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Abstract: This paper presents a novel thermoelectric radiant air-conditioning (TE-RAC) system, which employs the thermoelectric modules as radiant panels instead of conventional hydronic panels. The novel radiant system brings a lot of benefits including Freon free, convenient installation, no complex water distribution pipes, quiet and reliable operation, etc. Switching between cooling and heating modes can be easily achieved by reversing the input current. The purpose of this study is to investigate the feasibility and performance of the novel TE-RAC system, including its cooling and heating performances, condensation risk, dynamic thermal response and inertia, and cost-effectiveness. A case study of the TE-RAC system is presented to compare the TE-RAC system with conventional air-conditioning (AC) systems and radiant air-conditioning (RAC) systems in terms of cooling and heating capacities, energy consumption and operation reliability. It is found that the COP of the TE-RAC system could be comparable to conventional RAC systems and AC systems, besides many other benefits in life-cycle performances. [Copyright &y& Elsevier]

• Propose feature-level CNN HVAC FD model interpretation method by ImLRP. • CNN model achieves 96.87% accuracy for classifying seven chiller faults. • Use new metric relevance difference to identify CNN chiller FD criteria and misdiagnosis. • Discuss influencing factors: CNN filter size, LRP parameter, relevance score introduction. Convolutional neural networks (CNNs) have been widely utili sed for fault diagnosis (FD) in building heating, ventilation, and air conditioning (HVAC) systems. Despite achieving high accuracy in many HVAC FD tasks, misdiagnosis still occurs. As a black-box model, the CNN FD model and its diagnostic mechanism and decision-making process are opaque, making it difficult for HVAC operators and managers to trust it. To address this, this study proposes an improved Layer-wise Relevance Propagation (ImLRP) method for interpreting CNN FD models in HVACs.The proposed method addresses the issue of preserving positive/negative information from HVAC inputs by adopting a Softsign activation function in the CNN. The feature-matching issue is addressed by excluding pooling layers from the CNN. ImLRP evaluates the contribution of each neuron in the network to the output decision by assigning a relevance score to each neuron in each layer during the backpropagation of the feedforward transmission process. The relevance score difference, a new metric, is used to obtain the net impact of HVAC faults. The proposed method was validated using RP-1043 chiller fault experiment data, which showed a CNN FD accuracy of 96%. Both correct-diagnosis and misdiagnosis were interpreted at the feature variable level, and the study also discussed the influence of the CNN model parameter, ImLRP parameter, and the relevance score difference on the results. [ABSTRACT FROM AUTHOR]