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.

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Efficiency factors are here defined as the thermal energy performance indicators of the space heating. Until recently, the efficiency factors were assumed as one value for space heating located in any climate. This study addresses the problem of how the outdoor climate affects the efficiency factors of a space heating equipped with 1D model of hydronic floor heating. The findings show how the efficiency factors, computed with two numerical methods, are correlated with the solar radiation. This study highlights the paradoxes in understanding the results of efficiency factors analysis. This work suggests how to interpret and use the efficiency factors as a benchmark performance indicator.