A calibration methodology for building dynamic models based on data collected through survey and billings

Abstract A correct dynamic building modeling requires a proper definition of all the parameters that can affect the model outputs. While a preliminary survey will lead to a precise design of the building envelope, other parameters, such as the temperature set-point and the air leakage, are difficult to accurately evaluate, thus introducing errors in the model. Furthermore electrical and thermal consumption invoices are based on monthly records while simulations tools use hours or even more detailed time steps. For all these reasons, the present work is aimed at the definition of a calibration process based on survey, billings and dynamic modeling that takes into account the operator-dependent parameters. The innovative idea behind this calibration process consists of the comparison of the real and simulated energy signatures. 176 + 40 simulations were run in order to find the set of parameters that most accurately overlap the simulated and real energy signatures leading to the calibration of the model. The case study is a retail superstore of 3544 m2 floor area built in central northern Italy. Results demonstrate the validity of the approach proposed showing a calibrated signature with about 1% discrepancies from the real case one. The approach can be extended to different simulation software since the main advantage of the energy signature is to simplify consumption outputs interpretation even in case of complex buildings. A further innovative consequence of the methodology proposed is its capability to promptly identify inefficiencies in the building subsystems, i.e. the HVAC control, thus leading to a fast correction of the root cause without the implementation of complex and expensive monitoring devices.

[1]  Atila Novoselac,et al.  Impact of ventilation and filtration strategies on energy consumption and exposures in retail stores , 2016 .

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

[3]  Thierry Talbert,et al.  A procedure for modeling buildings and their thermal zones using co-simulation and system identification , 2014 .

[4]  F. W. Yu,et al.  Energy signatures for assessing the energy performance of chillers , 2005 .

[5]  Jin Wen,et al.  Review of building energy modeling for control and operation , 2014 .

[6]  A. Rabl,et al.  Energy signature models for commercial buildings: test with measured data and interpretation , 1992 .

[7]  Yi Zhang,et al.  Use jEPlus as an efficient building design optimisation tool , 2012 .

[8]  David E. Claridge,et al.  Manual of Procedures for Calibrating Simulations of Building Systems , 2003 .

[9]  Stig Hammarsten,et al.  A critical appraisal of energy-signature models , 1987 .

[10]  I Knight,et al.  Improved methods for evaluating base temperature for use in building energy performance lines , 2003 .

[11]  Massimiliano Manfren,et al.  Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation , 2013 .

[12]  Martin Lopušniak,et al.  Analysis of thermal energy demand and saving in industrial buildings: A case study in Slovakia , 2013 .

[13]  Paul Raftery,et al.  Calibrating whole building energy models: An evidence-based methodology , 2011 .

[14]  T. Agami Reddy,et al.  Calibrating Detailed Building Energy Simulation Programs with Measured Data—Part I: General Methodology (RP-1051) , 2007 .

[15]  Christine Liddell.  Human factors in energy efficient housing: Insights from a Northern Ireland pocket neighbourhood , 2015 .

[16]  Daniel Coakley,et al.  Calibration of detailed building energy simulation models to measured data using uncertainty analysis , 2014 .

[17]  J A Clarke,et al.  AN APPROACH TO THE CALIBRATION OF BUILDING ENERGY SIMULATION MODELS , 1993 .

[18]  Yeonsook Heo,et al.  Calibration of building energy models for retrofit analysis under uncertainty , 2012 .

[19]  Paul Raftery,et al.  A review of methods to match building energy simulation models to measured data , 2014 .

[20]  A. Persily.  Challenges in Developing Ventilation and Indoor Air Quality Standards: The Story of ASHRAE Standard 62. , 2015, Building and environment.

[21]  Burcin Becerik-Gerber,et al.  A knowledge based approach for selecting energy-aware and comfort-driven HVAC temperature set points , 2014 .

[22]  Mary Ann Piette,et al.  High Performance Commercial Building Systems , 2003 .

[23]  Mingsheng Liu,et al.  A rapid calibration procedure and case study for simplified simulation models of commonly used HVAC systems , 2011 .

[24]  Christoph F. Reinhart,et al.  MODELLING AN EXISTING BUILDING IN DESIGNBUILDER/E+: CUSTOM VERSUS DEFAULT INPUTS , 2009 .