Artificial neural network models for indoor temperature prediction: investigations in two buildings

Authors:
Bertil Thomas;Mohsen Soleimani-Mohseni
Affiliations:
Department of Signals and Systems, Chalmers University of Technology, P.O. Box 8873, Campus Lindholmen, 402 72, Goteborg, Sweden;Department of Building Services Engineering, Chalmers University of Technology, P.O. Box 8873, Campus Lindholmen, 402 96, Goteborg, Sweden
Venue:
Neural Computing and Applications
Year:
2006

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Abstract

The problem how to identify prediction models of the indoor climate in buildings is discussed. Identification experiments have been carried out in two buildings and different models, such as linear ARX-, ARMAX- and BJ-models as well as non-linear artificial neural network models (ANN-models) of different orders, have been identified based on these experiments. In the models, many different input signals have been used, such as the outdoor and indoor temperature, heating power, wall temperatures, ventilation flow rate, time of day and sun radiation. For both buildings, it is shown that ANN-models give more accurate temperature predictions than linear models. For the first building, it is shown that a non-linear combination of sun radiation and time of day is important when predicting the indoor temperature. For the second building, it is shown that the indoor temperature is non-linearly dependent on the ventilation flow rate.