A whole-building data-driven fault detection and diagnosis approach for public buildings in hot climate regions

Abstract

Fault detection and diagnosis (FDD) approaches comprise three main pillars: model-based, knowledge-based, and data-driven strategies. Data-driven approaches prioritise operational data and do not necessitate in-depth understanding of the system's background; yet, significant amounts of data is required, which often poses challenges to researchers. Since simulated data is inexpensive and can run numerous faults types with varying severities and time periods, it has been used in data-driven FDD analysis. However, the majority of FDD approaches are implemented at the system level of buildings. However, most buildings have numerous systems with distinct features. Furthermore, using individualised system-level analysis makes it difficult to see system-to-system relationships. Currently, there is a significant underrepresentation of research that investigate the applications of FDD models under whole-building scenarios, so as to identify a wider range of energy consumption related faults in buildings. Furthermore, since data-driven approaches significantly depend on the quantities of training data, it becomes challenging to diagnose faults that have limited features. As a result, this study diagnoses numerous building systems faults, including single and simultaneous faults with limited features. This is implemented within the context of the whole-building energy performance of religious buildings in hot climatic areas, employing data-driven FDD methodologies. Various multi-class classification approaches were investigated to classify both the normal condition and faulty classes. Furthermore, feature extraction methodologies were compared to quantify their potential for improving the diagnosis. In addition to the classification evaluation metrics, one-way ANOVA and Tukey-Kramer tests were implemented to examine the significance of the reported performance differences. RF classifier obtained highest classification accuracy during validation and testing with about 90%, indicating a promising performance in whole-building faults analysis. The adoption of feature extraction techniques did not improve classification performance, thereby emphasising that some classifiers may perform better with high-dimensional datasets.