Bill summaries and detailed variance reports are available for each of your billed utility services. Energy cost data isn’t available for submeters, because they aren’t subject to utility tariffs. Variance reports require 13 months or more of billing history data.
Individual bills are waiting for you in the Energy costs tab, where you can learn the drivers of variance for each billing period.
Key headline metrics summary
This is what a utility bill should look like: all of the most important data right up top, and placed in context to the same billing period one year ago. The comparison period dates likely won’t line up day for day, but that’s to be expected. Snapmeter looks carefully in your bill history for the most comparable bill available.
–Total cost: This figure is copied over straight from your utility bill online and it includes all taxes and fees, unless your bill is a Direct Access without generation charges.
–Total use: Also straight from the utility bill online.
–Cost per kWh: Simply total cost divided by total use.
–Cost per day: Simply total cost divided by the number of days in the bill period (found below in the operational factors table).
Source of variance table
Here is Snapmeter’s most detailed decomposition of bill variance.
–Operational energy use: The daily operations and baseload components of your load curve, measured in and compared with kWh.
–Demand: The peak kW reading during the bill period, converted to dollars based on your tariff, and compared to the same peak demand charge dollar figure from the comparison period bill.
–Time of energy use: A lot of math buckets your load curve into the time-of-use categories from your rate tariff–such as on hours, off hours, partial peak, summer peak, etc.–and compares the kWh allocation across the differently priced buckets. Imagine two load curves totalling the same kWh use, but one load curve spends more time in a more expensive bucket of the day.
–Temperature response: Weather-driven use from the Variance and M&V report is composed of two pieces, and this is one of them.
Snapmeter calculates the temperature response curve for the billing period last year, and a second temperature response curve for the current billing period, and returns the difference between those two response curves as the change in temperature response.
If you upgrade the efficiency of HVAC hardware in the building, you will see an improvement in the temperature response curve. The weather-effect will still be there, but the amount of energy your building uses in response to the weather will be lower. Refinements to BMS scripts can have similar results. This math isolates the change in energy use due to conditions internal to the building.
–Weather conditions: This is the second piece of weather-driven use.
Weather conditions variance assumes that nothing has changed in the current billing period, versus the same billing period last year, about how the building responds to changes in the weather. Put another way, Snapmeter holds constant the building’s temperature response, and it does this by drawing an average temperature response curve for the building and by using that single temperature response curve in both the current billing period and the billing period from last year.
By using one average temperature response curve, Snapmeter can calculate the change in energy use associated only to the change in temperature.
Imagine a weekday average temperature reading in September 2016 of 88 degrees and, in September 2017, a weekday average temperature reading of 84 degrees. With a single temperature response curve, it’s possible to compare the resulting energy use figure at 88 degrees (apple_A) with another estimate of the energy use figure at 84 degrees (apple_B). This math isolates the change in use due to conditions external to your building.
–Rate changes: Once all other drivers of variance are quantified, the remaining portion of the variance is assigned to rate changes. Queue the second reference to Einstein’s theory of relativity, and keep an eye out! Suspiciously high rate variances can be signs of utility billing errors.
–Bill period length: More than just the difference between the number of days in one bill period from the comparison bill, this also groups types of days together. The number of weekdays, weekends, and holidays are counted, and the average kWh and dollar costs are calculated for each type of day, and then compared.
–Total: A straightforward calculation of the bottom line total dollar change.
Operational factors table
This summary table of operational factors adds another layer of detail to the difference between the utility bill selected and its comparison period.
–Peak demand: The highest level of kW demand set by your meter during the current bill and the comparison period bill.
–Heating degree days: One heating degree day means that the average temperature outside the building for that day was one degree below 65ºF. The colder it is outside, the more heating degree days add up: 60ºF for one calendar day equals 5 heating degree days. This number is particularly relevant for meters connected to human-occupied space, and is not used in any of the quantitative models or analytics.
–Cooling degree days: The opposite of a heating degree day. Imagine the average temperature for the day is 66ºF, totaling one CDD. This number is particularly relevant for meters connected to human-occupied space, and is not used in any of the quantitative models or analytics.
–Bill period: The raw, unadjusted difference in calendar days.