Abstract
The energy efficiency of the building HVAC systems can be improved when faults in the running system are known. To this day, there are no cost-efficient, automatic methods that detect faults of the building HVAC systems to a satisfactory degree. This study induces a new method for fault detection that can replace a graphical, user-subjective evaluation of a building data measured on site with an automatic, data-based approach. This method can be a step towards cost-effective monitoring. For this research, the data from a detailed simulation of a residential case study house was used to compare a faultless operation of a building with a faulty operation. We argue that one can detect faults by analysing the properties of residuals of the prediction to the actual data. A machine learning model and an ARX model predict the building operation, and the method employs various statistical tests such as the Sign Test, the Turning Point Test, the Box-Pierce Test and the Bartels-Rank Test. The results show that the amount of data, the type and density of system faults significantly affect the accuracy of the prediction of faults. It became apparent that the challenge is to find a decision rule for the best combination of statistical tests on residuals to predict a fault.
Highlights
In the effort to fight global warming, one of the goals of the german government is to achieve a climate-neutral building stock by 2050
This study focuses on the energy efficiency of HVAC installations
The fault detection is carried out using residual analysis, model checking based on residual analysis is a standard technique for time series analysis, cf. [4], page 175 ff. and [5], page 360 ff
Summary
In the effort to fight global warming, one of the goals of the german government is to achieve a climate-neutral building stock by 2050. With the use of on-site measurement data, the actual energy consumption can be detected and flaws in energy efficiency identified. There are two methods for fault detection, measurement can be performed on-site and faults detected afterwards by analysing the data, or the faults are detected in real-time during operation and reported directly to the building technology manager. The fault detection is carried out using residual analysis, model checking based on residual analysis is a standard technique for time series analysis, cf [4], page 175 ff. There are two main types of time series methods for fault detection: non-parametric methods, which use spectral analysis, and parametric methods, which can be categorised as parameter-based and residual-based methods. The developed methods for fault detection could replace a graphical, user-subjective valuation of a residual plot using an automatic, data-based approach
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