## 3 Results and Discussion
### Methodology Analysis
#### 3.2.1 Model's Overall Accuracy Validation  
First, an ANN-based reference model is built up using the source domain dataset for training purposes (i.e., ASHRAE RP-1312). The considered neural network based model is a simple structure consisting of 3 hidden layers with 150, 100 and 50 neurons that use a ReLu activation function. This structure configuration, that is, the number of layers, number of neurons and activation function, has been designed following common empirical procedures that can be found in the corresponding literature based on available data size and resulting performance [36].  
As a result of the data balancing step, the source model is trained using a total of 10,028 samples, being 4977 of them normal data, selected using the NearMiss algorithm. The rest of the samples are distributed evenly. The ANN model is trained during 100 epochs. The confusion matrix obtained for the fault diagnosis is shown in Figure 7, where the reference model shows a global accuracy of 99%, verifying that the source domain contains a good representation of the considered faults. The model can label correctly the healthy class (F0), raising a low number of false positives. The classification accuracy for the fault classes is also high, with only minor difficulties for the F2 class that has samples which are confused with the normal operation. That behaviour can be due to the seasonality dependence since F2 corresponds to a leak in the heating coil and the fault has been characterized during the cooling season when the temperature setpoints require cooling of the supply instead of heating it, hiding the possible effects of a fault in the heating system.  
Applying the model directly to the target installation will not lead to an acceptable performance since it does not have any information about the machine. Figure 8 shows the accuracy of the reference model when it is tested against the target domain dataset, following a direct FDD assessment. As it can be seen, numerous target samples fall on the classification region of F3, clearly showing the dissimilarities between both domains since a great part of the target space fall in the region previously defined as F3 faults during the training. This behaviour is somehow expected since the difference in the cooling system performance can fool the reference model into thinking that there is a fault in the system when in reality it is working under normal conditions.  
It should also be noted that the direct use of the training data collected on the target HVAC system as the only source of data is not enough to correctly model the target HVAC. The evaluation of the performance of a model with the same structure but trained using only normal operation data and 30 samples for each of the fault classes is depicted in Figure 9, show that whilst the F2 class seems easily diagnosed with not more than 30 samples, there is a severe overlapping between samples belonging to the F1 class and the normal operation, and a partial overlapping between the samples belonging to F3 class and normal operation.  
Figure 8: Confusion matrix for the target dataset FDD obtained with a model trained using only the source dataset samples and then applying the dissimilarity reduction step (Source \(\rightarrow\) Target).  
Figure 7: Confusion matrix for the source dataset FDD obtained with a model trained using only the source dataset samples (Source \(\rightarrow\) Source).