Elsevier, vol. 222(C), pages 79-91.
    6. Soldo, Božidar, 2012. "Forecasting natural gas consumption," Applied Energy, Elsevier, vol. 92(C), pages 26-37.
    7. Matteo Rivoire & Alessandro Casasso & Bruno Piga & Rajandrea Sethi, 2018. "Assessment of Energetic, Economic and Environmental Performance of Ground-Coupled Heat Pumps," Energies, MDPI, vol. 11(8), pages 1-23, July.
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    1. Rosa Francesca De Masi & Antonio Gigante & Valentino Festa & Silvia Ruggiero & Giuseppe Peter Vanoli, 2021. "Effect of HVAC’s Management on Indoor Thermo-Hygrometric Comfort and Energy Balance: In Situ Assessments on a Real nZEB," Energies, MDPI, vol. 14(21), pages 1-30, November.

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