## 3 Results and Discussion
### Methodology Analysis
#### 3.2.2 Dissimilarity Filtering Comparison  
After obtaining the reference model, it is used to filter target samples that do not show a similarity with the already seen conditions. Here, the Algorithm 1 with a decision threshold of 50% is compared to the different uncertainty sampling techniques defined in Section 2.3. Table 2 contains a comparison between the proposed methodology, based on using the same model reference as a filter, entropy sampling, margin sampling and random sampling. In all the cases, the fault classes are balanced before feeding them to the model. The metrics chosen for the comparison are the F-score, the precision and the recall for the output obtained from the classifier on the target test dataset. For the case of random sampling, 200 data instances are selected randomly from the target training pool dataset and, as it can be seen, the performance obtained by using this sampling filter is rather poor having a precision and recall near to 70%. The rest of the uncertainty sampling techniques considered select a greater amount of data samples (around 3000) when compared to the proposed method (234 samples), and despite this difference, their performance is between 7% and 10% lower than the one for the proposed method. This drop in the performance can be explained by the common nature of the uncertainty sampling methods since this kind of approaches based on selecting the most informative samples for the model tend to choose the samples near to the model boundary decisions to increase the model's knowledge. To show this behaviour, a t-distributed stochastic neighbour embedding (t-SNE) representation of the whole training dataset (i.e., the source samples plus the selected target samples), has been depicted in Figure 10. This t-SNE map shows that the uncertainty sampling approaches select samples corresponding to a highly uncertain cluster of the target dataset, which does not fall over the distribution of the source domain. With these samples, the algorithms are selecting an unseen behaviour of the target machine, but as it does not fall on the already known region, for a transfer learning methodology applied to HVAC systems it is a probable cause of distortion for the already known space, hence con contributing to the negative transfer and leading to a performance decrease of the model. Figure 9: Confusion matrix for the target dataset FDD obtained with a model trained using only a few target samples (Target \(\rightarrow\) Target). The reference model is then also used as the starting point for the final model and a parameter transfer approach is used to fine-tune the model parameters with the target data instances. The samples selected for the fault conditions and the normal operation are balanced using SMOTE and added to the training dataset. The neural network is then trained for 300 epochs to achieve acceptable diagnosis accuracy. Figure 11 shows the confusion matrix after the transfer learning where the model predicts erroneously roughly 11% of F3 samples as F1. This is not a severe issue since, although the model fails in diagnosing which of the faults is occurring, it does not end up as a false negative and a warning is raised to the building administrator. It will trigger a maintenance action that could probably identify the misdiagnosis of the model. In addition, 15% of F1 samples are confused with the normal operation. This will raise false alarms that will reduce the administrator's trust in the model and the overall efficiency of the maintenance service.