“Next generation” fault detection for commercial building HVAC systems
Equipment faults and control issues cause energy waste and thermal discomfort, and drive up maintenance costs in commercial buildings. Automated fault detection and diagnostics tools help to systematically control these faults to achieve better tenant comfort and energy savings.
Huang will present a novel model-based anomaly-detection method developed with the objective of identifying the full extent of a building’s energy saving “potential” and materially significant system anomalies, which, if addressed, can lead to optimum building performance.
No “rules” were programmed into the system. Rather, the system acted as a search engine, with machine-learning algorithms finding patterns and correlations between multiple heterogeneous data sources including weather data, interval metering data, and building management system data.
Huang is a senior data scientist and building systems engineer at Buildings Alive. He joined the company in 2016 upon the completion of his PhD in mechanical engineering at the University of Adelaide, where he developed model-based intelligent control strategies for optimising the operation of HVAC systems in commercial buildings. During his PhD, he worked closely with facilities managers at the university and Adelaide Airport, where his research findings were field-tested.
Prior to moving to Australia, Huang worked as a field service engineer for GE in China. In addition to his PhD, Huang has a Master of Engineering (mechatronics) from the University of Adelaide and a bachelor’s degree in electrical engineering from the Beijing University of Technology.