Intelligent chiller evaluation with big data
HVAC systems produce large amounts of data, traditionally used for real-time operation and control. The lack of standardisation of data and systems has historically impeded the ability of it to be used for large time-series and cross-sectional analysis. Solutions are now available to collect this data from disparate systems, standardise it and export it in real-time to cloud-based services for analysis.
Chillers are the largest single piece of equipment in commercial HVAC systems. Wilson will show the power of using high-resolution data from deployed HVAC systems to effectively evaluate chillers. He will discuss how quantitative analysis on real data taken from installed chiller systems is used to analyse actual empirical load densities to IPLV weightings; evaluate chiller efficiencies; and assess the impact of control strategies on the chiller’s efficiency.
Wilson is the chief data scientist at CIM Enviro and a research fellow at Macquarie University. He has a PhD from the Australian Centre for Field Robotics at the University of Sydney, focused on artificial intelligence and machine learning applied to autonomous systems.
Wilson’s research interests include automated fault detection and verification; predictive optimisation; predictive maintenance; adaptive consumption; and measurement and verification. He is focused on applications which can be deployed cost-effectively at scale to achieve maximum impact.