Abstract
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.
Highlights
Since heating, ventilation and air conditioning (HVAC) systems account for a large proportion of the total energy consumed in buildings, they must maintain high efficiency
We focused on not Fault Detection and Diagnosis (FDD) but fault detection because it is necessary to detect the presence of faults before applying FDD which locates faults
The 90% confidence interval was 4.69 to 5.20 even though system coefficient of performance (SCOP) value determined from the monitored data was 5.05
Summary
Ventilation and air conditioning (HVAC) systems account for a large proportion of the total energy consumed in buildings, they must maintain high efficiency. FDD methods are primarily classified into three types: abnormal detection from historical data, rule-based methods using expert knowledge, and model-based methods [1]. These methods have different features, they all utilize monitored data as input values. The uncertainties targeted in this research are inevitable due to limited equipment accuracy, and these uncertainties are different from the errors targeted in prior research [6] From this analysis, system performance considering the uncertainties was calculated using a detailed simulation, and a fault detection method using the performance is proposed in this paper. The performance distributions of the system were calculated from Monte Carlo simulations with different uncertainties
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
