Lightweight and adaptive building simulation (LABS) framework for integrated building energy and thermal comfort analysis

Coupled and distributed simulation helps in understanding the complexity arising from the combined effects of interdependent systems, by connecting and exchanging information across several software programs. In the building energy analysis domain, several tools have been created in the past to facilitate such analyses. However, the existing coupling frameworks such as Building Control Virtual Test Bed (BCVTB), MLE+, High-Level Architecture (HLA), and Functional Mockup Unit are characterized by their inherent complexity, making it a challenge for the building practitioners to widely deploy them in everyday decision-making. In addition, several of these frameworks embody tight coupling, which means they lack the flexibility to incorporate models and components of decision-makers’ choice. This study addresses these gaps by proposing a Lightweight and Adaptive Building Simulation (LABS) framework that capitalizes on Lightweight Communications and Marshalling (LCM), an inter-process communication framework widely used by the robotics community. As a case study demonstrating this new framework, a building energy simulation model is coupled with an agent-based occupant behavior model to understand the energy effects of occupants’ thermal comfort related actions (e.g., adjusting the thermostat set point). These behavioral patterns are also influenced by various interventions (e.g., peer pressure, energy-based education) that may occur in the building. Measuring these effects in a real building for a lengthy period is impractical and resource-intensive and the LABS framework can be used for understanding this system better. The results also highlight opportunities for achieving energy savings by influencing the occupants’ comfort related behavior.

[1]  P. O. Fanger,et al.  Thermal comfort: analysis and applications in environmental engineering, , 1972 .

[2]  Carol C. Menassa,et al.  Distributed simulation framework to analyze the energy effects of adaptive thermal comfort behavior of building occupants , 2016, 2016 Winter Simulation Conference (WSC).

[3]  Werner Dubitzky,et al.  Large-Scale Computing Techniques for Complex System Simulations , 2011 .

[4]  Mani Golparvar-Fard,et al.  3D Visualization of thermal resistance and condensation problems using infrared thermography for building energy diagnostics , 2014 .

[5]  Jin Wen,et al.  INCLUDING OCCUPANTS IN BUILDING PERFORMANCE SIMULATION: INTEGRATION OF AN AGENT-BASED OCCUPANT BEHAVIOR ALGORITHM WITH ENERGYPLUS , 2014 .

[6]  Elie Azar,et al.  Evaluating the impact of extreme energy use behavior on occupancy interventions in commercial buildings , 2015 .

[7]  Jie Zhao,et al.  Energyplus Model-based Predictive Control (epmpc) By Using Matlab/simulink And Mle+ , 2013, Building Simulation Conference Proceedings.

[8]  Levent Yilmaz,et al.  Distributed Simulation: A Model Driven Engineering Approach , 2016 .

[9]  Stéphane Ploix,et al.  Virtual Simulation with Real Occupants using Serious Games , 2015, Building Simulation Conference Proceedings.

[10]  Elie Azar,et al.  Framework to Evaluate Energy-Saving Potential from Occupancy Interventions in Typical Commercial Buildings in the United States , 2014, J. Comput. Civ. Eng..

[11]  Joseph A. Paradiso,et al.  Personalized HVAC control system , 2010, 2010 Internet of Things (IOT).

[12]  Joseph Andrew Clarke,et al.  Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings , 2007 .

[13]  Eilif Pedersen,et al.  Distributed Co-Simulation of Maritime Systems and Operations , 2017, ArXiv.

[14]  Thierry S. Nouidui,et al.  Building energy simulation in real time through an open standard interface , 2016 .

[15]  Yixing Chen,et al.  EnergyPlus and CHAMPS-Multizone co-simulation for energy and indoor air quality analysis , 2015 .

[16]  Nicolas Morel,et al.  A personalized measure of thermal comfort for building controls , 2011 .

[17]  Truong Nghiem,et al.  MLE+: a tool for integrated design and deployment of energy efficient building controls , 2012, SIGBED.

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

[19]  Kristian Fabbri,et al.  The Indices of Feeling—Predicted Mean Vote PMV and Percentage People Dissatisfied PPD , 2015 .

[20]  Guillaume Deffuant,et al.  How can extremism prevail? A study based on the relative agreement interaction model , 2002, J. Artif. Soc. Soc. Simul..

[21]  Christoph F. Reinhart,et al.  Adding advanced behavioural models in whole building energy simulation: A study on the total energy impact of manual and automated lighting control , 2006 .

[22]  T. S. Liu,et al.  Automatic Control System for Thermal Comfort Based on Predicted Mean Vote and Energy Saving , 2015, IEEE Transactions on Automation Science and Engineering.

[23]  Tianzhen Hong,et al.  An ontology to represent energy-related occupant behavior in buildings. Part II: Implementation of the DNAS framework using an XML schema , 2015 .

[24]  Elie Azar,et al.  A comprehensive framework to quantify energy savings potential from improved operations of commercial building stocks , 2014 .

[25]  Carol C. Menassa,et al.  An LCM Framework to Couple Spatially Distributed Energy Simulation and Occupancy Models for Optimizing Building Energy Consumption , 2016 .

[26]  Richard M. Fujimoto,et al.  Parallel and distributed simulation , 1995, 2015 Winter Simulation Conference (WSC).

