Calibration of Building Energy Simulation Programs Using the Analytic Optimization Approach (RP-1051)

Reconciling results from detailed building energy simulation programs to measured data has always been recognized as essential in substantiating how well the simulation model represents the real building and its system. If the simulation results do not match actual monitored data, the programmer will typically “adjust” inputs and operating parameters on a trial-and-error basis until the program output matches the known data. This “fudging” process often results in the manipulation of a large number of variables, which may significantly decrease the credibility of the entire simulation. A major drawback to the widespread acceptance and credibility of the calibrated simulation approach is that it is highly dependent on the personal judgment of the analyst doing the calibration. The lack of a proper mathematical foundation for the general calibration problem has greatly contributed to the current state of affairs. This paper proposes a general analytic framework for calibrating building energy system simulation software/programs that has a firm mathematical and statistical basis. The approach is based on the recognition that although calibration can be cast as an optimization problem, the basic issue is that the calibration problem is underdetermined or overparametrized, i.e., there are many more parameters to tune than can be supported by the monitored data. Further, detailed simulation programs are made up of nonlinear, implicit, and computationally demanding models. The proposed methodology involves several distinct concepts, namely, sensitivity analysis (to identify a subset of strong influential variables), identifiability analysis (to determine how many parameters of this subset can be tuned mathematically and which specific ones are the best candidates), numerical optimization (to determine the numerical values of this best subset of parameters), and uncertainty analysis (to deduce the range of variation of these parameters). A synthetic example involving an office building is used to illustrate the methodology with the DOE-2 simulation program. The proposed methodology is recommended for use as the second step of a two-stage process with the first being a coarse-grid search that has reduced the number of simulation input parameters to a manageable few and also narrowed their individual range of variability.

[1]  Jeff W. Thornton,et al.  Using calibrated engineering models to predict energy savings in large-scale geothermal heat pump projects , 1998 .

[2]  Jeff Haberl,et al.  Graphical tools to help calibrate the DOE-2 simulation program , 1993 .

[3]  James V. Beck,et al.  Parameter Estimation in Engineering and Science , 1977 .

[4]  Per Olof Gutman,et al.  Dominant parameter selection in the marginally identifiable case , 2004, Math. Comput. Simul..

[5]  Joseph C. Lam,et al.  Sensitivity analysis of energy performance of office buildings , 1996 .

[6]  Kevin J. Lomas,et al.  Sensitivity analysis techniques for building thermal simulation programs , 1992 .

[7]  T. Agami Reddy,et al.  Literature review on calibration of building energy simulation programs : Uses, problems, procedures, uncertainty, and tools , 2006 .

[8]  B. D. Hunn,et al.  Comparison of DOE-2 computer program simulations to metered data for seven commercial buildings , 1981 .

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

[10]  D. C. Hittle,et al.  Calibrating building energy analysis models using short term test data , 1996 .

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

[12]  R. Barton.  COMPUTING FORWARD DIFFERENCE DERIVATIVES IN ENGINEERING OPTIMIZATION , 1992 .

[13]  Terri L. Moore,et al.  Regression Analysis by Example , 2001, Technometrics.

[14]  David E. Claridge,et al.  Simplified Building and Air-handling Unit Model Calibration and Applications , 2004 .

[15]  Andreas Griewank,et al.  Evaluating derivatives - principles and techniques of algorithmic differentiation, Second Edition , 2000, Frontiers in applied mathematics.

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

[17]  Philip E. Gill,et al.  Practical optimization , 1981 .

[18]  Jian Sun,et al.  A new approach to developing building energy system simulation programs suitable for both design and optimal operation , 2006 .

[19]  J. J. Hirsch,et al.  DOE-2 supplement: Version 2.1E , 1993 .

[20]  Aldomar Pedrini,et al.  A methodology for building energy modelling and calibration in warm climates , 2002 .

[21]  S. Chatterjee,et al.  Regression Analysis by Example (2nd ed.). , 1992 .

[22]  Veronica I. Soebarto.  CALIBRATION OF HOURLY ENERGY SIMULATIONS USING HOURLY MONITORED DATA AND MONTHLY UTILITY RECORDS FOR TWO CASE STUDY BUILDINGS , 1997 .