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Article Excerpt
BACKGROUND
Model calibration is essential to ensure that the architectural, mechanical, and electrical systems are properly modeled and integrated together for the purpose of estimating the building energy performance. Calibration of the energy model of a large building can be labor intensive even for an experienced modeler. It requires a thorough understanding of the architectural planning and mechanical systems, as well as of the assumptions, default values, mathematical models, and limitations of the energy analysis program.
During the calibration, the inputs for some variables with uncertain values are modified until the difference between the program prediction of energy use and measured data is acceptable. Utility bills are generally used for comparison with the predicted whole-building monthly and annual energy consumption since they contain, in a condensed format, the energy history of the building. This is also because they are available to the energy auditor at the beginning of the project, while monitored data require time and resources. Data monitored at the system or equipment level at smaller time intervals could give, however, more detailed and useful information than the whole-building energy consumption. For instance, Kaplan et al. (1990) calibrated the computer model of a small office building by using monitored energy end use for three months: a peak heating month, a peak cooling month, and a month with swing operation. The graphical representation of metered indoor air temperature was an additional help in understanding the room temperature setpoint schedule of operation and revealed differences from the design thermostat settings.
Kaplan and Canner (1992) have made recommendations for the maximum allowable difference between predicted and monitored data. For instance, the prediction of energy use for interior loads such as lighting, receptacles, or domestic hot water is satisfactory when the difference is within 5% on a monthly basis and 15% on a daily basis. However, the acceptable difference may increase up to 15%-25% (monthly) and 25%-35% (daily) for the simulation of HVAC systems. The annual simulated energy use should be within 10% of collected information, while a difference of less than 25% is acceptable on a seasonal basis.
Reddy (2006) presented a literature review of publications on calibration practices. The review showed that most studies were based on manual, iterative, and user-specific approaches. Several graphical comparative displays are also used to identify the calibration parameters. Other methods include short-term tests, measurements on test cells, energy signature, statistical screening techniques, and analytical models.
Carroll and Hitchcock (1993) proposed a method that automates the tuning of building parameters for matching the predicted energy use to utility data. Sun and Reddy (2006) presented a mathematical foundation to the calibration problem, with the goal of avoiding the "fudging process" of manipulating a large number of variables on a trial-and-error basis. Reddy et al. (2007a) reviewed literature on calibration from other scientific disciplines, such as environmental and structural engineering, and proposed a general methodology for the calibration of energy analysis programs against its utility bills. Reddy et al. (2007b) applied the new proposed methodology to the calibration of computer models of three office buildings, which were developed using the DOE-2 program. The calibration was performed using (1) synthetic data from two reference buildings, and (2) electricity use data from utility bills over 12 months from the third building.
The uncalibrated building energy models are still, however, largely used to estimate the energy savings from proposed energy conservation measures. This practice may lead to differences of 7%-73% when the annual predictions are compared with the measured whole-building electricity use, 25%-87% for chilled-water consumption, and 65%- 98% for hot-water use (Ahmad and Culp 2006). Other researchers concluded that a finely calibrated model does not ensure the accurate estimation of energy savings due to the building retrofit (Kaplan et al. 1990; Corson 1990). Nevertheless, the calibration of a computer model gives more confidence on the quality of work and the accuracy of predictions of the base case model.
A limited amount of information has been published thus far about the calibration of energy models for large buildings developed using the EnergyPlus program. Bellemare et al. (2002) modeled an institutional building with 54 interior zones and related variable-air-volume ( VAV ) systems. They presented the comparison between the predicted and monitored indoor air temperature and supply airflow rate of one classroom and found a similar pattern of variation. The weekly average difference between the room air temperature and airflow rate for the same classroom, as predicted by the EnergyPlus and DOE-2 programs, was 0.24[degrees]C and 9%, respectively. The daily average supply airflow rate of an air-handling unit was predicted within 8% of measured values. Ellis and Torcellini (2005) have simulated a tall building having an overall floor area of 240,000 [m.sup.2] (2,283,340 [ft.sup.2]). Their analysis was mainly focused on height-dependent properties and the use of floor multipliers, while HVAC systems were entered through the purchased air option that is offered by the EnergyPlus program. This approach reduces the computing time since it calculates the cooling and heating loads without taking into account the performance of HVAC equipment. Witte et al. (2001) used BESTEST guidelines to evaluate EnergyPlus for a base case building with mechanical systems. HVAC BEST-EST has been developed by the International Energy Agency and consists of a series of steady-state tests used to evaluate the ability of whole-building simulation programs (Hayer et al. 2001). The tests consist of analytical verifications of a specified mechanical system applied to a simplified near-adiabatic building envelope (Neymark et al. 2001). Results for eight different commonly used simulation programs are also included as a comparison tool for new software (Witte et al. 2001). The test helped to identify errors and documentation deficiencies. Most problems encountered were related to the system cycling mode and humidity. All issues encountered were investigated and fixed in later versions of the program.
OBJECTIVES
In this paper, the calibration of the case study is carried out separately over two periods with different operating conditions: period A, from May 4 to June 20, 2006, when the mechanical cooling system is in operation, and period B, from March 20 to May 3, 2006, which corresponds to the shoulder portions of the spring season, when the cooling coils are not in operation. The simulation of two distinct operation periods has the advantage of avoiding some compensating errors that can occur when the calibration process is performed over one year, with periods of different operating conditions.
The goal of the calibration presented in this paper is the development of a model of a large institutional building that predicts well the supply airflow rate and the supply and return air temperatures, which...
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