Simulation and evaluation of Building Information Modeling in a real pilot site

The current methods of building energy simulation that designers and engineers (D&E) use in order to find the energy performance of a building do not take into account the real behavior of the people who will use the building. The main aim of this paper is to show how by merely including the real behavior of people in building simulations there may be differences of up to 30%, through the study of a real pilot site simulation with existing software. These data confirm the possibilities of energy and money saving that energy simulation programs bring about when they include schedules of true use of the building (BIM).

[1]  J. Kelly Kissock,et al.  Measuring industrial energy savings , 2008 .

[2]  Taehoon Hong,et al.  Framework for the implementation of a new renewable energy system in an educational facility , 2013 .

[3]  Jiří Jaromír Klemeš,et al.  Advanced multimedia engineering education in energy, process integration and optimisation , 2013 .

[4]  Darren Robinson,et al.  Some trends and research needs in energy and comfort prediction , 2006 .

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

[6]  Jozef Stefan,et al.  Sustainable development of energy, water and environment systems , 2014 .

[7]  Bing Dong,et al.  Sensor-based occupancy behavioral pattern recognition for energy and comfort management in intelligent buildings , 2009 .

[8]  Rui Zhang,et al.  An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network , 2010 .

[9]  Denis J. Bourgeois,et al.  Detailed occupancy prediction, occupancy-sensing control and advanced behavioural modelling within whole-building energy simulation , 2005 .

[10]  Gerhard Zimmermann MODELING THE BUILDING AS A SYSTEM , 2003 .

[11]  Tianzhen Hong,et al.  Statistical analysis and modeling of occupancy patterns in open-plan offices using measured lighting-switch data , 2013 .

[12]  Rui Zhang,et al.  Information-theoretic environmental features selection for occupancy detection in open offices , 2009 .

[13]  Jessen Page,et al.  Simulating occupant presence and behaviour in buildings , 2007 .

[14]  Jon Hand,et al.  CONTRASTING THE CAPABILITIES OF BUILDING ENERGY PERFORMANCE SIMULATION PROGRAMS , 2008 .

[15]  Enrico Fabrizio,et al.  The impact of indoor thermal conditions, system controls and building types on the building energy demand , 2008 .

[16]  Konstantinos Papamichael,et al.  Building design advisor: automated integration of multiple simulation tools , 1997 .

[17]  Luke Bisby,et al.  A nascent educational framework for fire safety engineering , 2013 .

[18]  Douglas Probert,et al.  Environmental auditing: Estimating and reducing corporate greenhouse-gas emissions using monitoring and targeting software systems , 1992 .

[19]  Lidija Čuček,et al.  Multi-objective optimisation for generating sustainable solutions considering total effects on the environment , 2013 .

[20]  Jlm Jan Hensen,et al.  An approach to use building performance simulation to support design optimization , 2006 .

[21]  Svend Svendsen,et al.  Method for simulating predictive control of building systems operation in the early stages of building design , 2011 .

[22]  Veronica Soebarto,et al.  Multi-criteria assessment of building performance: theory and implementation , 2001 .

[23]  M Jahn,et al.  Towards a context control model for simulation and optimization of energy performance in buildings , 2012 .

[24]  R. Fritsch,et al.  A stochastic model of user behaviour regarding ventilation , 1990 .

[25]  Godfried Augenbroe BUILDING SIMULATION TRENDS GOING INTO THE NEW MILLENIUM , 2001 .

[26]  Raimar Scherer,et al.  eWork and eBusiness in Architecture, Engineering and Construction : Proceedings of the 12th European Conference on Product and Process Modelling (ECPPM 2018), September 12-14, 2018, Copenhagen, Denmark , 2009 .

[27]  Ardeshir Mahdavi,et al.  TOWARD EMPIRICALLY-BASED MODELS OF PEOPLE ' S PRESENCE AND ACTIONS IN BUILDINGS , 2009 .

[28]  Cheng Tian,et al.  Experimental and simulating examination of computer tools, Radlink and DOE2, for daylighting and energy simulation with venetian blinds , 2014 .

[29]  Tianzhen Hong,et al.  Building simulation: an overview of developments and information sources , 2000 .

[30]  J. Haymaker,et al.  THE IMPACT OF THE BUILDING OCCUPANT ON ENERGY MODELING SIMULATIONS , 2006 .

[31]  P. Smyth,et al.  Modeling Count Data from Multiple Sensors: A Building Occupancy Model , 2007, 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing.

[32]  Dimitrios Tzovaras,et al.  Occupancy and business modelling , 2012 .

[33]  Francis Rubinstein,et al.  Modeling occupancy in single person offices , 2005 .

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

[35]  Brian Vad Mathiesen,et al.  A review of computer tools for analysing the integration of renewable energy into various energy systems , 2010 .

[36]  David E. Claridge,et al.  Compilation of Diversity Factors and Schedules for Energy and Cooling Load Calculations, ASHRAE Research Project 1093-RP, Final Report , 1999 .

[37]  John Psarras,et al.  An integrated system for buildings’ energy-efficient automation: Application in the tertiary sector , 2013 .

[38]  William Chung,et al.  Review of building energy-use performance benchmarking methodologies , 2011 .

[39]  Jlm Jan Hensen,et al.  On incorporating uncertainty analysis in abstract building performance simulation tools , 2007 .