**3. Results**

## *3.1. ANN Selection and Training*

As explained earlier, 25 neural networks were trained and tested based on indoor and outdoor sampled data during the extreme winter and summer seasons to develop future predictions of indoor ambiences as a function of the daily weather. In particular, a GRN/PN network was selected as a dependent variable of the indoor partial vapour pressure and an independent variable for the outdoor partial vapour pressure.

As the frequency of 10 min gave about 80 samples per day during the unoccupied period, more than 1 week was needed to obtain a minimum training data of 300 samples. Finally, 75% of the data were employed to train the network and 25% were employed to validate the same.

The stopping criteria were the minimum absolute number of errors obtained in most of the indoor vapour pressure predictions. In particular, the maximum absolute error allowed was fixed in 6 during the training and 9 during the testing (standard deviation ±8%), which represents an actual nearly null percentage of incorrect predictions [25–28] as a clear example of the power of ANNs to model this process. Finally, all this training process required about 1 h per o ffice building, in a Hewlett Packard Intel i5-4200U computer.

## *3.2. Validation Results*

Like in past studies [6–10], extreme office buildings were analysed because their behaviour can be identified easily as a consequence of the real permeability level. As a consequence, indoor ambiences in these different offices were trained in the previous section and now simulated by the obtained ANNs models to be tested. Results showed a good agreemen<sup>t</sup> between the sampled and predicted curves of indoor partial vapour pressure as a function of outdoor weather conditions (outdoor partial vapour pressure), as reflected in Figures 3–6.

In such figures, more than 700 values of the outdoor weather conditions are employed to test the NNT and represented by a red line, its respective sampled indoor conditions by a blue line, and the predicted values obtained by the NNT are shown in a green line. On the horizontal axis, part of the number of samples of indoor and outdoor conditions with a time frequency of ten minutes are shown.

**Figure 3.** Partial vapour in office buildings using paper as an internal covering material.

**Figure 4.** Partial vapour in office buildings with wood as an internal covering material.

**Figure 5.** Partial vapour in office buildings with paint as an internal covering material.

**Figure 6.** Partial vapour in office buildings with plastic as an internal covering material.

## *3.3. Internal Coverings Behaviour Characterisation*

Once a neural net is trained for each of the interesting office buildings in accordance with a previous study—in which a testing laboratory bedroom was the subject of simulated outdoor weather conditions to analyse indoor ambience behaviour [4], in the present case study, partial vapour pressure was changed from 1200 to 1400 Pa and to 1000 Pa as outdoor weather conditions, and the predicted indoor conditions were compared with the effects obtained in the testing chamber. This procedure will allow one to understand the real behaviour of internal coverings in a transient process as it happens in real buildings and not in testing chambers.

Once each of the office building was modelled and tested with a neural network, past results obtained in [6–10] were applied to analyse the predicted values of internal vapour pressure for each office as a function of its internal covering effect. So, in Figures 7 and 8 it can be observed how the same sampled per hour weather conditions were employed as input data for each one neural network after trained. Because of this, a new output value for each different internal covering is represented in the unoccupied period (Figure 7) and occupied period (Figure 8). In this way, it can be observed in such figures the predicted effect over indoor ambiences partial vapour pressure of each internal covering under the same outdoor weather conditions.

**Figure 7.** Artificial neural networks (ANNs) prediction of indoor and outdoor partial vapour pressure during the unoccupied period.

**Figure 8.** ANNs prediction of indoor and outdoor partial vapour pressure during the occupied period.
