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Active Remote Setpoint Optimization Utilizing BAS Trend Data
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In this work, a new concept was explored for the optimization of heating, ventilating, and air-conditioning (HVAC) systems in buildings. The methods assume that only commonly trended sensor data would be available and that no live connection to sensor values would exist. An actual implementation would only require a small script to be written at the target building to request information from a centralized server and update setpoint values. A prioritization of sensors to trend at buildings is presented. Investigations into the feasibility were completed on a case study building on the Texas A&M Campus, the National Center for Therapeutic Medicine (NCTM) and the Preston Royal Library. The algorithms and models for the optimization are presented, along with uncertainty analysis into several key model parameters. 23-29% energy savings were found for AHU-2-3 at the NCTM building from June 1st, 2016 to January 1st, 2017. Missing fan power and air flow sensors reduced effectiveness, along with uncertainty in the plenum temperature for the series fan powered terminal units. Lack of readily available, accurate, manufacturers’ specifications were also limitations. A prototype of the system was developed on the web application CC-Compass, available at Texas A&M.
Paulus, Mitchell Thomas (2017). Active Remote Setpoint Optimization Utilizing BAS Trend Data. Doctoral dissertation, Texas A & M University. Available electronically from