Title :
BRCM Matlab Toolbox: Model generation for model predictive building control
Author :
Sturzenegger, David ; Gyalistras, Dimitrios ; Semeraro, Vito ; Morari, Manfred ; Smith, Roy S.
Author_Institution :
Autom. Control Lab., ETH Zurich, Zurich, Switzerland
Abstract :
Model predictive control (MPC) is a promising alternative in building control with the potential to improve energy efficiency and comfort and to enable demand response capabilities. Creating an accurate building model that is simple enough to allow the resulting MPC problem to be tractable is a challenging but crucial task in the control development. In this paper we introduce the Building Resistance-Capacitance Modeling (BRCM) Matlab Toolbox that facilitates the physical modeling of buildings for MPC. The Toolbox provides a means for the fast generation of (bi-)linear resistance-capacitance type models from basic building geometry, construction and systems data. Moreover, it supports the generation of the corresponding potentially time-varying costs and constraints. The Toolbox is based on previously validated modeling principles. In a case study a BRCM model was automatically generated from an EnergyPlus input data file and its predictive capabilities were compared to the EnergyPlus model. The Toolbox itself, the details of the modeling and the documentation can be found at www.brcm.ethz.ch.
Keywords :
building management systems; computational geometry; predictive control; time-varying systems; BRCM Matlab Toolbox; BRCM model; EnergyPlus input data file; MPC problem; bilinear resistance-capacitance type model; building construction; building geometry; building model; building resistance-capacitance modeling Matlab Toolbox; demand response capabilities; energy efficiency; model generation; model predictive building control; physical modeling; systems data; time-varying constraints; time-varying costs; Atmospheric modeling; Buildings; Data models; Heat transfer; Load modeling; Mathematical model; Solar heating; Building and facility automation; Modeling and simulation; Predictive control for linear systems;
Conference_Titel :
American Control Conference (ACC), 2014
Conference_Location :
Portland, OR
Print_ISBN :
978-1-4799-3272-6
DOI :
10.1109/ACC.2014.6858967