Title :
Cloud-based model predictive building thermostatic controls of commercial buildings: Algorithm and implementation
Author :
Biyik, Emrah ; Brooks, James D. ; Sehgal, Hullas ; Shah, Jigar ; Gency, Sahika
Author_Institution :
Dept. of Energy Syst. Eng., Yasar Univ., Izmir, Turkey
Abstract :
The contribution of this paper is in two-folds: 1) If more predictive and intelligent control of the thermostat setpoints with no explicit models of Root Top Units (RTUs) yet with simplistic lumped parameter thermal models of buildings can be effective in reducing a small commercial buildings summer-time peak load while adequately maintaining comfort levels, and 2) how this simplistic indirect control approach to RTUs compare to more sophisticated direct control approaches in terms of peak-load reduction and cost. First, the model-predictive control approach is presented. Second, the results of cloud-based implementation of the optimization algorithm at the two demonstration commercial buildings owned by General Electric (GE), optimizer characteristics, different set point trajectories and their implication with regards to peak load and comfort, and observations are described. On average, the savings from the indirect optimal control strategy utilized in our approach through a cloud-based control implementation architecture is shown to be comparable to previously stated savings in literature from more sophisticated direct optimal control of RTUs while the comfort levels are the same as the non-optimal strategy or slightly better in some cases.
Keywords :
building management systems; buildings (structures); cloud computing; control engineering computing; energy management systems; intelligent control; optimal control; optimisation; predictive control; thermostats; GE; General Electric; RTU; cloud-based control implementation architecture; cloud-based model predictive building thermostatic controls; comfort levels; commercial buildings; cost reduction; direct control approaches; indirect optimal control; intelligent control; lumped parameter thermal models; optimization algorithm; optimizer characteristics; peak-load reduction; predictive control; root top units; set point trajectories; summer-time peak load; thermostat setpoints; Buildings; Load modeling; Optimal control; Optimization; Predictive models; Temperature control; Temperature measurement;
Conference_Titel :
American Control Conference (ACC), 2015
Conference_Location :
Chicago, IL
Print_ISBN :
978-1-4799-8685-9
DOI :
10.1109/ACC.2015.7170975