%0 Conference Paper %K Energy efficiency %K Building %K HVAC %K Controls %K Simulation %K Grid Flexibility %K Semantic Interoperability %A Amir Roth %A Michael Wetter %A Kyle Benne %A Yan Chen %A Gabriel Fierro %A Marco Pritoni %A Avijit Saha %A Draguna Vrabie %B 2022 Summer Study on Energy Efficiency in Buildings %C Pacific Grove, CA %D 2022 %G eng %I ACEEE %R 10.20357/B70G62 %T Towards Digital and Performance-Based Supervisory HVAC Control Delivery %U https://escholarship.org/uc/item/59z6d46m %8 08/2022 %X

Upgrading supervisory HVAC control in commercial buildings is one of the most attractive decarbonization tools at our disposal. Modern controls are software programs and can in theory be deployed at scale and with a low up-front carbon “pulse”. In practice, however, control delivery is a disjointed and inefficient process, dominated by manual handoffs of imprecise English language documents. A particularly high barrier exists between control implementation and building energy modeling (BEM) which results in control sequences typically not being tested for correctness or performance before implementation.
Together with industry partners, DOE and the national labs are developing an ecosystem of tools and standards that can support fully digital performance-based control delivery workflows. This paper describes this ecosystem, which consists of three mutually supportive efforts. Semantic models of buildings and their systems enable automatic configuration and installation of control software. Platform-neutral control descriptions separate control algorithms from control platforms and enable the creation of libraries of reference control implementations. Dynamic whole-building energy-control simulation that can execute physically realistic control sequences makes it possible to test and evaluate the performance of control sequences and then directly compile them for installation and execution in control systems.
In addition to digitizing and streamlining project-level control delivery, these standards and related software support benchmarking of control algorithms, both rule-based and optimization-based, and help to both advance the state of the art and to implement ratings and programs that encourage the adoption of high-performance control.