Using Weather and Schedule based Pattern Matching and Feature based PCA for Whole Building Fault Detection — Part I Development of the Method

A whole building fault (WBF) refers to a fault occurring in one component, but may cause impacts on other
components or subsystems, or arise significant impacts on energy consumption and thermal comfort. Conventional
methods (such as component level rule-based method or physical model-based method) which targeted at
component level fault detection cannot be successfully employed to detect a WBF because of the fault propagation
among the closely coupled equipment or subsystems. Therefore, a novel data-driven method named weather and
schedule-based pattern matching (WPM) and feature based principal component analysis (FPCA) method for WBF
detection is developed. Three processes are established in the WPM-FPCA method to address three main issues in
the WBF detection. First, a feature selection process is used to pre-select data measurements which represent a
whole building’s operation performance under a satisfied status, namely baseline status. Secondly, a WPM process
is employed to locate weather and schedule patterns in the historical baseline database, which are similar to that
from the current/incoming operation data, and to generate a WPM baseline. Lastly, real-time PCA models are
generated for both the WPM baseline data and the current operation data. Statistic thresholds used to differentiate
normal and abnormal (faulty) operations are automatically generated in this PCA modeling process. The PCA
models and thresholds are employed to detect the WBF. This paper is the first of a two-part study. Performance
evaluation of the developed method is conducted using data collected from a real campus building and will be
described in the second part of this paper.