Development of Virtual Sensor Based on LSTM-Autoencoder to Detect Faults in Supply Chilled Water Temperature Sensor

Abstract

Supply chilled water temperature (SCWT) is an important variable for the efficient and stable operation of heating, ventilation, and air conditioning (HVAC) systems. A precisely measured value ensured by the continuous reliability of the temperature sensor is essential for optimal control of an HVAC system because temperature sensor faults can affect the chiller operation and waste energy. Therefore, temperature sensor fault-detection strategies are imperative for maintaining a comfortable indoor thermal environment and ensuring the efficient and stable operation of HVAC systems. This study proposes a fault-detection method for an SCWT sensor using a virtual sensor based on a long short-term memory-autoencoder. The fault-detection performance is evaluated considering a case study under various sensor fault scenarios to evaluate changes in indoor thermal comfort and energy consumption after correcting sensor faults detected by the virtual sensor. The results verify excellent fault-detection performance in various fault scenarios (F-1 scores ranging from 0.9350 to 1.000). After correcting the SCWT fault, indoor thermal comfort is steadily maintained without additional energy consumption (indoor set-point temperature unmet hour reduced by a maximum of 105.7 hours, and energy consumption decreased by up to 1.8%).