Context-based thermodynamic modeling of buildings spaces

Abstract Thermodynamic models are frequently used for modeling the thermal behavior of building spaces. However, the occurrence of events such as, for example, doors, windows and blinds being opened or closed, can drastically affect the underlying processes that govern the dynamics of temperature evolution of building spaces, rendering current thermodynamic models less effective for control and prediction. This article presents a framework for appropriate model structure and parameter selection that accounts for such discrete disturbances based on the notion of context. Contexts are modeled as discrete configurations, capable of representing different thermodynamic behavior models for a building space. Depending on how context changes, our thermodynamic model transitions through a set of different linear time-invariant sub-models. Each sub-model is effective in representing the thermal behavior of the space under a given context and the result is a hybrid automaton that effectively adjusts to the discrete and continuous dynamics of the building environment. We present an application example and use the outputs of EnergyPlus as reference for model performance evaluation. We show, through different context changes, how a context-based model can be used to represent, with reasonable accuracy, the evolution of temperatures in a simulated thermal zone.

[1]  Xiufeng Pang,et al.  Real-Time Building Energy Simulation Using EnergyPlus and the Building Controls Test Bed , 2013 .

[2]  Ari Rabl.  Parameter estimation in buildings: Methods for dynamic analysis of measured energy use , 1988 .

[3]  Qi Luo,et al.  Building thermal network model and application to temperature regulation , 2010, 2010 IEEE International Conference on Control Applications.

[4]  Prabir Barooah,et al.  Identification of multi-zone building thermal interaction model from data , 2011, IEEE Conference on Decision and Control and European Control Conference.

[5]  Yi Jiang,et al.  Dynamical building simulation: A low order model for thermal bridges losses , 2008 .

[6]  L. H. Hansen,et al.  Modelling the heat dynamics of a building using stochastic differential equations , 2000 .

[7]  Tadao Murata,et al.  Petri nets: Properties, analysis and applications , 1989, Proc. IEEE.

[8]  Edward A. Lee,et al.  Operational Semantics of Hybrid Systems , 2005, HSCC.

[9]  José A. Candanedo,et al.  Simplified Linear Models for Predictive Control of Advanced Solar Homes with Passive and Active Thermal Storage , 2010 .

[10]  Eduardo F. Camacho,et al.  Model predictive control techniques for hybrid systems , 2010, Annu. Rev. Control..

[11]  Dominique Derome,et al.  Determination of surface convective heat transfer coefficients by CFD , 2007 .

[12]  Gilles Fraisse,et al.  Development of a simplified and accurate building model based on electrical analogy , 2002 .

[13]  Standard Ashrae.  Thermal Environmental Conditions for Human Occupancy , 1992 .

[14]  Andreas K. Athienitis,et al.  Thermal zoning and interzonal airflow in the design and simulation of solar houses: a sensitivity analysis , 2011 .

[15]  Sergiu-Dan Stan,et al.  A model based predictive control algorithm for building temperature control , 2009, 2009 3rd IEEE International Conference on Digital Ecosystems and Technologies.

[16]  M. M. Gouda,et al.  Building thermal model reduction using nonlinear constrained optimization , 2002 .

[17]  Xin Li,et al.  Context Aware Middleware Architectures: Survey and Challenges , 2015, Sensors.

[18]  José Domingo Álvarez,et al.  Optimizing building comfort temperature regulation via model predictive control , 2013 .

[19]  Sean P. Meyn,et al.  Building thermal model reduction via aggregation of states , 2010, Proceedings of the 2010 American Control Conference.

[20]  Henrik W. Bindner,et al.  THERMAL MODELS FOR INTELLIGENT HEATING OF BUILDINGS , 2012 .

[21]  Sergiy Bogomolov,et al.  PDDL+ Planning with Hybrid Automata: Foundations of Translating Must Behavior , 2015, ICAPS.

[22]  Peng Xu,et al.  THE BUILDING CONTROLS VIRTUAL TEST BED - A SIMULATION ENVIRONMENT FOR DEVELOPING AND TESTING CONTROL ALGORITHMS, STRATEGIES AND SYSTEMS , 2007 .

[23]  Samuel Prívara,et al.  Building modeling: Selection of the most appropriate model for predictive control , 2012 .

[24]  Pekka Tuomaala,et al.  Implementation and evaluation of air flow and heat transfer routines for building simulation tools , 2002 .

[25]  Edward A. Lee,et al.  Exploring models of computation with Ptolemy II , 2010, 2010 IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).

[26]  Jadwiga Indulska,et al.  Developing context-aware pervasive computing applications: Models and approach , 2006, Pervasive Mob. Comput..

[27]  Brian S. Stensrud,et al.  Formalizing context-based reasoning: A modeling paradigm for representing tactical human behavior , 2008 .

[28]  Stefan Kersting,et al.  Online identification of piecewise affine systems , 2014, 2014 UKACC International Conference on Control (CONTROL).

[29]  Marius Mikalsen,et al.  Context: Representation and Reasoning. Representing and Reasoning about Context in a Mobile Environment , 2005, Rev. d'Intelligence Artif..

[30]  Hod Lipson,et al.  Learning symbolic representations of hybrid dynamical systems , 2012, J. Mach. Learn. Res..

[31]  Mei-Yu Wu,et al.  Context-based knowledge support for problem-solving by rule-inference and case-based reasoning , 2008, 2008 International Conference on Machine Learning and Cybernetics.

[32]  Michael Wetter,et al.  Co-simulation of building energy and control systems with the Building Controls Virtual Test Bed , 2011 .

[33]  Sergiy Bogomolov,et al.  Planning as Model Checking in Hybrid Domains , 2014, AAAI.

[34]  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).

[35]  Prabir Barooah,et al.  A method for model-reduction of non-linear thermal dynamics of multi-zone buildings , 2012 .

[36]  Patrick Brézillon,et al.  Using Knowledge in Its Context: Report on the IJCAI-93 Workshop , 1995, AI Mag..

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

[38]  Clara Verhelst,et al.  Building models for model predictive control of office buildings with concrete core activation , 2013 .

[39]  Henrik Madsen,et al.  Estimation of continuous-time models for the heat dynamics of a building , 1995 .

[40]  Avelino J. Gonzalez,et al.  Learning in context: enhancing machine learning with context-based reasoning , 2014, Applied Intelligence.

[41]  Mark Weiser,et al.  The computer for the 21st Century , 1991, IEEE Pervasive Computing.

[42]  Richmond H. Thomason.  Representing and Reasoning with Context , 1998, AISC.

[43]  Francisco Rodríguez,et al.  A comparison of thermal comfort predictive control strategies , 2011 .

[44]  Atila Novoselac,et al.  Localized air-conditioning with occupancy control in an open office , 2010 .

[45]  Prabir Barooah,et al.  A Method for model-reduction of nonlinear building thermal dynamics , 2011, Proceedings of the 2011 American Control Conference.

[46]  A. Sarić,et al.  Dynamic model reduction: An overview of available techniques with application to power systems , 2012 .

[47]  Sten Bay Jørgensen,et al.  Parameter estimation in stochastic grey-box models , 2004, Autom..

[48]  Patrick Brézillon,et al.  Context in problem solving: a survey , 1999, The Knowledge Engineering Review.

[49]  Euiho Suh,et al.  Context-aware systems: A literature review and classification , 2009, Expert Syst. Appl..

[50]  Joshua N. Cooper,et al.  Parameter identification and model based predictive control of temperature inside a house , 2011 .

[51]  Baskar Ganapathysubramanian,et al.  A stochastic approach to modeling the dynamics of natural ventilation systems , 2013 .