Delta-T-based operational signatures for operation pattern and fault diagnosis of building energy systems

Abstract

Energy consumption patterns are key information for efficiently operating energy systems in buildings or primary energy systems in cities. Nevertheless, operation pattern analysis (e.g., signature analysis), presenting the correlations between system variables, is needed to diagnose inefficient operation or system faults because conventional energy usage patterns have limited insight into the operation patterns inside building energy systems. Thus, a novel operation pattern analysis method is proposed for building energy systems using the clustering-based operational signatures. The novelty of this method is that it is based on delta-T signatures in building energy systems. Because delta-T can be a major representative of operational states, delta-T signature-based clustering can determine both normal and faulty operation. Moreover, delta-T (e.g., water temperature differences between supply and return paths) can be easily measured in a basic sensor deployment in a building automation system. The delta-T signatures can be converted into operational and faulty signatures. These features enable the operation pattern analysis to be applied to building energy systems widely without the need for luxurious sensing systems or building energy management systems. In this study, the proposed method was applied to an operational chilled water cooling system for real multiunit residential buildings. The application shows how the delta-T signature-based clustering method can diagnose operation patterns and various faults. In the target buildings, inefficient operation (accounting for 30.8%) was found in the normal delta-T area, and sensor errors (random, bias, and drift) and abnormal device sequences were captured in the negative or extreme delta-T areas. These patterns and faults could be matched to daily cooling energy usage patterns, thus providing a deeper knowledge of operation.