CalTRACK Compliance Checklist
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CalTRACK Compliance Checklist
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CalTRACK RequirementCalTRACKVerificationBillingDailyHourly
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Project data must include an intervention active date2.1.3.1.3eemeter
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The baseline must be 365 days immediately prior to the intervention start date3.1.3eemeter
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Unless fitting baseline models using hourly methods, the number of days of consumption and temperature data missing should not exceed 37 (10%).2.2.1.2eemeter
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For hourly methods, baseline consumption data must be available for over 90% of hours in the same calendar month as well as in each of the previous and following calendar months in the previous year.2.2.1.2eemeter
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Less than 50% of high-frequency data can be missing. Missing data must be imputed as average for the time period.2.2.2.1eemeter
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Daily temperature data has been checked for 50% coverage, if higher frequency temperature data is being used to calculate data temperature2.2.2.3 / 2.2.3.3eemeter
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Temperature data has been checked for coverage across billing periods so that high-frequency temperature data covers 90% of each averaged billing period2.2.3.2eemeter
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CDD balance point range has been limited to between 30 and 90 degrees3.2.1.1eemeter
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HDD balance point range has been limited to between 30 and 90 degrees3.2.1.2eemeter
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Cooling balance point must be greater than or equal to the heating balance point3.2.2.1eemeter
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Balance point search must check at least every 3 degrees (or fewer) within the range3.2.3eemeter
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Regression model must follow the form: UPDp,i=μ+βH,i∗HDDp+βC,i∗CDDp+εp,i ,3.3.2eemeter
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Daily Average Usage has been correctly specified3.3.3.1eemeter
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Cooling degree days have been correctly specified3.3.4.1.1eemeter
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Heating degree days have been correctly specified3.3.5.1.1eemeter
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Daily models are fit using ordinary least squares regression3.4.1eemeter
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Billing models are fit using weighted least squares regression3.4.2eemeter
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All combinations of candidate balance points are tried3.4.3.1eemeter
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Only include candidate models where each parameter estimate is not negative3.4.3.2eemeter
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Select the candidate model with the highest adjusted R-squared3.4.3.3eemeter
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If a day in the reporting period is missing a temperature value, the corresponding consumption value for the day should be masked3.5.1.1eemeter
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If a day in the reporting period is missing a consumption value, the corresponding counterfactual for that day should be masked3.5.2.1eemeter
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Avoided energy use should not be calculated when consumption data is missing3.5.4.1eemeter
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For daily and billing methods, avoided energy use should be calculated using the form: AEUp,i=np∗(μi+βH,i∗HDDp+βC,i∗CDDp−UPDp)3.6.1eemeter
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For hourly methods, divide a week into 168 hourly intervals starting on Monday3.8.2eemeter
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To determine occupancy status, fit a single HDD and CDD weighted least squares model to the baseline dataset using fixed balance points for heating (50 degrees) and cooling (65 degrees) using the following form: UPHpi=μi+βHiHDH50p+βCiCDH65p+ϵpi3.8.3eemeter
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To determine occupancy states of a building for hourly methods, group the predictions of the occupancy model into occupied and unoccupied modes by time of the week3.8.4eemeter
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To determine which of the temperature bins for the hourly methods to include in the regression design matrix, count the number of hours in each default bin and combine the bins with fewer than 20 hours3.9.1eemeter
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To develop an hourly methods design matrix, first sort temperature values into bins to create a temperature matrix3.9.1eemeter
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Compute an hourly methods design matrix comprising a dependent variable of total consumption per hour and independent variables of seven (or fewer) temperature features, 168 binary time-of-week dummy variables, and an occupancy binary variable.3.10.1eemeter
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For hourly methods, avoided energy use should be calculated using the form: AEUpi=∑αtTOWp+∑βT,nTcn,p+∑occupiedαtTOWp+∑occupiedβT,nTcn,p−UPHp.3.12.1eemeter
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A CVRMSE value should be used to define building-level model uncertainty4.3.2.1eemeter
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CVRMSE should be calculated using the following form: CV(RMSE)=∑Pp=1(Up−Up^)2P−c‾‾‾‾‾‾‾‾‾‾‾√U⎯⎯⎯⎯⎯4.3.2.2eemeter
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Selected weather station must include latitude and longitude coordinates2.1.2.1eeweather
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Weather station information must include IECC Climate Zone2.1.2.3eeweather
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Weather station information must include IECC Moisture Regime2.1.2.4eeweather
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Weather station information must include Building America Climate Zone2.1.2.5eeweather
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If the site is in the state of California, weather station information must include California Building Climate Zone Area2.1.2.6eeweather
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Weather station information must include observed dry-bulb temperature data2.1.2.7eeweather
