Delivered heating fuels, primarily propane and fuel oil but sometimes other fuels like wood or pellets, operate differently than electricity or gas. While electricity and utility gas are generally always available and consumption is well-monitored and typically billed monthly, delivered fuels are not available in perpetuity and need to be refilled as-needed based on interim usage.
That availability also correlates with billing cycles. While electricity and utility gas usage are monitored and billed regularly, delivered fuels don't work like that. They are paid for in bulk and delivered as needed.
While Snugg Pro readily models delivered heating fuels, how to input them is fundamentally different from how regularly billed utilities get entered. How they are input significantly affects modeling and thus BPI 2400 calibration.
It's worth noting that for 2400, like electric or utility gas, 355 days history is still required.
This is an example of the correct way to enter propane into Snugg Pro:
These entries represent the actual amount delivered and the date on which it was delivered. The model will distribute the usage over the full time interval; in this example, the 337 gallons delivered on April 19 will have its usage distributed across the previous 88 days (weather-normalized).
The incorrect way to enter this usage follows:
Note while the amounts line up with the delivery dates, the intervening months have deliveries documented with zero usage. Snugg Pro will model and calibrate these inputs incorrectly. In this example, the model will assume that between September 20 and November 20, the dwelling used zero propane and used 345 gallons between November 20 and December 20; then it will assume zero propane usage between January 22 and March 20 and 337 gallons between March 20 and April 19. Thus, the dwelling will not be modeled correctly.
While this example uses propane, enter other fuels like fuel oil, wood, or pellets similarly.
To conclude, it is best practice to enter delivery dates and amount used based on documented dates and amounts, even if the delivery dates span multiple months. The model will more accurately represent the usage in the home, calibration will be more accurate, and energy savings from proposed measures will also be more accurate leading to a better occupant experience.