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An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings

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  • Marco Massano

    (Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, 10129 Torino, Italy
    Energy Center Lab, Politecnico di Torino, 10129 Torino, Italy)

  • Edoardo Patti

    (Energy Center Lab, Politecnico di Torino, 10129 Torino, Italy
    Department of Control and Computer Engineering, Politecnico di Torino, 10129 Torino, Italy)

  • Enrico Macii

    (Interuniversity Department of Regional and Urban Studies and Planning, Politecnico di Torino, 10129 Torino, Italy)

  • Andrea Acquaviva

    (Department of Electrical, Electronic, and Information Engineering “Guglielmo Marconi”, Università di Bologna, 40126 Bologna, Italy)

  • Lorenzo Bottaccioli

    (Energy Center Lab, Politecnico di Torino, 10129 Torino, Italy
    Department of Control and Computer Engineering, Politecnico di Torino, 10129 Torino, Italy)

Abstract

Nearly 40% of primary energy consumption is related to the usage of energy in Buildings. Energy-related data such as indoor air temperature and power consumption of heating/cooling systems can be now collected due to the widespread diffusion of Internet-of-Things devices. Such energy data can be used (i) to train data-driven models than learn the thermal properties of buildings and (ii) to predict indoor temperature evolution. In this paper, we present a Grey-box model to estimate thermal dynamics in buildings based on Unscented Kalman Filter and thermal network representation. The proposed methodology has been applied in two different buildings with two different thermal network discretizations to test its accuracy in indoor air temperature prediction. Due to a lack of a real-world data sampled by Internet of Things (IoT) devices, a realistic data-set has been generated using the software Energy+ , by referring to real industrial building models. Results on synthetic and realistic data show the accuracy of the proposed methodology in predicting indoor temperature trends up to the next 24 h with a maximum error lower than 2 °C, considering one year of data with different weather conditions.

Suggested Citation

  • Marco Massano & Edoardo Patti & Enrico Macii & Andrea Acquaviva & Lorenzo Bottaccioli, 2020. "An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings," Energies, MDPI, vol. 13(8), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:8:p:2097-:d:349048
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    References listed on IDEAS

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    5. Ali Bagheri & Véronique Feldheim & Christos S. Ioakimidis, 2018. "On the Evolution and Application of the Thermal Network Method for Energy Assessments in Buildings," Energies, MDPI, vol. 11(4), pages 1-20, April.
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