Detecting and diagnosing faults that degrade the performance of heating, ventilation, and air conditioning (HVAC) systems is very important for maintaining high energy efficiency. The performance of HVAC systems can be evaluated by analyzing monitored data. However, data from a HVAC system generally includes uncertainties, which renders monitored data less reliable. Then, we focused on uncertainties and a calculated performance distribution. The uncertainties from sensors, actuators, and communications were modelled stochastically and were incorporated into a detailed simulation. The system coefficient of performance (SCOP) was used as a performance indicator, which is defined as the ratio of suppled heat to total power consumption. The SCOP distributions over the course of representative weeks in 2007 and 2015 were calculated by repeating the simulation 2,000 times with different uncertainties. Regarding the results for 2015, the 90% confidence interval of the distribution was -4.9% to 5.8% from the SCOP value without uncertainties. The SCOP value determined from the monitored data in 2015 was outside of the low end of the distribution though that in 2007 was inside of the interval. Through an analysis of the monitored data, it was found that fault detection is possible by comparing the monitored data with the distribution.
Development and Implementation of Fault-Correction Algorithms in Fault Detection and Diagnostics Tools
