Overview

In this module, you have the option to make changes to your baseline model to better calibrate it against metered data from the building. This step is optional and up to the user’s discretion. Some models may be created to represent only a fraction of a full metered facility and calibration may not make sense in those cases. In other cases, taking all reasonable steps to adjust baseline assumptions may not result in a reasonably calibrated model. Calibration is an iterative process, in the sense that the user identifies areas in which there is a discrepancy between modeled energy consumption and metered energy consumption, makes a series of changes expected to move the model toward a more calibrated state, re-runs the model, reviews the results, and repeats the process as necessary until the model is calibrated.

Four metrics are provided to summarize to progress made towards calibration. The first two make use of baseline monthly utility billing data, and the second two make use of baseline interval metered data. Depending on the availability of data, any number of these calibration metrics may or may not be available. The four metrics are as follows:

1. Overall meter calibration: A metric comparing the total annual energy consumption in each utility from the model to the real building
2. Seasonal meter calibration: A metric comparing the total energy consumption in three 4-month seasons for each utility from the model to the real building:
   1. Winter (December through March)
   2. Summer (June through September)
   3. Shoulder ( April, May, October, November)
3. Load profile calibration: A metric comparing the average annual energy consumption hour-by-hour and day-by-day (by utility) between the model and the real building. This metric quantifies how well the daily patterns of consumption in each utility are captured properly by the model.
4. Temperature bin calibration: A metric comparing the average annual energy consumption by utility, binned by the occupancy status and the associated outdoor air temperature. This metric quantifies how well the model captures the response of each utility to changes in weather, both during occupied and unoccupied hours.

It is possible for most models to achieve over 90% calibration for metric 1 and 2, as the metric is based on a single annual total per utility. For metrics 3 and 4, the calibration process may be much harder and a lower progress indicator of around 70% should be considered sufficient for most models. As the calibration progresses, it may be hard to improve calibration progress in one metric without negatively affecting calibration progress in another metric. The user should decide for themselves when the model feels sufficiently calibrated and move on to evaluation of re-tuning measures.

In-Depth

After the baseline model has been created by entering all of the known inputs about the building and its HVAC system, it is likely that the energy consumption predicted by the model may be substantially different from the actual (metered) energy consumption in the building. While there is often a high level of confidence in certain inputs (usually those that can be intentionally collected during the equipment and controls audit), other modeling parameters may require a best guess or the use of a default value. These harder-to-quantify values are thus the best candidates for adjustment in order to calibrate the model. Calibration is more art than science. There may be several different ways to calibrate the model, and there is no guarantee that the choices the user makes to calibrate the model are accurate. Still, a calibrated model, by one means or another is more likely to produce more accurate estimates of energy savings from re-tuning than an uncalibrated model.

This section provides __ calibration variables that can be adjusted by toggling a slider to lower or raise their value. The selected calibration variables include the following metrics, and recommended ranges of reasonable values:

• Infiltration: 0.05 to 0.5 cfm/sf of exterior wall area at neutral building pressure
• Wall U-factor: 0.02 to 0.4 Btu/hr-ft2-F
• Window U-factor: 0.1 to 1 Btu/hr-ft2-F
• Inter-zone U-factor 0 to 5.0 Btu/hr-ft2-F
• Construction thermal mass 20 to 60 W-h/K-m2
• Chiller Rated COP: 2 to 8
• Hot Water Loop heat loss to unconditioned spaces: 0 to 2
• VAV box minimum airflow setpoint: 0 to 100%
• VAV box maximum heating airflow setpoint: 0 to 100%
• VAV box sizing factor: 0.5 to 2.0
• Night Setback heating setpoint: 50 to 75
• Night setback cooling setpoint: 70 to 100
• Lighting schedule minimum: 0 to 1 (Note that this parameter will identify the minimum (or base) level of the schedule, and adjust the minimum values in the schedule up or down to create this new base level)
• Equipment schedule minimum: 0 to 1
• Minimum Outdoor airflow fraction (variable cfm): 0 to 100%: Note that the use of this calibration lever will reset the outdoor airflow control type to “Outdoor airflow fraction (variable cfm)”
• Fan efficiency: 0.4 to 0.6: Note that this value is applied to all fans in the DOAS/AHU system
• Weekday HVAC Schedule Start Time: 0 to 12 hours
• Weekday HVAC End Time: 12 to 24 hours
• Zone Heating Sizing Factor: 0.5 to 2.0
• Zone Design Lighting and Equipment Levels: 0.2 to 2.0 W/m2 for lighting. No recommended range for equipment.

Table 1 and Table 2 map the various calibration variables to a set of common calibration problems that can be understood through the graphs and tables in this calibration module. The mapping indicates which calibration variables can be deployed to correct each calibration problem, the direction to adjust the calibration variable to have the desired impact and the expected magnitude of impact (small arrows are low impact, large arrows are high impact). In addition, the table indicates which other calibration problems will likely be impacted by adjusting that calibration variable. This can help prioritize which calibration variable to use. For example, if 5 calibration variables can be used to solve a given calibration problem, but 3 of them will cause simultaneous effects that drive another area out of calibration, that would leave 2 calibration variables that are appropriate to address the current calibration problem.

Table 1 is appropriate for calibrating buildings with non-electric space heating, while Table 2 is appropriate for calibrating buildings with electric space heating (typically all-electric buildings). Note that these calibration tables are appropriate for most commercial buildings with daytime occupancy, and may not be applicable (especially in regard to the load profile calibration) for buildings like apartments and hotels that have opposite occupancy patterns. The tables also reflect common impacts of the calibration variables, however there is considerable variation in these impacts based on the specific HVAC infrastructure, control and climate of the building.