A Virtual Supply Airflow Rate Sensor Based on Original Equipment Manufacturer Data for Rooftop Air Conditioners

The supply airflow rate is crucial for monitoring, controlling, and detecting faults in rooftop air conditioner units (RTUs). However, the cost and intrusiveness of a supply airflow rate sensor (SARS) make it difficult to deploy in the field. Virtual SARSs have been proposed, but they often require testing or experimentation to train the model, which is not easily scalable. To overcome this limitation, the present study proposed deriving supply airflow using publicly available and scalable original equipment manufacturer (OEM) data of RTU blowers. Two models, the gray-box, and the black-box, were proposed using the OEM data and applied to data from four different manufacturers. Despite limited OEM data, the gray-box model showed an accuracy of ±5%, while the black-box model provided high overall accuracy for the full range of data but yielded low accuracy (up to 27% error) at a lower blower rotation speed. The models were also validated through laboratory testing, with an accuracy of ± 10% for the motor speed range of 50%-100% of the rated speed.