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A Dynamic Model for Indoor Temperature Prediction in Buildings

Author

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  • Petri Hietaharju

    (Control Engineering, University of Oulu, P.O. Box 4300, FI-90014 Oulu, Finland)

  • Mika Ruusunen

    (Control Engineering, University of Oulu, P.O. Box 4300, FI-90014 Oulu, Finland)

  • Kauko Leiviskä

    (Control Engineering, University of Oulu, P.O. Box 4300, FI-90014 Oulu, Finland)

Abstract

A novel dynamic model for the temperature inside buildings is presented, aiming to improve energy efficiency by providing predictive information on the heat demand. To analyse the performance and generalizability of the modelling approach, real measurement data was gathered from five different types of buildings. Easily available data from various sources was utilized. The chosen model structure leads to a minimal number of input variables and free parameters. Simulations with real data from five buildings, and applying the identical model structure showed that the average modelling error during the 28-h prediction horizon was constantly below 5%. The results thus demonstrate that the model structure can be standardized and easily applied to predict the indoor temperatures of large buildings. This would finally enable demand side management and the predictive optimization of the heat demand at city level.

Suggested Citation

  • Petri Hietaharju & Mika Ruusunen & Kauko Leiviskä, 2018. "A Dynamic Model for Indoor Temperature Prediction in Buildings," Energies, MDPI, vol. 11(6), pages 1-20, June.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1477-:d:151017
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