###### Abstract  
Heating, ventilation and air-conditioning (HVAC) systems are the major energy consumers among buildings' equipment. Reliable fault detection and diagnosis schemes can effectively reduce their energy consumption and maintenance costs. In this respect, data-driven approaches have shown impressive results, but their accuracy depends on the availability of representative data to train the models, which is not common in real applications. For this reason, transfer learning is attracting growing attention since it tackles the problem by leveraging the knowledge between datasets, increasing the representativeness of fault scenarios. However, to date, research on transfer learning for heating, ventilation and air-conditioning has mostly been focused on learning algorithmic, overlooking the importance of a proper domain similarity analysis over the available data. Thus, this study proposes the design of a transfer learning approach based on a specific data selection methodology to tackle dissimilarity issues. The procedure is supported by neural network models and the analysis of eventual prediction uncertainties resulting from the assessment of the target application samples. To verify the proposed methodology, it is applied to a semi-supervised transfer learning case study composed of two publicly available air-handling unit datasets containing some fault scenarios. Results emphasize the potential of the proposed domain dissimilarity analysis reaching a classification accuracy of 92% under a transfer learning framework, an increase of 37% in comparison to classical approaches. c 2022128837 2022128837 2022128837 202212837 2022129 August 20221222221  
HVAC systems' operation. Besides BEMS, the surge of IoT-enabled devices and smart sensors has contributed to the huge increment of data related to building services and their energy consumption. This massive digitalization is leading to advanced studies focused on optimization strategies, such as energy consumption forecast [5,6] or advanced control strategies [7,8] aiming to increase the energy efficiency of building HVAC systems. Additionally, the deployment of advanced maintenance strategies is considered one of the research lines with more potential impact on HVAC energy consumption [9]. Once a fault occurs in one of the HVAC sub-components, it leads to a failure or degradation of the system's performance to meet the setpoint requirements, and a significant portion of energy can be wasted continuously [10]. Indeed, without predictive maintenance strategies, such as fault detection and diagnosis (FDD) models, the system will evolve to critical damage to the HVAC equipment. In this regard, automated fault detection algorithms are expected to use the available data in BEMS to determine the actual HVAC condition and detect earlier possible deviations from the expected behaviours [11]. Automated FDD for HVAC systems has been extensively researched in the last few decades, obtaining reliable results and impressive accuracies when detecting and diagnosing faults. However, the availability of a large number of samples for both the normal and faulty operation regimes is a common requirement for these methodologies. Generally, current studies use specific and complete datasets where all the scenarios are well represented and very few articles can be found to address the issue of lack of representativeness of one or more faulty classes [9].