Active Remote Setpoint Optimization Utilizing BAS Trend Data
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Date
2017-12-06
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Abstract
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.
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Keywords
HVAC, Energy Optimization, Building Automation Systems, Trend Data