A Dynamic Model for Indoor Temperature Prediction in Buildings

Petri Hietaharju, Mika Ruusunen and Kauko Leiviskä
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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

Energies, 2018, vol. 11, issue 6, 1-20

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.

Keywords: thermal modeling; indoor temperature prediction; cross-validation; parameter estimation; grey-box model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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