Building Energy Simulation Calibration 36

Calibrated models are frequently used to support selection of investment-grade ECMs as well as to identify contractual baseline (Reddy A. T., 2005). A calibrated building simulation model should be able to closely represent the actual behavior of the building under investigation. The fine-tuning of a simulation model to an existing situation involves using as-built information, observations, and monitored data to

iteratively adjust the parameters. This fitting is called “calibration.” Early identification of ECMs involved the use of utility bill analysis, which involved no additional cost of metering. However, large commercial building systems were found to be too profound

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to rely on monthly utility bill analysis. This lead to development of more data-driven models such as specialized inverse models.

In fact, Ahmad and Culp (Ahmad, 2003, Ahmad & Culp, 2006) found that uncalibrated simulation does not reflect the real operations of a building. The comparison between a model mainly based on available design data and a model

including as-built and operating information obtained from the maintenance personal for two different weather conditions revealed that there was too much variability between individual buildings. Discrepancies of +/- 30 percent were observed when comparing recorded and simulated total energy uses for four individual case-study buildings. This study emphasized that a good practice is to obtain information on operational data and occupancy when calibrating building simulation models. Furthermore, defining envelope details and the exact layout of the buildings was found to be important to minimize the errors that can occur from the default parameters built into the simulation program.

Previous studies have developed techniques and general process to calibrate building energy models with measured data. Bronson et al. (1992) developed graphical tools that plotted simulation output and measured energy consumption as a function of day and time. The plots aided in visualization of the comparison between the simulated values and the measured data to support the calibration process. In this study, it was found that schedules for occupancy and HVAC equipment that reflect the real operation of the building was important for calibrating the building energy simulation models.

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Soebarto and Degelman (2008) developed an improved calibration method using short-term monitoring and disaggregated energy use data. Systematic data collection included obtaining building’s physical data, HVAC data, operation data, weather data, and monthly utility records. In particular, building operation data was observed more in detail and included observing and collecting data in the field by visiting the site during the day-time and night-time period. Space temperature was obtained by measurements with portable temperature loggers. An operating schedule was derived using the “on-off tests” and “short-term monitoring” techniques. The on-off tests included segregating and obtaining different type of electrical load in the building (i.e., lighting, receptacles, fan motors) by turning them off and on consecutively and recording the reduction in load by a data logger that is connected to the electrical panels. The results were used to derive 24-hour use profiles for the whole building electric, lighting, receptacles, and fan motors. The on-off tests and the short term monitoring showed to be an effective method to calibrate the model when long-term monitoring was not practical.

Norford et al. (1994) calibrated office buildings by first addressing the

occupancy energy consumption and HVAC schedule. The subsequent phase included addressing HVAC equipment and the building shell performance. Parameters that had a major impact on the as-designed to the as-calibrated model included the variability in the lights and equipment use, HVAC operation beyond the normal scheduled hours, and the actual thermostat setting. The recommended process to calibrate the building energy model included a process to measure occupant loads, part-load performance of major HVAC equipment, indoor space temperature, and outside weather data.

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Pedrini et al. (2002) proposed a three step process to calibrate building energy models. The first step was to gather information about the building without any prior site visits. Architecture drawings and existing documentation was used to build the model. The second step was to conduct a walk-through audit with direct measurements using portable hand held instruments to check lighting levels, space temperature, and power in circuits for equipment and lighting. Information obtained from this phase was used to calibrate the lighting power density, equipment power density, cooling set point, and schedules. Finally, the third step was to split the aggregated energy use into end-use by lights, equipment, and air-conditioning circuits. This method was applied to six different buildings. The study showed that for the commercial building, occupant schedule and building operation had the greatest effect on the actual energy

consumption. Evidently, measured energy consumption by end-use was shown to have a great impact on sufficiently calibrating the building energy simulation model.

In summary, calibration methods were largely categorized as being manual and iterative, based on informative graphical comparisons, based on specialized tests, and or based on analytical and mathematical methods (Agami Reddy 2006). The calibration process was a combination of approaches.

The manual and iterative process showed that a good practice was to identify the building parameters with information that were readily available by obtaining existing documentation first (i.e., drawings, specifications). Then conduct an on-site audit and perform short term measurements of existing building systems and temperatures to develop occupancy and equipment profiles to tune the model. Finally, measured data

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along with actual weather data was used to investigate and match the simulation to measured end-use consumption (Kaplan 1992).

Informative and graphical comparison method by Haberl and Abbas (1998) included special tool-kit that showed 3D surface plots of energy use and juxtaposition binned box and whisker and mean plots. Simpler plots included plotting monthly and hourly time series using spreadsheets (Waltz 2000).

More advanced techniques included the adoption of special tests such as intrusive blink tests (Soebarto and Degelman 2008) and use of mathematical algorithms

developed by Sun and Reddy (2006) that screened the most influential parameters, designating a range of realistic values for the sensitive parameters leading to numerical optimization of calibration.