A hybrid technique to improve measurement accuracy and reliability in AC systems

Abstract

Sensors are one of the key components in a modern building energy management system (BEMS). Accurate sensors are the prerequisite for the success of any building energy optimization strategy. As sensors are subject to environment disturbance and performance deterioration, their accuracy tends to decrease during their service lives. Sensor calibration is an efficient way to improve measurement accuracy and reliability. However, due to a large number of sensors installed in modern air conditioning (AC) systems, conventional regular calibration may be laborious while not optimal when the system energy performance are concerned. In order to improve measurement accuracy and reliability, a hybrid sensor management strategy is proposed in this article. This strategy integrates a measured variable importance ranking technique with a data fusion technique. Comparison of this strategy with a conventional regular calibration in case studies shows that this strategy improves both the energy and control performance of AC systems.