Formación y gestión de expectativas

During the past three decades, hourly building energy simulation programs have been used to predict the peak energy demand and energy consumption of new buildings, which includes the design and proper sizing of the heating, ventilating and

air-conditioning (HVAC) systems. Simulation programs have also been used to evaluate energy savings from energy conservation retrofits to existing buildings. An important issue when evaluating energy savings in an existing building is how well the simulated model predictions fit measured data from a real building (Bronson et al. 1992, Bou-Saada 1994, Song 2006, Cho 2009). During the last ten years, numerous studies about calibrated simulations have been reported (Hsieh 1988, Subbarao et al. 1990, Kaplan et al. 1990, 1992, Bronson et al. 1992, Bou-Saada 1994, Soebarto 1996, Manke and Hittle.

1996, Haberl et al.1998a, Haberl and Bou-Saada1998, Abushakra et al. 2001, Sylvester et al. 2002, and Song 2006).

Of special interest are the studies by Hsieh (1988) who calibrated a DOE-2 model to two instrumented commercial buildings to track performance; Subbarao et al.

(1990) who studied the problem of matching simulated data to measured data in

buildings; Kaplan et al. (1990, 1992) who developed a general procedure for calibrated simulation; Bronson et al. (1992) who developed a procedure for calibrating DOE-2 to non-weather-dependent loads; Bou-Saada (1994) who showed an improved procedure for developing a calibrated hourly simulation model to weather-dependent loads;

Soebarto (1996) who presented a calibration methodology using only two to four weeks of hourly monitored and monthly utility bills; Manke and Hittle (1996) who proposed

short term building monitoring and calibration; Haberl et al. (1998a) who used calibrated simulation to analyze energy conservation measures in two identical Habitat for

Humanity houses; Haberl and Bou-Saada (1998) who reviewed the previous literature about calibration techniques and presented several new calibration methods; and Abushakra et al. (2001) who presented a method to derive diversity factors and typical hourly load shapes of the lighting and receptacle loads in office buildings. In the study by Abushakra et al., the authors used a percentile analysis (i.e., the 50th percentile was used in their study) to derive the typical hourly load shapes.

In addition, Sylvester et al. (2002) presented a method for verifying the energy savings of a newly constructed commercial building using a baseline simulation model calibrated to the measured whole-building energy consumption; and Song (2006) who developed and demonstrated several new calibration methodologies for evaluating the energy performance of new commercial buildings. Of these, the following studies are the most relevant for the dissertation study.

Hsieh (1988) calibrated the DOE-2 model to two commercial buildings in New Jersey to track performance. This study was one of the first studies to show a general procedure for calibrating simulation. The results of Hsieh’s study showed that

calibration at the hourly level to measured data provided the best alignment between the simulation and the measured data. The results also showed that a potential 18-20%

difference in envelope heat loss could exist between the measured data and the design stage predictions, which showed the significance of calibration after design stage

simulation. Hsieh’s research provided this dissertation study with several procedures used for the calibration.

Kaplan et al. (1990) also calibrated a DOE-2 model to monitored data from a small office building. Their study was also one of the first studies to publish a general procedure for calibrated simulation. In this study, monitored data were used both to generate DOE-2 inputs and to verify DOE-2 outputs. Then, a series of iterations were made until the modeled output was within a certain tolerance band with the monitored data. The result showed that nine major changes were required to tune the DOE-2 model of the case-study building within the tolerance band. Although the target of the Kaplan et al. study was a small office building, the general calibration procedure is also helpful for this dissertation study.

Haberl and Bou-Saada (1998) reviewed the previous literature about calibration techniques and presented several new calibration methods including graphical

procedures and statistical goodness-of-fit parameters for quantitatively comparing simulated data to measured data. Haberl and Bou-Saada’s calibration methods were applied to a case-study building that was a four zone, single-story electrically heated and cooled building. The results showed that the new calibration procedures were able to produce an hourly mean bias error (NMBE) of -0.7% and an hourly coefficient of variation of the root mean squared error (CV(RMSE)) of 23.1 %, which is acceptable compared with the most accurate hourly neural network models (Kreider and Haberl, 1994; and Haberl et al. 1998b). Haberl and Bou-Sadda’s research is useful for this dissertation study since it provides detailed calibration procedures including the required

information for calibrating DOE-2, the graphical methods for improving a calibration, and statistical indices to gauge the goodness-of-fit of the calibration.

Song (2006) used several methodologies for evaluating the energy performance of new commercial buildings including several new calibration methods. Song’s study also provided the detailed calibration procedures that could be useful to this dissertation.

The detailed calibration procedures included: the importance of measured consistent solar radiation data, building thermal mass effects, and a new percentile analysis added to the previous signature method by Wei et al. (1998). The procedures by Song are also useful to the current study. In summary, the several previous studies about calibrated simulations provided useful procedures for calibrating a simulation that will be used in the current study.