[27]  Ahmed Amine Jerraya,et al.  MCI- Multilanguage Distributed Co- Simulation Tool , 1998, DIPES.

[28]  Soonhoi Ha,et al.  Virtual synchronization for fast distributed cosimulation of dataflow task graphs , 2002, 15th International Symposium on System Synthesis, 2002..

[29]  Radu Zmeureanu,et al.  Automatic assisted calibration tool for coupling building automation system trend data with commissioning , 2016 .

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

[31]  Tuncer I. Ören Uses of Simulation , 2008 .

[32]  Bing Dong,et al.  The Contribution Of Occupancy Behavior To Energy Consumption In Low Income Residential Buildings , 2014 .

[33]  Robert G. Sargent,et al.  Verification, validation and accreditation of simulation models , 2000, 2000 Winter Simulation Conference Proceedings (Cat. No.00CH37165).

[34]  Alberto Cerpa,et al.  Thermovote: participatory sensing for efficient building HVAC conditioning , 2012, BuildSys@SenSys.

[35]  Igal M. Shohet,et al.  Deterioration patterns of building cladding components for maintenance management , 2002 .

[36]  W. H. Engelmann,et al.  The National Human Activity Pattern Survey (NHAPS): a resource for assessing exposure to environmental pollutants , 2001, Journal of Exposure Analysis and Environmental Epidemiology.

[37]  Stephen G. Tell,et al.  An engineering environment for hardware/software co-simulation , 1992, [1992] Proceedings 29th ACM/IEEE Design Automation Conference.

[38]  Thierry S. Nouidui,et al.  Functional mock-up unit for co-simulation import in EnergyPlus , 2014 .

[39]  Jin Wen,et al.  Simulating the human-building interaction: Development and validation of an agent-based model of office occupant behaviors , 2015 .

[40]  Chen Feng,et al.  Conceptual Framework to Optimize Building Energy Consumption by Coupling Distributed Energy Simulation and Occupancy Models , 2014, J. Comput. Civ. Eng..

[41]  John E. Taylor,et al.  Modeling building occupant network energy consumption decision-making: The interplay between network structure and conservation , 2012 .

[42]  Mani Golparvar-Fard,et al.  Mapping actual thermal properties to building elements in gbXML-based BIM for reliable building energy performance modeling , 2015 .

[43]  Scott E. Page,et al.  Emergent cultural signatures and persistent diversity: A model of conformity and consistency , 2010 .

[44]  Edwin Olson,et al.  LCM: Lightweight Communications and Marshalling , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[45]  Carol C. Menassa,et al.  System Dynamics Framework to Study the Effect of Material Performance on a Building's Lifecycle Energy Requirements , 2016, J. Comput. Civ. Eng..

[46]  Averill M. Law,et al.  Simulation Modeling and Analysis , 1982 .

[47]  Edward A. Lee,et al.  Heterogeneous Concurrent Modeling and Design in Java (Volume 1: Introduction to Ptolemy II) , 2008 .

[48]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[49]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[50]  Elie Azar,et al.  A comprehensive analysis of the impact of occupancy parameters in energy simulation of office buildings , 2012 .

[51]  Tianzhen Hong,et al.  An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework , 2015 .

[52]  Chang Ho Sung,et al.  Framework for Simulation of Hybrid Systems: Interoperation of Discrete Event and Continuous Simulators Using HLA/RTI , 2011, 2011 IEEE Workshop on Principles of Advanced and Distributed Simulation.

[53]  Jie Zhao,et al.  Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining , 2014 .

[54]  Yuan Cao,et al.  A distributed simulation system and its application , 2007, Simul. Model. Pract. Theory.

[55]  Jlm Jan Hensen,et al.  INTEGRATION OF CONTROL AND BUILDING PERFORMANCE SIMULATION SOFTWARE BY RUN-TIME COUPLING , 2003 .

[56]  Guillaume Deffuant,et al.  Mixing beliefs among interacting agents , 2000, Adv. Complex Syst..

[57]  Ernest H. Page Theory and Practice for Simulation Interconnection , 2007, Handbook of Dynamic System Modeling.

[58]  Anna Dyson,et al.  Development of a modeling strategy for adaptive multifunctional solar energy building envelope systems , 2015, SpringSim.

[59]  Carol C. Menassa,et al.  A Framework to Understand Effect of Building Systems Deterioration on Life Cycle Energy , 2015 .

[60]  Jie Zhao,et al.  EnergyPlus model-based predictive control within design–build–operate energy information modelling infrastructure , 2015 .

[61]  Flávio Rech Wagner,et al.  A Standardized Co-simulation Backbone , 2001, VLSI-SOC.

[62]  Judith S. Dahmann,et al.  Creating Computer Simulation Systems: An Introduction to the High Level Architecture , 1999 .

[63]  Carol C. Menassa,et al.  Impact of Social Network Type and Structure on Modeling Normative Energy Use Behavior Interventions , 2014, J. Comput. Civ. Eng..

[64]  Edward A. Lee,et al.  Ptolemy II, Heterogeneous Concurrent Modeling and Design in JAVA , 2001 .

[65]  Elie Azar,et al.  Integrating building performance simulation in agent-based modeling using regression surrogate models: A novel human-in-the-loop energy modeling approach , 2016 .