### Abstract

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.

Original language | English (US) |
---|---|

Pages (from-to) | 177-196 |

Number of pages | 20 |

Journal | HVAC and R Research |

Volume | 12 |

Issue number | 1 |

State | Published - Jan 2006 |

Externally published | Yes |

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### ASJC Scopus subject areas

- Building and Construction
- Mechanical Engineering
- Physical and Theoretical Chemistry
- Fluid Flow and Transfer Processes

### Cite this

*HVAC and R Research*,

*12*(1), 177-196.

**Calibration of building energy simulation programs using the analytic optimization approach (RP-1051).** / Sun, Jian; Reddy, T Agami.

Research output: Contribution to journal › Article

*HVAC and R Research*, vol. 12, no. 1, pp. 177-196.

}

TY - JOUR

T1 - Calibration of building energy simulation programs using the analytic optimization approach (RP-1051)

AU - Sun, Jian

AU - Reddy, T Agami

PY - 2006/1

Y1 - 2006/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=33644639915&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33644639915&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:33644639915

VL - 12

SP - 177

EP - 196

JO - Science and Technology for the Built Environment

JF - Science and Technology for the Built Environment

SN - 2374-4731

IS - 1

ER -