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When using hourly temperature data, data may not be missing for more than six (6) consecutive hours. Missing temperature data may be linearly interpolated for up to 6 consecutive missing hours.2.2.4.1eeweather
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Weather data should be converted to hourly intervals using interpolation and downsampling2.3.4eeweather
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Site should be matched to nearest weather station within the climate zones that meets data sufficiency requirements2.4.1eeweather
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If no weather stations within the climate zone meet data sufficiency requirements, fallback to closest weather station that has complete data2.4.1.1eeweather
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Weather station matches further than 200 km from the site should be flagged2.4.2eeweather
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Consumption data frequency must be specified2.1.1.1Provider
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If data from multiple meters is combined, must be noted2.1.1.2Provider
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All consumption data must be converted to units of energy consumption2.1.1.3Provider
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Presence of net metering must be flagged2.1.1.5Provider
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Project data must include a project start date2.1.3.1.1Provider
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Project data must include an Intervention completion date2.1.3.1.2Provider
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Project data must include a baseline period end2.1.3.1.4Provider
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Building site data must include latitude and longitude coordinates of at least four decimal places2.1.4.1Provider
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If Building site data does not include requisite latitude and longitude coordinates, the latitude and longitude coordinates of the centroid of the ZIP Code Tabulation Area (ZCTA) may be used instead.2.1.4.1.1Provider
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Building site data must include time zone2.1.6Provider
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If data is marked as NULL, NaN, or similar, it is considered missing2.2.1.3Provider
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Values of zero (0) are considered missing for electricity data, but not gas data2.2.1.4Provider
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If periods are estimated, they should be combined with subsequent periods2.2.2.2Provider
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Temperature and meter data has been checked to ensure they were recorded with the same time zoneProvider
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When using billing data, estimated periods should be combined with the next period up to a 70 day limit. Estimated periods are counted as missing dta for the purpose of determining data sufficiency.2.2.3.1Provider
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Excessively long Billing Periods have been removed (>35 or >70 days)2.2.3.4Provider
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Excessively short Billing Periods have been removed (< 25 days)2.2.3.4Provider
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Excess consumption data should be trimmed prior to analysis2.2.5Provider
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Projects should be excluded from analysis if net metering status changes during baseline period2.2.6Provider
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Projects should be flagged if electric vehicle charging is installed during the baseline or reporting period2.2.7Provider
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If using billing data and the date provided is impossible (e.g., January 32nd), use the first of the month2.3.1.1Provider
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If using billing data and the month or year is impossible, flag the date and remove it from the dataset2.3.1.2Provider
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Where two time series overlap, combine into a single time series by dropping duplicate records, using the most complete version possible. If timestamps conflict, flag for review. If multiple meters present, may be aggregated.2.3.2.1Provider
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Ensure time zone and daylight savings consistency across meter and temperature data2.3.3Provider
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Presence of negative meter data should be flagged as possible unreported net metering2.3.5Provider
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Extreme values (more than 3 interquartile ranges larger than the median) should be flagged as outliers for manual review.2.3.6Provider
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CDDs are not used as a variable in the calculation for gas data.3.2.1.1Provider
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A baseline model must have at least 10 non-zero degree days and at least 20 degree days per year, unless using billing data3.2.2.2Provider
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To aggregate single project results from individual time periods, the following should be calculated: AEUtotal,P=∑p=1P(AEUp,i)4.1.1Provider
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To aggregate multiple project results from the same time periods, the following should be calculated: AEUp,S=∑i=1S(AEUp,i)4.2.1Provider
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A Fractional Savings Uncertainty value should be used to define portfolio-level uncertainty4.3.2.3Provider
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Fractional Savings Uncertainty should be calculated using the following form: FSUi=ΔUsave,QiUsave,Qi=t(aM2+bM+d)CV(RMSE)∗PP′(1+2P′)1Q‾‾‾‾‾‾‾‾‾‾‾‾√)F4.3.2.4Provider
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Portfolio Fractional Savings Uncertainty should be calculated using the following form: FSUportfolio=∑Ni=1(ΔUsave,Qi)2‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾√∑Ni=1Usave,Qi4.3.2.5Provider
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Site level Bias should be calculated using the following form: MBi=1P∑p=1P(yp−ypˆ)4.3.2.6.1Provider
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Portfolio level Bias should be calculated using the following form: FBEportfolio=∑Ni=1(MBi)2∑Ni=1Usave,Qi4.3.2.6.1Provider
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