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

Abstract

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. Monitoring and controlling the airflow rate in rooftop air conditioner units (RTUs) is essential, but traditional sensors for this purpose are costly and intrusive, making them challenging to use in the real world. To address this issue, researchers have proposed virtual sensors that estimate airflow without physical sensors, but these often require complex training processes that are not easily scalable. In this study, a novel approach is introduced. It leverages readily available data from RTU manufacturers (OEM data) to estimate airflow. Two models, known as the gray-box and the black-box models, are developed using this OEM data and tested on data from four different RTU manufacturers. The gray-box model, despite limited OEM data, achieves impressive accuracy within ±5%. The black-box model performs well overall but struggles with lower blower rotation speeds, resulting in up to a 27% error. To validate the models, laboratory tests were conducted, confirming an accuracy of ±10% for motor speeds ranging from 50% to 100% of the rated speed. This research offers a promising and cost-effective solution for accurately estimating supply airflow rates in RTUs, making it easier to monitor and control these systems efficiently.