The virtual summit takes place Wednesday, Sept. 27 from 1-3 p.m. ET. fnPrime members can register for free
Bring your questions and get answers from Joan Stein, nationally recognized ADA expert, in this interactive virtual session
BuildingIQ has accelerated its leadership in artificial intelligence (AI) solutions with Epiphany – an AI-driven engine that uncovers how data is interconnected, dependent and influenced within a system. Epiphany is used to create foundational knowledge about a system that can enable insights like root cause analysis of anomalies; predictive failure and maintenance of equipment; better capital planning; and prioritization of service based on the real, hidden impact of issues versus simplistic cost-to-replace estimates.
Inferences derived from Epiphany are today powering BuildingIQ’s Outcome-based Fault Detection (OFD) service, bringing deeper anomaly detection and more valuable insights to fault detection within a building. With the introduction of the Epiphany full suite of tools, BuildingIQ can create a virtualized network of a system that brings an even more holistic view of system interactions among myriad data points and assets, regardless of how they are wired or logically connected.
“The problem with the IoT is that in practical terms, it’s a free-for-all in stand-alone devices and sub-systems. Using Epiphany, BuildingIQ is able to map how those devices and sub-systems are interdependent – creating a virtual network that shows how data and assets are connected in chains of influence,” said Steve Nguyen, vice president of products for BuildingIQ. “These virtual networks are manageable as if they were one network, making them able to be optimized for performance and tuned to the outcomes that best suit the needs of the operators and end-users. The creation of a virtual network is key to leveraging how IoT devices interact with each other and existing systems, such as building automation systems.”
The four components of Epiphany as initially applied to building systems are:
· Influence Engine
o Reveals the impact of all points relative to whole building power, inferring what is driving a building’s energy consumption.
· Influence Delta
o Examines how an influence changes over time, looking at associated power impact from equipment due to that change.
· Delta T
o Correlates real energy impacts with comfort impacts, allowing facilities teams to prioritize work better than with simple rule violation faults.
· Correlation Map
o Uses machine learning to create a map of the relationships between points, quickly inferring root cause through powerful filtering capabilities.