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Use of Machine Learning Methods for Indoor Temperature Forecasting

Author

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  • Lara Ramadan

    (Laboratoire Génie Civil et Géo-Environnement, University Lille, IMT Lille Douai, JUNIA Hauts de France, ULR 4515–LGCgE, 59000 Lille, France
    Modeling Center, Doctoral School of Science and Technology, Lebanese University, Hadath 99000, Lebanon)

  • Isam Shahrour

    (Laboratoire Génie Civil et Géo-Environnement, University Lille, IMT Lille Douai, JUNIA Hauts de France, ULR 4515–LGCgE, 59000 Lille, France)

  • Hussein Mroueh

    (Laboratoire Génie Civil et Géo-Environnement, University Lille, IMT Lille Douai, JUNIA Hauts de France, ULR 4515–LGCgE, 59000 Lille, France)

  • Fadi Hage Chehade

    (Modeling Center, Doctoral School of Science and Technology, Lebanese University, Hadath 99000, Lebanon)

Abstract

Improving the energy efficiency of the building sector has become an increasing concern in the world, given the alarming reports of greenhouse gas emissions. The management of building energy systems is considered an essential means for achieving this goal. Predicting indoor temperature constitutes a critical task for the management strategies of these systems. Several approaches have been developed for predicting indoor temperature. Determining the most effective has thus become a necessity. This paper contributes to this objective by comparing the ability of seven machine learning algorithms (ML) and the thermal gray box model to predict the indoor temperature of a closed room. The comparison was conducted on a set of data recorded in a room of the Laboratory of Civil Engineering and geo-Environment (LGCgE) at Lille University. The results showed that the best prediction was obtained with the artificial neural network (ANN) and extra trees regressor (ET) methods, which outperformed the thermal gray box model.

Suggested Citation

  • Lara Ramadan & Isam Shahrour & Hussein Mroueh & Fadi Hage Chehade, 2021. "Use of Machine Learning Methods for Indoor Temperature Forecasting," Future Internet, MDPI, vol. 13(10), pages 1-18, September.
    • Handle: RePEc:gam:jftint:v:13:y:2021:i:10:p:242-:d:641469
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    References listed on IDEAS

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    1. Juan Botero-Valencia & Luis Castano-Londono & David Marquez-Viloria, 2022. "Indoor Temperature and Relative Humidity Dataset of Controlled and Uncontrolled Environments," Data, MDPI, vol. 7(6), pages 1-15, June.
    2. Joanna Kajewska-Szkudlarek & Jan Bylicki & Justyna Stańczyk & Paweł Licznar, 2021. "Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems," Energies, MDPI, vol. 14(22), pages 1-15, November.

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