Semantics for assembling modular components in a scalable building performance simulation

This work investigates a scalable building performance simulation (BPS) based on a modular setup deploying the recently developed functional mock-up interface standard. A procedure for the realization of such an approach is presented and a prototypical implementation is described. One of the key obstacles of the modular simulation is identified to be the collocation of simulation modules, which is addressed using a knowledge-based method enabled through ontology. The procedure allows to automatically derive a simulation topology based on the information about the interfacing variables for varying levels of detail. Also information from other sources such as building information models is incorporated. The feasibility of the proposed method is demonstrated by deploying it in test cases of a single-zone and a multi-zone BPS as well as a zonal airflow simulation.

[1]  M Marija Trcka,et al.  Co-simulation for performance prediction of innovative integrated mechanical energy systems in buildings , 2008 .

[2]  Frédéric Haldi,et al.  A Probabilistic Model To Predict Building Occupants’ Diversity Towards Their Interactions With The Building Enveloppe , 2013, Building Simulation Conference Proceedings.

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

[4]  Daniel E. Fisher,et al.  EnergyPlus: creating a new-generation building energy simulation program , 2001 .

[5]  Pieter Pauwels,et al.  A semantic rule checking environment for building performance checking , 2011 .

[6]  Joseph Andrew Clarke Integrated building performance simulation , 2001 .

[7]  Harry Timmermans,et al.  Towards more effective use of building performance simulation in design , 2004 .

[8]  Michael Wetter,et al.  Co-simulation of innovative integrated HVAC systems in buildings , 2009 .

[9]  E. Rank,et al.  Analysis of Building Structure and Topology Based on Graph Theory , 2004 .

[10]  Edward A. Lee,et al.  CyPhySim: a cyber-physical systems simulator , 2015, HSCC.

[11]  Clarice Bleil De Souza,et al.  Thermal performance simulation from an architectural design viewpoint , 2007 .

[12]  Sebastian Herkel,et al.  Integration of Modelica models into an existing simulation software using FMI for Co-Simulation , 2012 .

[13]  Georg Ferdinand Schneider,et al.  An FMI-enabled methodology for modular building performance simulation based on Semantic Web Technologies , 2017 .

[14]  C Christian Struck,et al.  Uncertainty propagation and sensitivity analysis techniques in building performance simulation to support conceptual building and system design , 2012 .

[15]  Livio Mazzarella,et al.  BUILDING ENERGY SIMULATION AND OBJECT-ORIENTED MODELLING : REVIEW AND REFLECTIONS UPON ACHIEVED RESULTS AND FURTHER DEVELOPMENTS , 2009 .

[16]  Stefan Biffl,et al.  Semantic Web Technologies for Intelligent Engineering Applications , 2016, Springer International Publishing.

[17]  Andreas Junghanns,et al.  The Functional Mockup Interface for Tool independent Exchange of Simulation Models , 2011 .

[18]  Christoph van Treeck,et al.  MVD based information exchange between BIM and building energy performance simulation , 2018, Automation in Construction.

[19]  Koen Steemers,et al.  Thermal performance of a naturally ventilated building using a combined algorithm of probabilistic occupant behaviour and deterministic heat and mass balance models , 2009 .

[20]  Dieter Fensel,et al.  Knowledge Engineering: Principles and Methods , 1998, Data Knowl. Eng..

[21]  Andreas Junghanns,et al.  Functional Mockup Interface 2.0: The Standard for Tool independent Exchange of Simulation Models , 2012 .

[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]  Yvon Haradji,et al.  Coupling occupant behaviour with a building energy model - A FMI application , 2014 .

[24]  Shady Attia,et al.  Selection criteria for building performance simulation tools: contrasting architects' and engineers' needs , 2012 .

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

[26]  Jochen Köhler,et al.  Modelica-Association-Project “System Structure and Parameterization” – Early Insights , 2016 .

[27]  Matthias Mitterhofer,et al.  A methodology for a scalable building performance simulation based on modular components , 2017 .

[28]  Edward A. Lee,et al.  Scalable Semantic Annotation Using Lattice-Based Ontologies , 2009, MoDELS.

[29]  Edward A. Lee,et al.  Demo : CyPhySim — A Cyber-Physical Systems Simulator , 2015 .

[30]  Pieter Pauwels,et al.  Representing SimModel in the Web Ontology Language , 2014 .

[31]  James O'Donnell SIMMODEL: A DOMAIN DATA MODEL FOR WHOLE BUILDING ENERGY SIMULATION , 2013 .

[32]  Christoph van Treeck,et al.  New Generation Computational Tools for Building & Community Energy Systems - Annex 60 Final Report , 2017 .

[33]  Jan Hensen,et al.  Building Performance Simulation for Design and Operation , 2019 .

[34]  Wolfgang Kastner,et al.  ThinkHome Energy Efficiency in Future Smart Homes , 2011, EURASIP J. Embed. Syst..

[35]  Alex Ferguson,et al.  THE DESIGN OF AN ESP-R AND TRNSYS CO-SIMULATOR , 2011 .

[36]  Pieter Pauwels,et al.  EXPRESS to OWL for construction industry: Towards a recommendable and usable ifcOWL ontology , 2016 .

[37]  Stéphane Citherlet,et al.  Towards the holistic assessment of building performance based on an integrated simulation approach , 2001 .

[38]  P.J.C.J. De Wilde,et al.  Computational Support for the Selection of Energy Saving Building Components , 2004 .

[39]  Jan Hensen,et al.  Integrated building performance simulation: Progress, prospects and requirements , 2015 .

[40]  Il-Woo Lee,et al.  A Rule-Based Ontology Reasoning System for Context-Aware Building Energy Management , 2015, 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing.

[41]  Stéphane Vialle,et al.  FMI-based distributed multi-simulation with DACCOSIM , 2015, SpringSim.

[42]  Sebastian Rudolph,et al.  Foundations of Semantic Web Technologies , 2009 .

[43]  Svend Svendsen,et al.  Simulation-based support for integrated design of new low-energy office buildings , 2011 .

[44]  Michael Wetter,et al.  A view on future building system modeling and simulation , 2011, Building Performance Simulation for Design and Operation.

[45]  Huajun Chen,et al.  The Semantic Web , 2011, Lecture Notes in Computer Science.

[46]  E. Prud hommeaux,et al.  SPARQL query language for RDF , 2011 .

[47]  James A. Hendler,et al.  Introduction to the Semantic Web Technologies , 2011, Handbook of Semantic Web Technologies.

[48]  E Ery Djunaedy,et al.  External coupling between building energy simulation and computational fluid dynamics , 2005 .

[49]  M. Mitterhofer,et al.  Semantics for Assembling Modular Network Topologies in FMI-Based Building Performance Simulation , 2017, Building Simulation Conference Proceedings.

[50]  Ondrej Holub,et al.  Knowledge-Based Fault Propagation in Building Automation Systems , 2016, 2016 International Conference on Systems Informatics, Modelling and Simulation (SIMS).