Title :
Selecting building predictive control based on model uncertainty
Author :
Maasoumy, Mehdi ; Razmara, Meysam ; Shahbakhti, M. ; Sangiovanni Vincentelli, Alberto
Author_Institution :
Mech. Eng. Dept., Univ. of California, Berkeley, Berkeley, CA, USA
Abstract :
Model uncertainty limits the utilization of Model Predictive Controllers (MPC) to minimize building energy consumption. We propose a new Robust Model Predictive Control (RMPC) structure to make a building controller robust to model uncertainty. The results from RMPC are compared with those from a nominal MPC and a common building Rule Based Control (RBC). The results are then used to develop a methodology for selecting a controller type (i.e. RMPC, MPC, and RBC) as a function of building model uncertainty. RMPC is found to be the desirable controller for the cases with an intermediate level (30%-67%) of model uncertainty, while MPC is preferred for the cases with a low level (0-30%) of model uncertainty. A common RBC is found to outperform MPC or RMPC if the model uncertainty goes beyond a certain threshold (e.g. 67%).
Keywords :
HVAC; buildings (structures); energy consumption; predictive control; RBC; RMPC structure; building energy consumption; building model uncertainty function; common building rule based control; robust model predictive controller; Atmospheric modeling; Buildings; Heating; Mathematical model; Robustness; Temperature measurement; Uncertainty; Building and facility automation; Predictive control for linear systems; Robust control;
Conference_Titel :
American Control Conference (ACC), 2014
Conference_Location :
Portland, OR
Print_ISBN :
978-1-4799-3272-6
DOI :
10.1109/ACC.2014.6858875