Algorithm for defining structure of thermal model of building based on RC analogy

Improving control systems in buildings and HVAC systems is one of the roads toward better energy efficiency in such applications. It is often problematic to define the model in complex and nonlinear systems such as buildings and HVAC systems, while accurate and simple model of the system is important condition for efficient control. Resistance- Capacitance networks are recognized as useful and flexible tool for developing such models, but have disadvantages in unclear and long procedure and possibility of errors due to large number of variables and parameters. This paper proposes an algorithm that enables semi-automated design of white-box or grey-box models of buildings, based on layout and used materials, resulting in shorter time and less errors during development of model. Model is implemented and then tested on real building, first as white-box model with initial parameters and later as grey-box model with parameters estimated from data.

[1]  Manfred Morari,et al.  Model Predictive Climate Control of a Swiss Office Building: Implementation, Results, and Cost–Benefit Analysis , 2016, IEEE Transactions on Control Systems Technology.

[2]  Henrik Madsen,et al.  Identifying suitable models for the heat dynamics of buildings , 2011 .

[3]  Chris Underwood,et al.  Modelling Methods for Energy in Buildings , 2004 .

[4]  Lieve Helsen,et al.  Control-Oriented Thermal Modeling of Multizone Buildings: Methods and Issues: Intelligent Control of a Building System , 2016, IEEE Control Systems.

[5]  Brandon Hencey,et al.  Online thermal estimation, control, and self-excitation of buildings , 2013, 52nd IEEE Conference on Decision and Control.

[6]  Zheng O'Neill,et al.  MODEL-BASED THERMAL LOAD ESTIMATION IN BUILDINGS , 2010 .

[7]  Chao Wang,et al.  Automatic 3D Thermal Zones Creation for Building Energy Simulation of Existing Residential Buildings , 2014 .

[8]  Francesco Borrelli,et al.  Predictive Control for Energy Efficient Buildings with Thermal Storage: Modeling, Stimulation, and Experiments , 2012, IEEE Control Systems.

[9]  Florian Judex,et al.  Implementation of an automated building model generation tool , 2014, 2014 12th IEEE International Conference on Industrial Informatics (INDIN).

[10]  Prabir Barooah,et al.  Issues in identification of control-oriented thermal models of zones in multi-zone buildings , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[11]  Christoph F. Reinhart,et al.  Autozoner: an algorithm for automatic thermal zoning of buildings with unknown interior space definitions , 2016 .