Identification of Causal Variables for Building Energy Fault Detection by Semi-supervised LDA and Decision Boundary Analysis

This paper addresses the identification problem of causal variables for the system anomaly. In real-world complicated systems, even experts often fail to specify causal factors, thus they attempt to detect the anomaly with exploratory heuristics. Our goal is to offer further information that supports anomaly cause analysis using the incomplete empirical knowledge. Proposed technique discovers responsible factors for the fault by leveraging domain knowledge with an effective combination of semi-supervised linear discriminant analysis (LDA) and boundary-based discriminative subspace identification method. Experimental results on synthetic and real dataset confirmed validity of our approach. Moreover, we applied this method to the building energy fault diagnosis and succeeded in extracting causal variables for energy waste in a building.