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The Data-Driven Multi-Step Approach for Dynamic Estimation of Buildings’ Interior Temperature

Author

Listed:
  • Stefano Villa

    (Evogy srl, Via Pastrengo 9, 24068 Seriate, Italy)

  • Claudio Sassanelli

    (Department of Management, Economics and Industrial Engineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133 Milan, Italy)

Abstract

Buildings are among the main protagonists of the world’s growing energy consumption, employing up to 45%. Wide efforts have been directed to improve energy saving and reduce environmental impacts to attempt to address the objectives fixed by policymakers in the past years. Meanwhile, new approaches using Machine Learning regression models surged in the modeling and simulation research context. This research develops and proposes an innovative data-driven black box predictive model for estimating in a dynamic way the interior temperature of a building. Therefore, the rationale behind the approach has been chosen based on two steps. First, an investigation of the extant literature on the methods to be considered for tests has been conducted, shrinking the field of investigation to non-recursive multi-step approaches. Second, the results obtained on a pilot case using various Machine Learning regression models in the multi-step approach have been assessed, leading to the choice of the Support Vector Regression model. The prediction mean absolute error on the pilot case is 0.1 ± 0.2 °C when the offset from the prediction instant is 15 min and grows slowly for further future instants, up to 0.3 ± 0.8 °C for a prediction horizon of 8 h. In the end, the advantages and limitations of the new data-driven multi-step approach based on the Support Vector Regression model are provided. Relying only on data related to external weather, interior temperature and calendar, the proposed approach is promising to be applicable to any type of building without needing as input specific geometrical/physical characteristics.

Suggested Citation

  • Stefano Villa & Claudio Sassanelli, 2020. "The Data-Driven Multi-Step Approach for Dynamic Estimation of Buildings’ Interior Temperature," Energies, MDPI, vol. 13(24), pages 1-23, December.
  • Handle: RePEc:gam:jeners:v:13:y:2020:i:24:p:6654-:d:463572
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    References listed on IDEAS

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    Cited by:

    1. Rabia Charef & Jean-Claude Morel & Kambiz Rakhshan, 2021. "Barriers to Implementing the Circular Economy in the Construction Industry: A Critical Review," Sustainability, MDPI, vol. 13(23), pages 1-18, November.
    2. Idiano D’Adamo & Claudio Sassanelli, 2022. "Biomethane Community: A Research Agenda towards Sustainability," Sustainability, MDPI, vol. 14(8), pages 1-22, April.
    3. 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.
    4. Love Kumar & Ramna Kumari & Avinash Kumar & Imran Aziz Tunio & Claudio Sassanelli, 2023. "Water Quality Assessment and Monitoring in Pakistan: A Comprehensive Review," Sustainability, MDPI, vol. 15(7), pages 1-38, April.

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