## 1 Introduction  
Heating and cooling energy consumption accounts for approximately 56.5% of the total energy consumption in commercial buildings in South Korea [1]. Furthermore, the global warming has resulted in a decrease in heating degree days and a gradual increase in cooling degree days. Consequently, the demand for cooling energy in commercial buildings is expected to gradually rise [2]. Therefore, it is essential to operate chillers in an energy-efficient manner because they account for the largest proportion of energy consumption in a cooling system. The energy consumption of a chiller is influenced by the return water temperature and supply chilled water temperature (SCWT). The return water temperature varies based on the mixed air temperature encountered by the cooling coil, which is influenced by outdoor and indoor loads. Thus, return water temperature has a limited direct correlation with the energy-efficient operation of a chiller. Conversely, a chiller assesses the satisfaction of a set point for the SCWT by measuring the actual SCWT. Consequently, the supply water temperature has a substantial impact on a chiller's efficiency because it determines the necessary work of the compressor. Accurate measurement of the SCWT is essential for achieving energy-efficient operation of a chiller. However, SCWT sensors are exposed to environmental factors such as temperature, humidity, and vibrations that can lead to sensor faults. Faults in the SCWT sensor not only reduce the chiller efficiency but also potentially cause thermal discomfort for occupants [3]. This arises solely from sensor faults and not from faults in the chiller. Therefore, it is crucial to detect sensor faults to maintain the accuracy of SCWT sensors. Detection methods for faults in physical sensors within heating, ventilation, and air conditioning (HVAC) systems, including the SCWT sensor, are categorized as rule-based modeling or data-driven methods [4]. The rule-based model detects sensor faults by explicitly assessing the conformity of the input data to predefined algorithms [5; 6; 7; 8]. Data-driven methods employ machine learning or artificial intelligence techniques to detect faults, and models are trained on fault patterns. Rule-based modeling is based on simple physics, with the advantage of being easy to understand. In addition, the relationship between the input and output data is straightforward. However, this model has limited flexibility that leads to lower detection rates for complex or novel data and fault patterns. This model also has disadvantages, such as high model update costs and challenges in automation. The data-driven method does not explain the internal workings of the model but has the advantages of abundant data availability and ease of model development. The data-driven method is more convenient for sensor fault detection than rule-based modeling, and research on fault-detection technology using data-driven methods is being conducted. [PERSON] et al. developed a sensor fault-detection and diagnostic method based on the principal component analysis (PCA) model [9]. This PCA model learned the correlation between the sensors installed within the cooling system and evaluated the square prediction error of the data to detect sensor faults. This model demonstrated excellent fault-detection performance through simulation tests, assuming four types of sensor faults. Another study argued that it was necessary to address sensor faults before detecting other faults in the overall HVAC system to ensure reliable fault detection [10]. Accordingly, a sensor fault-detection strategy based on PCA was introduced that exhibited the capability of detecting various sensor faults, achieving detection rates of 82% to 100%. [PERSON] et al. developed a Boltzmann-machine-based method for detecting faults in sensors within HVAC systems [11]. This approach involved assessing the occurrence of sensor faults by exchanging information between sensors.