#### Literature Review
The idea behind transfer learning is that the knowledge acquired by training a predictor on an existing and fully labelled database can be used in another, but similar, with fewer or unlabelled samples. In the field of HVAC, some studies have been conducted to address the potential of transfer learning, mostly devoted to monitoring, supervision and control of the system by predicting temperature, humidity or energy consumption [20; 40; 41] to then adjust the control strategy accordingly. In the FDD field, only a few studies have been published using transfer learning techniques applied to chillers. For example, [19] applies domain adversarial neural network (DANN), a common technique used in domain adaptation based on minimizing the classification loss while maximizing the domain confusion, to solve the problem of cross-domain models for two screw chillers. Similarly, the authors of [23] use transfer component analysis (TCA) to adapt the feature space of both the source and target chillers to a more similar sample distribution. As it can be extracted from the extensive review on fault diagnosis cross-domain transfer learning carried out by [PERSON] et al. [42], while most of the transfer learning algorithms lean on using the similarity between the source and target domains (either indirectly or employing a discrepancy metric that helps to align the source and target datasets distributions), very few study how the dissimilarity of the samples fed to the model can affect its performance and in what measure can be improved if these samples are dropped before the actual training phase of the model. In addition, the majority of published studies assume and use datasets with high similarity between the source and target domains and apply transfer learning methodologies taking the focus out of the particularities of the application which, in the case of HVAC, is characterized by custom-made systems for each building so neither the dimensions of the equipment and the ducts, the energy requirements of the machines (fans, compressors,...), the building geometry nor the external variables (i.e., weather, control schedule, occupancy pattern,...) are necessarily similar. This study provides new insights on the importance of considering dissimilar datasets when dealing with transfer learning in HVAC related applications since it is in these situations where transfer learning methodologies struggle to achieve reliable results [21]. The proposed study considers a transfer learning approach, based on a discrepancy filter, applied to neural network algorithms for fault detection and diagnosis problems in HVAC machinery. It aims to deal with the low and imbalanced number of representative data by leveraging different installations data samples selected through the discrepancy filter. The rest of the paper is organized as follows. Section 2 describes the typical AHU system and exposes the common ways of obtaining a model for it. Then the methodology proposed is presented and derived step by step. Section 3 covers the results of the methodology when applied to a real case study consisting of two publicly available datasets, to verify its effectiveness. Section 4 discusses the results presented and finally, Section 5 summarizes this article and presents its conclusions.