In this paper we apply HVAC control modeling tools implemented in the OpenBuildingControl project. We utilize the Control Description Language, currently being standardized through ASHRAE Standard 231P, and our libraries of control sequences based on ASHRAE Guideline 36. We created a closed-loop model of a cooling plant that includes a chiller, a water-side economizer, and an evaporative cooling tower. The plant provides cooling at 27◦C zone supply air temperature to a data center in Sacramento, CA, USA. We present chiller plant energy use comparison using a base and an alternative (ASHRAE Guideline 36) set of control sequences. We compare annual and summer season power usage effectiveness and illustrate controller operation. The results show a 25% annual energy use reduction with the alternative controls. The capabilities of simulation-based control performance evaluation, enabled through our tools, will further support energy efficiency, grid flexibility, and resilience aspects of system controls.

Advances in building control have shown significant potential for improving building energy performance and decarbonization. One of the challenges to realizing those savings is the correct implementation of such advanced control strategies and regularly verifying their actual operational performance. Currently, the verification is often manually conducted, which is time-consuming, adhoc, and error-prone. To address this challenge, we introduced an automated control verification framework, aiming to create automatic verification of control performance in buildings. This framework integrates interpretations of the control requirements from recent energy codes and knowledge of conducting control verification into a knowledge base. The verification is based on time series data and thus is applicable for both trended data from a building automation system and simulation results of building energy simulations. This paper discusses the new version of the control performance verification framework, the expansion of the verification library, and the newly developed key performance indicators (KPIs) for control verification. It also provides an evaluation of the tool using both simulation and real building operation data and also an end-to-end demonstration case from importing a data set equipped with a semantic model, automatically instantiating control verification cases, and then conducting the verification and reporting the result.