## 6 Conclusions  
The objective of this study was to develop a virtual sensor based on an LSTM-autoencoder to detect faults in a SCWT sensor that negatively impact HVAC systems. To achieve this, a simulation model (building, HVAC system, and SCWT sensor faults) was developed using EnergyPlus, and a virtual sensor was developed to detect faults in the SCWT sensor. Subsequently, a case study was conducted to evaluate the fault-detection performance and correction effects of the virtual sensor. The conclusions of this study are as follows. * The evaluation of the fault-detection performance of the virtual sensor showed excellent results for various fault types. The virtual sensor detected all faults for the transient fault types (Cases A-1 and A-2), with an F-1 score of 1.000. The virtual sensor detected the majority of faults for the continuously fixed fault types (Cases B-1 to B-4), with F-1 scores ranging from 0.9965 to 0.9997. However, the virtual sensor occasionally failed to detect faults with subtle offsets for continuous random offset faults (Case C) and faults with offsets that gradually increased or decreased over time (Cases D-1 and D-2), resulting in F-1 scores ranging from 0.9350 to 0.9846. * The evaluation of indoor thermal comfort following fault resolution revealed that the impact of sensor faults on indoor thermal comfort in types with transient and random offsets varied depending on the occurrence frequency and offset of the sensor faults. In the case of faults with continuously fixed offsets and offsets worsening over time, the indoor set-point temperature unmet hours increased by up to 425% for negative fault offsets. Consequently, indoor thermal comfort was adversely affected. * The evaluation of energy consumption following fault resolution revealed that the impact of sensor faults on energy consumption in types with transient faults and random offsets varied depending on the occurrence frequency and offset of the sensor faults. In the case of faults with continuously fixed offsets and those with offsets worsening over time, the chiller energy consumption decreased for faults with negative offset values, whereas the circulation pump and fan energy consumption increased. Conversely, for faults with positive offset values, the chiller energy consumption increased, whereas the circulation pump energy consumption decreased. The results of this study suggest the necessity of periodic sensor fault detection using a virtual sensor to simultaneously achieve indoor thermal comfort satisfaction and prevent excessive energy consumption. However, the virtual sensor developed in this study was trained based on simulation data. Validation with actual building data will be \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Offset Types** & **Equipment** & **Negative Offset** & **Positive Offset** \\ \hline \multirow{3}{*}{Point} & Chiller & & \\  & Circulation pump & & \\  & Fan & & \\ \hline \multirow{3}{*}{Fixed} & Chiller & Decrease & Increase \\  & Circulation pump & Increase & Decrease \\  & Fan & Increase & - \\ \hline \multirow{3}{*}{Random} & Chiller & & \\  & Circulation pump & & \\  & Fan & & \\ \hline \multirow{3}{*}{Drift} & Chiller & Decrease & Increase \\  & Circulation pump & Increase & Decrease \\  & Fan & Increase & - \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Effects on energy consumption for various fault types and offsets. required in the future to consider the applicability of the virtual sensor to an actual building. Additionally, comparisons and analyses with state-of-the-art models are also required as sensor fault-detection models continue to be developed. All authors contributed equally to this study. Conceptualization, S.J. and S.L.D. ; methodology, S.J. and S.L.D. ; formal analysis, S.J., A.J. and S.L.D. ; data curation, S.J., A.J. and S.L.D. ; writing--original draft preparation, S.J. and S.L.D. ; writing--review and editing, D.L., S.K., M.S. and S.L.D. ; visualization, S.J., A.J., D.L., S.K., M.S. and S.L.D. ; supervision, S.L.D.