On-line simulation of building energy processes: Need and research requirements

Most building energy simulation software offers significant building energy performance capabilities; however, its use is limited to design phase only. There is significant benefit to have these energy simulation models available during operation phase for detection and diagnostics. Since simulation models and real building states are not coupled, the models are initialized in an empty state or run through a warm-up period (i.e., off-line simulation). This paper develops the need and research requirements for on-line simulation of building energy processes where current state variables obtained from sensors and meters in buildings are used to initialize the model. Based on the simulation results, a new corrective decision is made and implemented in the real process. This paper argues that on-line simulation can provide decision makers with reliable energy models to test different technical and behavioral interventions, and improve predictions of building performance, compared to the results obtained with existing off-line models.

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

[2]  Gamma-Gamma Collider ERNEST ORLANDO LAWRENCE BERKELEY NATIONAL LABORATORY , 1996 .

[3]  Elie Azar,et al.  Agent-Based Modeling of Occupants and Their Impact on Energy Use in Commercial Buildings , 2012, J. Comput. Civ. Eng..

[4]  Carol C. Menassa,et al.  A framework for automated control and commissioning of hybrid ventilation systems in complex buildings , 2013 .

[5]  Steven T. Bushby,et al.  A rule-based fault detection method for air handling units , 2006 .

[6]  John Psarras,et al.  Assessing energy-saving measures in buildings through an intelligent decision support model , 2009 .

[7]  John E. Taylor,et al.  Response–relapse patterns of building occupant electricity consumption following exposure to personal, contextualized and occupant peer network utilization data , 2010 .

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

[9]  H. Staats,et al.  A longitudinal study of informational interventions to save energy in an office building. , 2000, Journal of applied behavior analysis.

[10]  J. Banks,et al.  Discrete-Event System Simulation , 1995 .

[11]  Christoph F. Reinhart,et al.  Non-technical barriers to energy model sharing and reuse , 2011 .

[12]  Michael Wetter,et al.  A framework for simulation-based real-time whole building performance assessment , 2012 .

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

[14]  Carol C. Menassa,et al.  Optimizing hybrid ventilation in public spaces of complex buildings – A case study of the Wisconsin Institutes for Discovery , 2013 .

[15]  S. Mirdamadi,et al.  Discrete Event Simulation-Based Real-Time Shop Floor Control , 2007 .

[16]  J. Taylor,et al.  The impact of peer network position on electricity consumption in building occupant networks utilizing energy feedback systems , 2012 .

[17]  Carol C. Menassa,et al.  Energy Consumption Evaluation of U.S. Navy LEED-Certified Buildings , 2012 .

[18]  Hyunjoo Kim,et al.  Energy Modeling System Using Building Information Modeling Open Standards , 2013 .

[19]  C. Goldman Coordination of Energy Efficiency and Demand Response , 2010 .

[20]  Frauke Oldewurtel,et al.  Experimental analysis of model predictive control for an energy efficient building heating system , 2011 .

[21]  H. Staats,et al.  Effecting Durable Change , 2004 .

[22]  R. N. Elliott,et al.  American Council for an Energy-Efficient Economy , 2002 .

[23]  Nhan Nguyen,et al.  “Green engineering: Defining the principles”— resdts from the sandestin conference , 2003 .

[24]  Seth Mangasarian ENERGY CONSUMPTION EVALUATION OF UNITED STATES NAVY LEED CERTIFIED BUILDINGS FOR FISCAL YEAR 2009 , 2010 .

[25]  Cecilia R. Aragon,et al.  How People Actually Use Thermostats , 2010 .

[26]  Srinivas Katipamula,et al.  Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I , 2005 .

[27]  Russell C. H. Cheng,et al.  Selecting input models , 1994, Proceedings of Winter Simulation Conference.

[28]  David E. Claridge,et al.  Calibration Procedure for Energy Performance Simulation of a Commercial Building , 2003 .

[29]  Mark S. Martinez,et al.  International performance measurement & verification protocol: Concepts and options for determining energy and water savings , 2001 .

[30]  C. Vlek,et al.  A review of intervention studies aimed at household energy conservation , 2005 .

[31]  Barney L. Capehart,et al.  Guide to Energy Management , 1969 .

[32]  Frank Painter Whole-Building Commercial HVAC System Simulation for Use in Energy Consumption Fault Detection , 2007 .

[33]  Jerry Yudelson,et al.  Greening Existing Buildings , 2009 .

[34]  Donald T Resio Virtual Test Bed , 1999 .

[35]  G. Lowry,et al.  Factors affecting the success of building management system installations , 2002 .

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

[37]  Jeff Haberl,et al.  Procedures for Calibrating Hourly Simulation Models to Measured Building Energy and Environmental Data , 1998 .

[38]  Yi Jiang,et al.  An information sharing building automation system , 2009 .

[39]  Xinyu Shao,et al.  On-Line Simulation for Shop Floor Control in Manufacturing Execution System , 2008, ICIRA.